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# Computer Engineering Commons™

Articles 1 - 30 of 352

## Full-Text Articles in Computer Engineering

Dec 2016

##### Journal of Digital Forensics, Security and Law

No abstract provided.

Bloom Filters Optimized Wu-Manber For Intrusion Detection, Monther Aldwairi, Koloud Al-Khamaiseh, Fatima Alharbi, Babar Shah Dec 2016

#### Bloom Filters Optimized Wu-Manber For Intrusion Detection, Monther Aldwairi, Koloud Al-Khamaiseh, Fatima Alharbi, Babar Shah

##### Journal of Digital Forensics, Security and Law

With increasing number and severity of attacks, monitoring ingress and egress network traffic is becoming essential everyday task. Intrusion detection systems are the main tools for capturing and searching network traffic for potential harm. Signature-based intrusion detection systems are the most widely used, and they simply use a pattern matching algorithms to locate attack signatures in intercepted network traffic. Pattern matching algorithms are very expensive in terms of running time and memory usage, leaving intrusion detection systems unable to detect attacks in real-time. We propose a Bloom filters optimized Wu-Manber pattern matching algorithm to speed up intrusion detection. The Bloom ...

The 2016 Analysis Of Information Remaining On Computer Hard Disks Offered For Sale On The Second Hand Market In The Uae, Thomas Martin, Andy Jones, Mohammed Alzaabi Dec 2016

#### The 2016 Analysis Of Information Remaining On Computer Hard Disks Offered For Sale On The Second Hand Market In The Uae, Thomas Martin, Andy Jones, Mohammed Alzaabi

##### Journal of Digital Forensics, Security and Law

This research describes our survey of data remaining on computer hard disks sold on the second hand market in the United Arab Emirates (UAE). This is a repetition of the first survey conducted in 2012 (Jones, Martin, & Alzaabi, 2012). Similar studies have been carried over the last ten years in the United Kingdom, Australia, USA, Germany and France: (Jones, Mee, Meyler, & Gooch, 2005), (Jones, Valli, Sutherland, & Thomas, 2006), (Jones, Valli, Dardick, & Sutherland, 2008), (Jones, Valli, Dardick, & Sutherland, 2009). This research was undertaken to gain insight into the volumes of data found on second-hand disks purchased in the UAE, as well ...

Leveraging The Windows Amcache.Hve File In Forensic Investigations, Bhupendra Singh, Upasna Singh Dec 2016

#### Leveraging The Windows Amcache.Hve File In Forensic Investigations, Bhupendra Singh, Upasna Singh

##### Journal of Digital Forensics, Security and Law

The Amcache.hve is a registry hive file that is created by Microsoft® Windows® to store the information related to execution of programs. This paper highlights the evidential potential of Amcache.hve file and its application in the area of user activity analysis. The study uncovers numerous artifacts retained in Amcache.hve file when a user performs certain actions such as running host-based applications, installation of new applications, or running portable applications from external devices. The results of experiments demonstrate that Amcache.hve file stores intriguing artifacts related to applications such as timestamps of creation and last modification of any ...

A Survey Of Social Network Forensics, Umit Karabiyik, Muhammed Abdullah Canbaz, Ahmet Aksoy, Tayfun Tuna, Esra Akbas, Bilal Gonen, Ramazan S. Aygun Dec 2016

#### A Survey Of Social Network Forensics, Umit Karabiyik, Muhammed Abdullah Canbaz, Ahmet Aksoy, Tayfun Tuna, Esra Akbas, Bilal Gonen, Ramazan S. Aygun

##### Journal of Digital Forensics, Security and Law

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent ...

The Impact Of Md5 File Hash Collisions On Digital Forensic Imaging, Gary C. Kessler Dec 2016

#### The Impact Of Md5 File Hash Collisions On Digital Forensic Imaging, Gary C. Kessler

##### Journal of Digital Forensics, Security and Law

The Message Digest 5 (MD5) hash is commonly used as for integrity verification in the forensic imaging process. The ability to force MD5 hash collisions has been a reality for more than a decade, although there is a general consensus that hash collisions are of minimal impact to the practice of computer forensics. This paper describes an experiment to determine the results of imaging two disks that are identical except for one file, the two versions of which have different content but otherwise occupy the same byte positions on the disk, are the same size, and have the same hash ...

Dec 2016

#### The Impact Of Sha-1 File Hash Collisions On Digital Forensic Imaging: A Follow-Up Experiment, Gary C. Kessler

##### Journal of Digital Forensics, Security and Law

A previous paper described an experiment showing that Message Digest 5 (MD5) hash collisions of files have no impact on integrity verification in the forensic imaging process. This paper describes a similar experiment applied when two files have a Secure Hash Algorithm (SHA-1) collision.

A New Distributed Chinese Wall Security Policy Model, Saad Fehis, Omar Nouali, Mohand-Tahar Kechadi Dec 2016

#### A New Distributed Chinese Wall Security Policy Model, Saad Fehis, Omar Nouali, Mohand-Tahar Kechadi

##### Journal of Digital Forensics, Security and Law

The application of the Chinese wall security policy model (CWSPM) to control the information flows between two or more competing and/or conflicting companies in cloud computing (Multi-tenancy) or in the social network, is a very interesting solution. The main goal of the Chinese Wall Security Policy is to build a wall between the datasets of competing companies, and among the system subjects. This is done by the applying to the subjects mandatory rules, in order to control the information flow caused between them. This problem is one of the hottest topics in the area of cloud computing (as a ...

Computer Network Design For Universities In Developing Countries, Rafid Salih Sarhan Alsarhan Dec 2016

#### Computer Network Design For Universities In Developing Countries, Rafid Salih Sarhan Alsarhan

##### Information Technology Capstone Research Project Reports

The purpose of this project is to design a suitable network system for universities in developing countries. The aim was to design a network with high-quality security and low cost, in such a way that network devices of universities in developing countries, will meet standards associated with the universities in developed countries. This project will help to enhance education in developing countries. There are many devices that were used in designing the network, such as routers, switches, backup, firewall, and servers. All devices were connected to each other to make integration network system and configured by putting IP addresses to ...

Dec 2016

#### Analysis Of 3d Cone-Beam Ct Image Reconstruction Performance On A Fpga, Devin Held

##### Electronic Thesis and Dissertation Repository

Efficient and accurate tomographic image reconstruction has been an intensive topic of research due to the increasing everyday usage in areas such as radiology, biology, and materials science. Computed tomography (CT) scans are used to analyze internal structures through capture of x-ray images. Cone-beam CT scans project a cone-shaped x-ray to capture 2D image data from a single focal point, rotating around the object. CT scans are prone to multiple artifacts, including motion blur, streaks, and pixel irregularities, therefore must be run through image reconstruction software to reduce visual artifacts. The most common algorithm used is the Feldkamp, Davis, and ...

Distributed All-Ip Mobility Management Architecture Supported By The Ndn Overlay, Zhiwei Yan, Guanggang Geng, Sherali Zeadally, Yong-Jin Park Dec 2016

#### Distributed All-Ip Mobility Management Architecture Supported By The Ndn Overlay, Zhiwei Yan, Guanggang Geng, Sherali Zeadally, Yong-Jin Park

##### Information Science Faculty Publications

Two of the most promising candidate solutions for realizing the next-generation all-IP mobile networks are Mobile IPv6 (MIPv6), which is the host-based and global mobility supporting protocol, and Proxy MIPv6 (PMIPv6), which is the network-based and localized mobility supporting protocol. However, the unprecedented growth of mobile Internet traffic has resulted in the development of distributed mobility management (DMM) architecture by the Internet engineering task force DMM working group. The extension of the basic MIPv6 and PMIPv6 to support their distributed and scalable deployment in the future is one of the major goals of the DMM working group. We propose an ...

#### Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

##### Conference papers

Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set ...

A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert Morelos-Zaragoza Dec 2016

#### A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert Morelos-Zaragoza

##### Faculty Publications

This paper proposes a new multi-value sequence generated by utilizing primitive element, trace, and power residue symbol over odd characteristic finite field. In detail, let p and k be an odd prime number as the characteristic and a prime factor of p-1, respectively. Our proposal generates k-value sequence T={ti | ti=fk(Tr(ωi)+A)}, where ω is a primitive element in the extension field $\F{p}{m}$, Tr(⋅) is the trace function that maps $\F{p}{m} \rightarrow \f{p}$, A is a non-zero scalar in the prime field $\f{p}$, and fk(⋅) is a certain mapping function based ...

Dec 2016

#### What Broke Where For Distributed And Parallel Applications — A Whodunit Story, Subrata Mitra

##### Open Access Dissertations

Detection, diagnosis and mitigation of performance problems in today's large-scale distributed and parallel systems is a difficult task. These large distributed and parallel systems are composed of various complex software and hardware components. When the system experiences some performance or correctness problem, developers struggle to understand the root cause of the problem and fix in a timely manner. In my thesis, I address these three components of the performance problems in computer systems. First, we focus on diagnosing performance problems in large-scale parallel applications running on supercomputers. We developed techniques to localize the performance problem for root-cause analysis. Parallel ...

Video Annotation By Crowd Workers With Privacy-Preserving Local Disclosure, Apeksha Dipak Kumavat Dec 2016

#### Video Annotation By Crowd Workers With Privacy-Preserving Local Disclosure, Apeksha Dipak Kumavat

##### Open Access Theses

Advancements in computer vision are still not reliable enough for detecting video content including humans and their actions. Microtask crowdsourcing on task markets such as Amazon Mechnical Turk and Upwork can bring humans into the loop. However, engaging crowd workers to annotate non-public video footage risks revealing the identities of people in the video who may have a right to anonymity.

This thesis demonstrates how we can engage untrusted crowd workers to detect behaviors and objects, while robustly concealing the identities of all faces. We developed a web-based system that presents obfuscated videos to crowd workers, and provides them with ...

Android Drone: Remote Quadcopter Control With A Phone, Aubrey John Russell Dec 2016

#### Android Drone: Remote Quadcopter Control With A Phone, Aubrey John Russell

##### Computer Engineering

The purpose of the “Android Drone” project was to create a quadcopter that can be controlled by user input sent over the phone’s Wi-Fi connection or 4G internet connection. Furthermore, the purpose was also to be able to receive live video feedback over the internet connection, thus making the drone an inexpensive option compared to other, equivalent drones that might cost thousands of dollars. Not only that, but the Android phone also has a host of other useful features that could be utilized by the drone: this includes GPS, pathing, picture taking, data storage, networking and TCP/IP, a ...

Tangible Interaction As An Aid For Object Navigation In 3d Modeling, Sanmathi Dangeti Dec 2016

#### Tangible Interaction As An Aid For Object Navigation In 3d Modeling, Sanmathi Dangeti

##### Open Access Theses

This study introduced an interaction technique that used tangible interaction for 3D modeling. A hybrid interaction technique using a Kinect camera and a smartphone with a gyroscope was developed for the navigating objects in a 3D modeling software. It was then tested on 20 participants categorized as amateurs who had basic 3D/ CAD modeling experience and 20 participants categorized as the experts who had extensive experience working with the modeling software. This research study presents the need for existence of such interaction technique, gaps from the related previous studies, statistical findings from the current study and possible reasons for the ...

Context-Sensitive Auto-Sanitization For Php, Jared M. Smith, Richard J. Connor, David P. Cunningham, Kyle G. Bashour, Walter T. Work Dec 2016

#### Context-Sensitive Auto-Sanitization For Php, Jared M. Smith, Richard J. Connor, David P. Cunningham, Kyle G. Bashour, Walter T. Work

##### Chancellor’s Honors Program Projects

No abstract provided.

Multipath And Rate Stability, Junjie Liu, Roch A. Guérin Dec 2016

#### Multipath And Rate Stability, Junjie Liu, Roch A. Guérin

##### All Computer Science and Engineering Research

Originally Published In Proc. IEEE Globecom Conference - CQRM: Communication QoS, Reliability & Modeling Symposium

#### Just In Time Assembly (Jita) - A Run Time Interpretation Approach For Achieving Productivity Of Creating Custom Accelerators In Fpgas, Sen Ma

##### Theses and Dissertations

The reconfigurable computing community has yet to be successful in allowing programmers to access FPGAs through traditional software development flows. Existing barriers that prevent programmers from using FPGAs include: 1) knowledge of hardware programming models, 2) the need to work within the vendor specific CAD tools and hardware synthesis. This thesis presents a series of published papers that explore different aspects of a new approach being developed to remove the barriers and enable programmers to compile accelerators on next generation reconfigurable manycore architectures. The approach is entitled Just In Time Assembly (JITA) of hardware accelerators. The approach has been defined ...

On Path Consistency For Binary Constraint Satisfaction Problems, Christopher G. Reeson Dec 2016

#### On Path Consistency For Binary Constraint Satisfaction Problems, Christopher G. Reeson

##### Computer Science and Engineering: Theses, Dissertations, and Student Research

Constraint satisfaction problems (CSPs) provide a flexible and powerful framework for modeling and solving many decision problems of practical importance. Consistency properties and the algorithms for enforcing them on a problem instance are at the heart of Constraint Processing and best distinguish this area from other areas concerned with the same combinatorial problems. In this thesis, we study path consistency (PC) and investigate several algorithms for enforcing it on binary finite CSPs. We also study algorithms for enforcing consistency properties that are related to PC but are stronger or weaker than PC.

We identify and correct errors in the literature ...

#### Semeo: A Semantic Equivalence Analysis Framework For Obfuscated Android Applications, Zhen Hu

##### Computer Science and Engineering: Theses, Dissertations, and Student Research

Software repackaging is a common approach for creating malware. In this approach, malware authors inject malicious payloads into legitimate applications; then, to ren- der security analysis more difficult, they obfuscate most or all of the code. This forces analysts to spend a large amount of effort filtering out benign obfuscated methods in order to locate potentially malicious methods for further analysis. If an effective mechanism for filtering out benign obfuscated methods were available, the number of methods that must be analyzed could be reduced, allowing analysts to be more productive. In this thesis, we introduce SEMEO, a highly effective and ...

#### Towards Building A Review Recommendation System That Trains Novices By Leveraging The Actions Of Experts, Shilpa Khanal

##### Computer Science and Engineering: Theses, Dissertations, and Student Research

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to ...

From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan Dec 2016

#### From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan

##### Research Collection School Of Information Systems

With the booming popularity of online social networks like Twitter and Weibo, online user footprints are accumulating rapidly on the social web. Simultaneously, the question of how to leverage the large-scale user-generated social media data for personal credit scoring comes into the sight of both researchers and practitioners. It has also become a topic of great importance and growing interest in the P2P lending industry. However, compared with traditional financial data, heterogeneous social data presents both opportunities and challenges for personal credit scoring. In this article, we seek a deep understanding of how to learn users’ credit labels from social ...

Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang Dec 2016

#### Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang

##### Research Collection School Of Information Systems

Microblogging services allow users to create hashtags to categorize their posts. In recent years,the task of recommending hashtags for microblogs has been given increasing attention. However,most of existing methods depend on hand-crafted features. Motivated by the successful use oflong short-term memory (LSTM) for many natural language processing tasks, in this paper, weadopt LSTM to learn the representation of a microblog post. Observing that hashtags indicatethe primary topics of microblog posts, we propose a novel attention-based LSTM model whichincorporates topic modeling into the LSTM architecture through an attention mechanism. Weevaluate our model using a large real-world dataset. Experimental results ...

#### A Reduced Labeled Samples (Rls) Framework For Classification Of Imbalanced Concept-Drifting Streaming Data., Elaheh Arabmakki

##### Electronic Theses and Dissertations

Stream processing frameworks are designed to process the streaming data that arrives in time. An example of such data is stream of emails that a user receives every day. Most of the real world data streams are also imbalanced as is in the stream of emails, which contains few spam emails compared to a lot of legitimate emails. The classification of the imbalanced data stream is challenging due to the several reasons: First of all, data streams are huge and they can not be stored in the memory for one time processing. Second, if the data is imbalanced, the accuracy ...

Dec 2016

#### Vulnerability Analysis And Security Framework For Zigbee Communication In Iot, Charbel Azzi

##### UNLV Theses, Dissertations, Professional Papers, and Capstones

Securing IoT (Internet of Things) systems in general, regardless of the communication technology used, has been the concern of many researchers and private companies. As for ZigBee security concerns, much research and many experiments have been conducted to better predict the nature of potential security threats. In this research we are addressing several ZigBee vulnerabilities by performing first hand experiments and attack simulations on ZigBee protocol. This will allow us to better understand the security issues surveyed and find ways to mitigate them. Based on the attack simulations performed and the survey conducted, we have developed a ZigBee IoT framework ...

A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert H. Morelos-Zaragoza Nov 2016

#### A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert H. Morelos-Zaragoza

##### Robert Henry Morelos-Zaragoza

This paper proposes a new multi-value sequence generated by utilizing primitive element, trace, and power residue symbol over odd characteristic finite field. In detail, let p and k be an odd prime number as the characteristic and a prime factor of p-1, respectively. Our proposal generates k-value sequence T={ti | ti=fk(Tr(ωi)+A)}, where ω is a primitive element in the extension field $\F{p}{m}$, Tr(⋅) is the trace function that maps $\F{p}{m} \rightarrow \f{p}$, A is a non-zero scalar in the prime field $\f{p}$, and fk(⋅) is a certain ...

Nov 2016

#### Who's In And Who's Out?: What's Important In The Cyber World?, Tony M. Kelly

##### HON499 projects

The aim of this paper is to offer an introduction to the exploding field of cybersecurity by asking what are the most important concepts or topics that a new member of the field of cybersecurity should know. This paper explores this question from three perspectives: from the realm of business and how the cyber world is intertwined with modern commerce, including common weaknesses and recommendations, from the academic arena examining how cybersecurity is taught and how it should be taught in a classroom or laboratory environment, and lastly, from the author’s personal experience with the cyber world. Included information ...

A Deep And Longitudinal Approach To Mining Mobile Applications, Khalid Ahmed Alharbi Nov 2016

#### A Deep And Longitudinal Approach To Mining Mobile Applications, Khalid Ahmed Alharbi

##### Computer Science Graduate Theses & Dissertations

Modern software platforms feature digital distribution channels called marketplaces, which have revolutionized the way applications are developed and delivered to users. As the number of applications continues to proliferate in marketplaces, the need to fully understand them is ever increasing. While researchers have recently started to observe the wealth of information in marketplaces, their efforts have been largely constrained to one view of analysis and a single snapshot in time. As a result, the increasing number of application updates published to marketplaces has largely gone unobserved. Such view misses the much larger opportunity of mining applications with both a deep ...


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# Physical semantics

I noticed that physicists use the word physical as an adjective to modify many different types of words. I was curious to understand how physical modifies the words it is applied to as an adjective. It appears that physical is like a physical operator: it maps a non-physical word into physics:

$P\rightarrow&space;W=PW$

Physical takes a plain word W and it transforms it into a property of physics. PW is a new word, not a modified W. For instance, adjectives small and large may change the scale of the word but physical changes its meaning and effectively creates a new word. Since physics is immune to semantic arguments from non-physicists I am not worried that this article will cause any kind of harm to physics. Just for my own understanding.

I noticed that the most important difference is with quantity and physical quantity.

Quantity and physical quantity

There is a fundamental difference between quantity and physical quantity. Physics assumes as an unstated truth that what is measured is physical:

measured=physical

This is not true. The pun that what is measured is necessarily physical is a professional propaganda.

Measurement is defined like this:

measured quantity = a unit – measured multiple of the unit

There is no other way to measure and there is nothing in measurement that says that what is measured is physical or not physical . Therefore what is measured is quantity not physical quantity.

Meaning and physical meaning

Physical meaning is

physics that is hidden behind a mathematical construction.

There is something called physics and this thing is hidden behind a mathematical construction. This sounds a lot like mysticism to me. I thought that physics was a mathematical science and not metamathematical mysticism. The prototypical hidden meaning hidden behind a mathematical construction gracing physics has been the occult, i.e., hidden Newtonian force. As I understand it, physical meaning is not related to measurement, it is an interpretation of the hidden.

Reasoning and physical reasoning

Reasoning without the “physical” is rationalism. As rational scientists we treat the mathematical framework as a mathematical framework and do not assume that there is hidden “physics” in mathematics. In mathematics we look for mathematical objects, like patterns. Physical reasoning on the other hand assumes that nature is physical therefore there must be hidden physics behind the mathematics and looks for them. And finds them. How? By labeling mathematical patterns with colorful labels and calling them physical fields, physical particles, physical forces and physical anything. Therefore, if physical semantics is practiced by physicists it is legal physics. The world is only semantically physical.

Model and physical model

In this case model is called physical because it is a model that explains physical nature. So nature is physical pun again.

Theory and physical theory

Physical theories have hidden physics in them. Where are they? Again you can find them in colorful labels. A proportionality constant may be called “Newton’s universal constant of gravity” and because it is called “Newton’s universal constant of gravity” a proportionality constant is assumed to be a physical constant of nature. All these suggest that what physicists mean by physics is “label mechanics.” Or more correctly “legal labels mechanics.”

Observable and physical observable

Physical observable is a physics term of art. What is measured is not an observable what is measured is distance.

Law and physical law

Law legislate society. Physical law legislates nature.

Commentary and physical commentary

Personally I see nothing wrong that physicists fit measurements into an elaborate legal system called mechanics, pardon me, physical mechanics, because this is how nature works. Standard is the thing. As Buckminster Fuller said

There are no solids. There are no things. There are only interfering and noninterfering patterns operative in pure principle . . .

I would say “in pure definition” because the world is definitional. The world is physical only by definition. It’s not surprising that physicists’ Standard Model explains observations well. It is a fit to observations in which various mathematical patterns are labeled by physicists with ideological labels. Standard Model is a definition. If physicists dropped their search for hidden physics and saved phenomena with mathematics then physics will look more like a science. Shut up and fit, as Max Tegmark would say. But this is impossible. In physics Newton’s words are sacred. Newton decreed in the Principia that his disciples shall search for hidden qualities in nature by studying motion. This is the physical program Newton ordered his disciples to continue to do until the second coming of the Kingdom of Science. Don’t hold your breath. A bureaucratic habit can never be changed. And looking for hidden physics in nature is a strong physical habit of a huge bureaucracy. It will never change.

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# object detection techniques

In this approach, we define the features and then train the classifier (such as … Fortunately, this was changed in the third iteration for a more standard feature pyramid network output structure. From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. An overview of object detection: one-stage methods. There are many common libraries or application program interface (APIs) to use. The nature of the techniques largely depends on the application. Object detection is the process of finding instances of objects in images. This algorithm … Redmond offers an approach towards discovering the best aspect ratios by doing k-means clustering (with a custom distance metric) on all of the bounding boxes in your training dataset. The goal of object detection is to recognize instances of a predefined set of object classes (e.g. This choice will depend on your dataset and whether or not your labels overlap (eg. There are a variety of techniques that can be used to perform object detection. The software is called Detectron that incorporates numerous research projects for object detection and is powered by the Caffe2 deep learning framework. defined by a point, width, and height), and a class label for each bounding box. However, we will not include bounding boxes which have a high IoU score (above some threshold) but not the highest score when calculating the loss. in 2015 and subsequently revised in two following papers. In the respective sections, I'll describe the nuances of each approach and fill in some of the details that I've glanced over in this section so that you can actually implement each model. Each feature vector is fed into a sequence of fully connected (fc) layers that finally branch into two sibling output layers: one that produces softmax probability estimates over K-object classes plus a catch-all background class and another layer that outputs four real-valued numbers for each of the K-object classes. It has a wide array of practical applications - face recognition, surveillance, tracking objects, and more. {1, 2, 3, 1/2, 1/3}) to use for the $B$ bounding boxes at each grid cell location. "golden retriever" and "dog"). Our story begins in 2001; the year an efficient algorithm for face detection was invented by Paul Viola and Michael Jones. , . Their demo that showed faces being detected in real time on a webcam feed was the most stunning demonstration of computer vision and its potential at the time. Our final script will cover how to perform object detection in real-time video with the Google Coral. For Machine Learning approaches, it becomes necessary to first define features using one of the methods below, then using a technique such as support vector machine (SVM) to do the classification. Thus, we directly predict the probability of each class using a softmax activation and cross entropy loss. Methods for object detection generally fall into either machine learning-based approaches or deep learning-based approaches. Our final script will cover how to perform object detection in real-time video with the Google Coral. Object detection is performed to check existence of objects in video and to precisely locate that object. Simplified scale-space extrema detection in SIFT algorithms accelerates feature extraction speed, so they are several times faster than SIFT algorithms. Ensemble methods for object detection In this repository, we provide the code for ensembling the output of object detection models, and applying test-time augmentation for object detection. Creating Convolutional Neural Networks from Scratch: Background Extraction from videos using Gaussian Mixture Models, Deep learning using synthetic data in computer vision. In order to fully describe a detected object, we'll need to define: Thus, we'll need to learn a convolution filter for each of the above attributes such that we produce $5 + C$ output channels to describe a single bounding box at each grid cell location. Object detection is performed to check existence of objects in video and to precisely locate that object. Then, for each object proposal a region of interest (RoI) pooling layer extracts a fixed-length feature vector from the feature map. Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. The "predictions on a grid" approach produces a fixed number of bounding box predictions for each image. Here's a survey of object detection techniques which although is targeted towards planetary applications, it discusses some interesting terrestrial methods. Faster R-CNN is an object detection algorithm proposed by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun in 2015. However, some images might have multiple objects which "belong" to the same grid cell. The live feed of a camera can be used to identify objects in the physical world. The first iteration of the YOLO model directly predicts all four values which describe a bounding box. We can also determine roughly where objects are located in the coarse (7x7) feature maps by observing which grid cell contains the center of our bounding box annotation. This library has been designed to be applicable to any object detection model independently of the underlying algorithm and the framework employed to implement it. Object Detection Challenges. One area that has attained great progress is object detection. The above are examples images and object annotations for the Grocery data set (left) and the Pascal VOC data set (right) used in this tutorial. To accomplish this, we'll use a technique known as non-max suppression. Object detection has proved to be a prominent module for numerous important applications like video surveillance, autonomous driving, face detection, etc. If I can classify an object by colour, I can track the object from video frame to video frame. The descriptor describes a distribution of Haar-wavelet responses within the interest point neighborhood. Object detection is useful for understanding what's in an image, describing both what is in an image and where those objects are found. Object detection is a key ability required by most computer and robot vision systems. Object detection is a computer vision technique for locating instances of objects in images or videos. SURF algorithms that rely on image descriptor are robust against different image transformations and disturbance in the images by occlusions. The first is an online-network based API, while the second is an offline-machine based API. As the paper points out, "with $\gamma=2$, an example classified with $p_t = 0.9$ would have 100X lower loss compared with CE and with $p_t = 0.968$ it would have 1000X lower loss.". The latest research on this area has been making great progress in many directions. Effective testing for machine learning systems. who conducted object class detection survey in the year 2013, Jiao Licheng et al. The mobile platform libraries are highly efficient enabling the users to deploy machine learning or object detection models on mobile platforms to make use of the computation power of the handheld devices. Introduction. We define the boxes width and height such that our model predicts the square-root width and height; by defining the width and height of the boxes as a square-root value, differences between large numbers are less significant than differences between small numbers (confirm this visually by looking at a plot of $y = \sqrt {x}$). You’ll love this tutorial on building your own vehicle detection system Over last few years, moving object detection has received much of attraction due to its wide range of applications like video surveillance, human motion analysis, robot navigation, event detection, anomaly detection, video conferencing, traffic analysis and security. As I mentioned previously, the class predictions for SSD bounding boxes are not conditioned on the fact that an object is present. Excited by the idea of smart cities? In Keypoint localization, among keypoint candidates, distinctive keypoints are selected by comparing each pixel in the detected feature to its neighbouring ones. An object detection model is trained to detect the presence and location of multiple classes of objects. Hence the performance of object detectors plays an important role in the functioning of such systems. Object detection in video with the Coral USB Accelerator Figure 4: Real-time object detection with Google’s Coral USB deep learning coprocessor, the perfect companion for the Raspberry Pi. Object detection techniques are described. In the example below, we have a 7x7x512 representation of our observation. The task of object detection is to identify "what" objects are inside of an image and "where" they are.Given an input image, the algorithm outputs a list of objects, each associated with a class label and location (usually in the form of bounding box coordinates). Object detection systems construct a model for an object class from a set of training examples. Although we can filter these bounding boxes out by their $p_{obj}$ score, this introduces quite a large imbalance between the predicted bounding boxes which contain an object and those which do not contain an object. Thanks to deep learning! In order to understand what's in an image, we'll feed our input through a standard convolutional network to build a rich feature representation of the original image. Every year, new algorithms/ models keep on outperforming the previous ones. This means that we'll learn a set of weights to look across all 512 feature maps and determine which grid cells are likely to contain an object, what classes are likely to be present in each grid cell, and how to describe the bounding box for possible objects in each grid cell. Input : An image with one or more objects, such as a photograph. For similar reasons as originally predicting the square-root width and height, we'll define our task to predict the log offsets from our bounding box prior. Today, ther e is a plethora of pre-trained models for object detection (YOLO, RCNN, Fast RCNN, Mask RCNN, Multibox etc. Object detection is the process of finding instances of objects in images. In a sliding window mechanism, we use a sliding window (similar to the one used in convolutional networks) and crop a part of the image in … Originally, class prediction was performed at the grid cell level. Despite reduced time for feature computation and matching, they have difficulty in providing real-time object recognition in resource-constrained embedded system environments. The extracted interest points lie on distinctive, high-contrast regions of the image. Many object detection techniques rely on the detection of local invariant features as a first step such as the surveys presented by Mikolajczyk et al. Object Detection & Tracking Using Color – in this example, the author explains how to use OpenCV to detect objects based on the differences of colors. The … The present works gives a perspective on object detection research. On the other hand, deep learning techniques are able to do end-to-end object detection without specifically defining features, and are typically based on convolutional neural networks All of these models were first pre-trained as image classifiers before being adapted for the detection task. We can alter our layer to produce $B(5 + C)$ filters such that we can predict $B$ bounding boxes for each grid cell location. McInerney and Terzopoulos presented a survey of deformable models commonly used in medical image analysis. YOLO makes less than half the number of background errors compared to Fast R-CNN. Object detection methods are vast and in rapid development. At a high level, this technique will look at highly overlapping bounding boxes and suppress (or discard) all of the predictions except the highest confidence prediction. However, we cannot sufficiently describe each object with a single activation. In YOLOv2, Redmond adds a weird skip connection splitting a higher resolution feature map across multiple channels as visualized below. an object classification co… The key method in the application is an object detection technique that uses deep learning neural networks to train on objects users simply click and identify using drawn polygons. Below I've listed some common datasets that researchers use when evaluating new object detection models. In simple words, the goal of this detection technique is to determine where objects are located in a given image called as object localisation and which category each object belongs to, that is called as object classification. Rather than using k-means clustering to discover aspect ratios, the SSD model manually defines a collection of aspect ratios (eg. A Fast R-CNN network takes an entire image as input and a set of object proposals. Object detection is a technology related to computer vision that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or vehicles) in digital videos and images. Faster R-CNN is an object detection algorithm that is similar to R-CNN. Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. To approximate the Laplacian of Gaussian, SURF uses a box filter representation. Object detection is one of the areas of computer vision that is maturing very rapidly. Note: Although it is not visualized, these anchor boxes are present for each cell in our prediction grid. Let’s move forward with our Object Detection Tutorial and understand it’s various applications in the industry. Object Detection is a common Computer Vision problem which deals with identifying and locating object of certain classes in the image. In the case of deep learning, object detection is a subset of object recognition, where the object is not only identified but also located in an image. In this post, I'll discuss an overview of deep learning techniques for object detection using convolutional neural networks. The SIFT method can robustly identify objects even among clutter and under partial occlusion because the SIFT feature descriptor is invariant to scale, orientation, and affine distortion. Object detection methods fall into two major categories, generative [1,2,3,4,5] The original YOLO network uses a modified GoogLeNet as the backbone network. 15 min read, The goal of this document is to provide a common framework for approaching machine learning projects that can be referenced by practitioners. In the third version, Redmond redefined the "objectness" target score $p_{obj}$ to be 1 for the bounding boxes with highest IoU score for each given target, and 0 for all remaining boxes. Holistic approaches using generative models rely on the ability to model the shape of the target object. Object detection algorithms are improving by the minute. However, we also end up predicting for a large number grid cells where no object is found. We'll use rectangles to describe the locations of each object, which may lead to imperfect localizations due to the shapes of objects. Thus, we need a method for removing redundant object predictions such that each object is described by a single bounding box. In each section, I'll discuss the specific implementation details and refinements that were made to improve performance. There are many common libraries or application pro-gram interface (APIs) to use. Sliding windows for object localization and image pyramids for detection at different scales are one of the most used ones. With the recent advancements in the 21st century, there has been a lot of innovation and creative methodologies which enable the users to use object detection in a modular structure in the domain of object detection. Although there have been many different types of methods throughout the years, we want to focus on the two most popular ones (which are still widely used).The first one is the Viola-Jones framework proposed in 2001 by Paul Viola and Michael Jones in the paper Robust Real-time Object Detection. Soon, it was implemented in OpenCV and face detection became synonymous with Viola and Jones algorithm.Every few years a new idea comes along that forces people to pause and take note. Faster R-CNN. There are algorithms proposed based on various computer vision and machine learning advances. Objects detected by Vector Object Detection using Deep Learning. The two models I'll discuss below both use this concept of "predictions on a grid" to detect a fixed number of possible objects within an image. Object detection algorithms typically use machine learning, deep learning, or computer vision techniques to locate and classify objects in images or video. However, we'll also match the ground truth boxes with any other anchor boxes with an IoU above some defined threshold (0.5) in the same light of not punishing good predictions simply because they weren't the best. Get all the latest & greatest posts delivered straight to your inbox. Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. In this feature, I continue to use colour to use as a method to classify an object. In order to detect this object, we will add another convolutional layer and learn the kernel parameters which combine the context of all 512 feature maps in order to produce an activation corresponding with the grid cell which contains our object. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. That is the power of object detection algorithms. The difference is that SURF algorithms simplify scale-space extrema detection by constructing the scale space via distribution changes instead of using Difference of Gaussian (DoG) filter. This is a multipart post on image recognition and object detection. The Harris corner detector used in the SIFT method has good performance but it is not effective for real-time object recognition due to its long computation time. Object detection builds on my last article where I apply a colour range to allow an area of interest to show through a mask. In each section, I'll discuss the specific implementation details for this model. Machine learning engineer. Adapting the classification network for detection simply consists of removing the last few layers of the network and adding a convolutional layer with $B(5 + C)$ filters to produce the $N \times N \times B$ bounding box predictions. The most two common techniques ones are Microsoft Azure Cloud object detection and Google Tensorflow object detection. Based on the normalized corner information, support vector machine and back-propagation neural network training are performed for the efficient recognition of objects. General object detection framework. Redmond later created a new model named DarkNet-19 which follows the general design of a $3 \times 3$ filters, doubling the number of channels at each pooling step; $1 \times 1$ filters are also used to periodically compress the feature representation throughout the network. Object Detection using Single Shot MultiBox Detector The problem. These region proposals are a large set of bounding boxes spanning the full image (that is, an object … This allows for predictions that can take advantage of finer-grained information from earlier in the network, which helps for detecting small objects in the image. Object detection and object recognition are similar techniques for identifying objects, but they vary in their execution. In general, there's two different approaches for this task – we can either make a fixed number of predictions on grid (one stage) or leverage a proposal network to find objects and then use a second network to fine-tune these proposals and output a final prediction … Object Detection using Deep Learning To detect objects, we will be using an object detection algorithm which is trained with Google Open Image dataset. Object detection is a particularly challenging task in computer vision. However, for the dense prediction task of image segmentation, it's not immediately clear what counts as a "true positive&, Stay up to date! In this project, we are using highly accurate object detection-algorithms and methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate ones like SSD and YOLO. Higher detection quality (mean Average Precision) than R-CNN, SPPnet (Spatial Pyramid Pooling), Training is single-stage, using a multi-task loss, No disk storage is required for feature caching. Safepro offer opticsense object detection edge video analytics enables the cameras in detecting and counting objects within its vicinity, recognition techniques simple objects like … SURF algorithms have detection techniques similar to SIFT algorithms. This leads to a simpler and faster model architecture, although it can sometimes struggle to be flexible enough to adapt to arbitrary tasks (such as mask prediction). Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. These region proposals are a large set of bounding boxes spanning the full image (that is, an object localisation component). An alternative approach would be image segmentation which provides localization at the pixel-level. This is especially difficult for models which don't separate prediction of objectness and class probability into two separate tasks, and instead simply include a "background" class for regions with no objects. The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion.The object detection and object classification are preceding steps for tracking an object in sequence of images. Circle of sixteen pixels around the corner candidate these region proposals some common datasets that researchers use when new. 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Predict object bounding boxes which has a wide array of practical applications - face recognition, surveillance autonomous! ’ ll focus on model architectures which directly predict object bounding boxes spanning the image...


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## December 13, 2012

Hey, so I don’t have a lot of time, but I wanted to write really quickly to let you know that I haven’t fallen off the wagon. This wasn’t one of those things that I do where I diet for a few weeks, don’t get results and quit.

After my last post, I got a really terrible cold that had me down for about a week and a half. I was still trying to eat right but I didn’t have a very big appetite and I wasn’t exercising at all. My weight stayed the same.

Then I started getting ready for my Disneyland trip and that was sucking up all of my time, but I was still eating right, only I didn’t have time to work out. My weight stayed the same.

Then I went to Disneyland. I was bummed because I wanted to be down to 181 before I left, but remember all that trouble I was having with the scale? Well, it didn’t change. The scale hadn’t budged. I determined that I must be doing something wrong and I would handle it when I got back from vacation.

After vacation I hopped on the scale to see how things were measuring up and FINALLY, I was back down to 190 again. Not where I was hoping to be at this point, but I was really happy to see that something had happened. I am wondering if perhaps the stress of planning a vacation for 5 people had something to do with the weight not coming off.

I ate pretty much like crap in Disneyland, not terrible, but pretty much like crap because we were eating out everywhere. I was able to make a few healthy choices, like egg white omelets, but most of the time I was chowing down burgers and pizza because they were cheap and easy to share. Thankfully we did a TON of walking and I think that is what helped keep the pounds off while we were eating out so much.

Now that we are back home I am still eating right, but I haven’t been able to work out yet. With all the Christmas shopping, birthday parties, Christmas parties, work functions, blah blah blah, I haven’t worked out a bit. My weight is still down to 190 and hasn’t gone up. I am watching it really closely since I am worried about gaining weight while I’m too busy to work out.

I’ll keep you updated.

## November 6, 2012

### Day 17 - Standing Still

I feel like I am standing still. I weighed 190 again today. Maybe I should be happy about that? I would have been happy about it a week ago, but today I still feel like I am in a hole. I can't believe I am stuck in this rut, and what is more amazing to me is that I am still working out and eating right.

It's totally like me to give up by now - especially without any results. Still I am going. I worked out 45 minutes last night even though I really didn't want to... and I mean REALLY didn't want to. I was going to give up after 15 minutes and use my "a little bit is better than a not-a-bit" excuse. That one is a real classic when it comes to working out... but I didn't stop. Then, I was going to quit after half an hour... the doctor only asks you to do 30 minutes a day... but I didn't stop. I knew I had to increase my intensity and the length of my workout.

How many people do you know who work out 45-60 minutes 5 times a week. I mean, faithfully truly do it? I don't know many - I had an old coworker who ran everyday. Guess what? She was a rail! Thin, petite, fit... and here I am working out 5 times a week, more if I can, and I can't lose anything! I'm not sure how I am staying motivated, but I hope it pays off.

## November 5, 2012

### Day 16 - Big drop not big enough

All of the research that I have been doing on my body hanging on to my weight throughout my new exercise program promises a big drop in weight sometime in either week 3 or week 4. Well, today I saw a huge drop on the scale. Considering that I weighed in at a hefty 195 yesterday, the 190 that the scale was boasting today was welcome... but I still feel slighted.

The lowest I have weighed so far was 189, and I still haven't seen that number yet. You would think that by now I should have dropped another 2 pounds past that, so now I am really behind. My starting weight was 192, so really, I have only lost 2 pounds in two weeks. I guess that is not so far under average, but I am wondering why it took so long, why I saw a big fast drop and then a steady gain, and why I don't weight less when the math says I should.

My goal was to weigh 180 by the end of November, but now it looks like that isn't going to work. The longer it takes me to shed pounds off this initial gain, the harder it is going to be to reach my goal. I was trying to lose about 2 pounds per week, but now I am seriously set back. I am trying not to get discouraged, but it is definitely hard. Especially since this is the first time I have tried to do things the "right" and "healthy" way. I have been working out so hard and really trying to eat right and honestly doing my very very best even though I want to give up... but I won't. I won't give up.

### 24 Days until Disneyland!

Let's see... how do I put this...

EEEEEEEEEEEEEEEEEEEEEEE!!!

We are leaving for Disneyland in less that a month! I thought that I had managed to save all of the money that I needed, but then I remembered that I have to pay for transit. Boo! I was trying to find the cheapest way to get from Disneyland to Universal Studios. I think if I take the metrolink train to the subway, and then take the shuttle from the subway to the main gate, then that would be the cheapest way... Although taking the greyline bus tour sounds pretty appealing too! Maybe I will do one on the first day and one on the second.

Oh well, doesn't matter - have Disneyland!

## November 4, 2012

### Day 15 - Why am I still doing this?

Seriously, the scale is still the same. Today I weighed in at 195 - 3 pounds more than when I started this two weeks ago. That's right, I have been doing this two full weeks and the scale seems to be on a slow upward climb. I have to wonder what is going on and why I am still doing this. I am keeping complete track of all of the foods I eat, what I weigh each day, and the workouts that I complete. I just want to take it to some scientist and say, "Study this until it makes sense!"

My weight is fluctuating, but it is slowly climbing up. I don't know why, but it's happening. At the same time, people are noticing a difference. I saw my mom today after a few days and the first thing that she said was, "Wow, you've lost weight!" I told her she was mistaken. In fact I had gained weight. She rephrased her statement and told me that I looked thinner and told me to keep at it. She's been encouraging, but it's hard not to feel beaten down by the scale.

I've increased my work out from a half hour everyday to 45-60 minutes a day. I took a couple of days off right after Halloween because I thought if I gave my muscles a rest that I might finally see something change on the scale. When it didn't work, I went back to working out. I didn't want to become sedentary again so I made myself get right back on the stair-stepper. Hopefully I will see something tomorrow. I am way behind the weight loss schedule that I had wanted to be on.

## November 3, 2012

### Day 14 - Trying not to give up!

Today I noticed that my belly felt slimmer. I also realized that I had been absentmindedly resting my arms on my stomach for who knows how long. So long that it became a habit. So long that when I went to rest my arms in their natural resting place, I noticed when it wasn't there anymore. I can't believe I was so fat that I used my gut as a shelf. Talk about working with what ya got!

It was also nice because Travis said I was looking a little slimmer and it was noticeable in my face and neck. OMGsh did I have a fat neck? Yes, I think I must have because otherwise it wouldn't be noticeably thinner.

That's twice that I noticed I looked and felt slimmer and once that someone else noticed, but the scale hasn't budged! I am now bouncing around between 195 and 192. 192 is my starting weight and I can't believe that 195 is even showing up on the scale! Is this weight loss all in my head? Have I talked enough about diet and exercise that other people are seeing it too? If that's how I did it then I am glad I am broadcasting it to the internet because then maybe I can brainwash the whole world into thinking I am slimming down!

## November 2, 2012

### Day 13 - differences

I went out to dinner with Travis last night and I only ate half my meal and I brought the other half home. I noticed it a couple of days ago as well... I am eating smaller portions. I think I can eat the same amount and I load up my plate, but I just can't. I'm sure if I went all professional eater mode and suffered through it I could stretch my stomach back out again, but who wants that? I feel full with less food and that is good.

Also, yesterday I felt thinner and I paused in the mirror and thought, "am I thinner? ... Nah..." But still, if I think I am noticing it, am I noticing something very small? Perhaps, but the scale still says otherwise.

## November 1, 2012

### On one hand I need to lose weight, On the other hand doughnut. (Day 12)

I fell off the wagon on Halloween night and ate about 6 pieces of candy and I didn't workout unless you count trick-or-treating.

## October 31, 2012

### Day 11

This is getting kind of ridiculous. I have worked out 10 of the last 11 days. I have counted my calories. I have eaten healthy foods. I have been dedicated and true. And no amount of science or explanation can make me feel good about being hungry, sore, and tired, and NOT losing weight!

I was 192 when I weighed in this morning. STILL above where I was a couple of days in to working out. STILL at my starting weight. What am I supposed to do? Keep going? Is it a coincidence that I heard this on 4 different occasions over the last 2 days: "The definition of insanity is doing the same thing over and over again and expecting different results." Isn't that what I am doing? Working out, counting calories, and expecting to lose weight when I haven't?

Oh, and just so we are prefectly clear... that is NOT the definition of insanity.

in·san·i·ty noun \in-ˈsa-nə-tē\
plural in·san·i·ties

Definition of INSANITY

1 : a deranged state of the mind usually occurring as a specific disorder (as schizophrenia)

2 : such unsoundness of mind or lack of understanding as prevents one from having the mental capacity required by law to enter into a particular relationship, status, or transaction or as removes one from criminal or civil responsibility

3 a : extreme folly or unreasonableness
b : something utterly foolish or unreasonable

Geeze that stupid saying drives me crazy.

## October 30, 2012

### Why am I gaining weight in week 2 of my new exercise program? - The (Real) ANSWER!

I was looking for the reason I was gaining weight by searching the internet. What I found was a bunch of people in a bunch of forums complaining about the same problem, and a bunch of their peers giving their best guess. Most of the answers were "Muscle weighs more than fat", which as I said before is not an acceptable answer for me. I did not gain 4 pounds of muscle in 3 days.

Finally I found a link to an article (which I have posted below), and thank goodness! After reading the article it makes more sense as to why my harder workouts are making me gain weight on the scale. It's not because of my hard workout from yesterday or the day before, but it's from the whole new workout routine. As usual, when it takes me forever to find the answer to something on the internet, I always share it so that hopefully, you will stumble upon my search first and find the answer without wasting your time like I did.

You sometimes gain weight when starting a new exercise program

### Weird Cravings

It's still day 10 of my stupid diet/workout lifestyle and I just noticed this morning that I am having weird cravings. So far I have craved Boca Burger (which I can satisfy because it is chopped up in my lunch today), Beef Stroganoff, and Sausage Pizza. Does that mean that I am craving protein?

### Day 10 LOSING MY MIND

I thought I was supposed to be losing weight, not losing my mind! I have been working so hard to stay within my calorie goal and to work out everyday, and today I weighed in at 193! 193! That is more than I weighed before I started this whole thing! So I not only lost weight and gained it back, and then kept it, but you're telling me that I gained a full pound?! Are you SERIOUS?

This is a chart of my caloric intake. A real chart; not one where I fudged the numbers. There it is in black and white and it should mean that I should be losing weight, not maintaining it, and most definitely not gaining it.

Now here is a chart of my weight. You have to ignore the steady part. It looks like I maintained the same weight before this, but that is just because it assumes I did. I have only been checking in on my weight for 10 days, not a month. Anyway, as you can see, the two charts just do NOT jive! And please for the love of Pete, don't give me that "muscle weighs more than fat" talk because I am clearly not gaining any muscle. Not 5 lbs of muscles that's for gosh darned sure!

## October 29, 2012

### I'm Still at it

I can't believe that I am still doing this stupid diet. Outside of hCG I have never stuck with a diet or exercise plan this long before. Usually, I burn myself out. I guess that is why I have never hit this kind of week 2 slump before.

Years of watching The Biggest Loser have taught me that it is really hard to lose weight the 2nd week for some reason. I don't know why and I'm not even sure that it matters, but what I do know is that it happens to them and it's happening to me. I guess I will keep going.

I woke up with a cold today and I'm not sure what is more important, resting and letting my body heal from this cold, or working out to burn calories. I think I will do a VERY , VERY light workout, just to burn a few calories and then I will go to bed early and get lots of rest.

### Day 9 - Grrrrrr

That scale is not my friend! It weighed me at 191 today! I did another hard workout yesterday and my body won't let go of the weight! My workouts have increased from a half hour everyday to an hour everyday and I think my body is protesting. This sucks.

I have noticed that I have been feeling different. A little more confident and better about myself. That's nice and I really enjoy it even if its has no real merit.

## October 28, 2012

### Day 8 - stupid scale

Remember how I had that super hard workout yesterday? Well it sucked. Not just at the time I was doing it, but this morning when I weighed myself I was all the way back to 192; BARF!

I know my swollen muscles are retaining water after an extra hard workout. Betcha didn't know that I knew science. Even so, I don't like seeing that number and had high hopes that I would be another pound closer to my goal.

### Day 7

Since I got the Zombies, Run app, working out is a little less boring but still time consuming and very uncomfortable.

I did mysteriously lose another pound. I now weigh 189. That seems like it can't be true. I thought you were supposed to only lose 2 pounds a week.

Anyway, today is the 7th day which means that is one full week. Omgsh and I had to work out an HOUR tonight because not only did I skip yesterday but I ate a flipping piece of birthday cake today and lemme tell ya, it was NOT worth it.

### Speaking of Zombies

I came across an app on my iPhone called "Zombies, Run!" And I love it. It's a story about zombies that you listen to in your headphones when you run. As you run you pick up supplies that you use to build up your community. I've used it twice so far at it is awesome.

Usually it's $8, but I found out its on sale for half off until Halloween! Otherwise I wouldn't have paid so much for it. ## October 27, 2012 ### Why My Body Thinks We Are Currently Experiencing a Zombie Apocalypse I tried drinking water. I tried putting lemon in it. But this isn't me just feeling a little uncomfortable, this is my stomach growling and groaning for food. My body is in total shock about what is happening here. As far as my body knows, we are in deep trouble. Why My Body Thinks We Are Currently Experiencing a Zombie Apocalypse • Our food intake has been cut short - clearly, we are rationing supplies. • We are working out - obviously, this means we are getting fit to survive • We are running for up to 45 minutes straight - which without a doubt means we are running for our very lives while being chased by hordes of flesh-eating zombies! My poor body... ## October 26, 2012 ### I'm so Hungry A haiku about my stupid new life style change I am so starving. I am so hungry. Working out makes me eat more. My body craves food. No, seriously, I am sitting here and my stomach is audibly protesting my breakfast. I had half a grapefruit and a slice of sourdough bread with half an ounce of cheese. Half an ounce. Infants eat more than half an ounce. That all equals to roughly 280 calories, which is a not-so-bad breakfast, but it wasn't enough. I am so hungry that all I can do is watch the clock and pray for lunch. ### Day 6 I lost another pound so now I am 190 even. That is still a hunk-o-chunk. I still really dislike working out. Nothing makes the clock tick slower than sweating. But I find that I have to work out if I want to eat anything. I ate an extra helping at dinner last night and had to add an extra 15 minutes on to my workout. BLECH. ## October 25, 2012 ### Day 5 I have lost one pound and now weigh 191. That still sucks. I have worked out everyday, but I still don't feel better. I don't have any extra energy and I don't like it even a little bit more than I did before. Working out is still difficult and time consuming and uncomfortable. ## October 24, 2012 ### Day 4 I have been eating healthy, fresh foods and consuming less than 1,200 net calories a day, and working out for 4 days. My weight is the same -- 192lbs. There. I said it. I don't feel any different, except for the soreness from working out. I don't have more energy. I still prefer to sleep in than to work out. I still think exercise is boring. Just thought I would check in and let you know how things are going. ### Circle of Fatness If it seems to you that just about 2 or 3 times a year I commit to losing weight and then fall off the wagon, then you've got pretty average observation skills. I do it all the time. I think I am stuck in a cycle. You know what I think my problem is? I think I come on too strong in the beginning. I obsess. And then I fail. Or maybe my problem is that I have no will power. Well that can't be the problem because I had will power when I did my hCG diet. I had the will power to only eat 500 calories a day, but then I didn't have the will power to keep my weight down. I think it comes back to the whole "lifestyle change" thing... I think I am probably pretty typical. If not typical of what goes on in the world then I am typical of what goes on in the world around me. My mom has been yo-yo dieting for the last 20 years. She was thin for much of my childhood, but always trying to lose weight. She never had a good self image. Even when she was so thin that you could see her shoulder bones sticking out of her clothes, she was trying to lose another 5 lbs. It probably didn't help that her mother-in-law kept calling her fat, but that is besides the point. I was 8 when my brother was born and that was when my mom had trouble keeping her weight down. She lost it at first but after that it was up and down. I'm not saying that this is my mom's fault by any stretch of the imagination, but what I am saying is that I am caught in the same up and down weight loop that I have seen her suffer through for so long. I really don't want to go through everything that she has gone through. Out of desperation to see instant results she has eaten blocks of cheese for breakfast, lunch, and dinner on a no carb diet. How she avoided getting gout is beyond me. I don't want to be desperate. I don't want to suffer. And I don't want to be fat. But I don't know what to do about it? Just exercise and eating right. I guess that is all there is I can do... other than crash diet; I don't want to do that! I have broken cycles in the past. Cycles of hurt, cycles of despair, etc. I know I can break this cycle, too. I know I can, I just don't know how... ## October 16, 2012 ### Saving for Disneyland 45 Days. 45 Days until we get on a plane and go to Disneyland! I cannot believe it! I am so excited! We have a very loose daily plan of where we are going to be and what we are going to do. I want to be able to drop everything and switch plans if Caleb says he wants to go somewhere or do something else. I want to be able to take him to nice places to eat. I want to take him on a magical birthday getaway. And although I am sure that I will have to tell him, "no" about some thing or another, I really don't want to. I am a little stressed about saving spending money. Since I want to be able to take him out to Medieval Times and to Rain Forest Cafe and I want to be able to buy him souvenirs. And I don't want to stress out about it. I am trying so hard to save money, but it's hard. I only get paid once a month, which means that I only get paid one more time before we leave! Plus, I have Caleb's Halloween Party coming up this weekend. I am trying to save money while spending money... It's tough! I am pretty sure that I can get it done, but -whew- it won't be easy! ## September 20, 2012 ### Gearing up for Christmas with K-Mart Layaway This post brought to you by Kmart. All opinions are 100% mine. Due to my many years in retail, I know that now is the time to begin getting ready for Christmas. Acutally, the time was about 12 days ago, but who's counting? You might not be able to tell yet, but the retail stores are all abuzz with the Christmas Spirit right now. They are collecting resumes and even conducting interviews. In fact, most places will have all of their Holiday help hired by October first so they can be trained to handle the masses on Black Friday. Black Friday has never really been my "thing". I prefer to shop for my holiday gifts the easy way. Travis and I were just discussing how we are going to make it through the Christmas season this year since our families have grown so much. Even if we get little gifts for every kid on the list we are still going to take a pretty hefty hit to the pocket book. That's where the free layaway at K-Mart comes into play. It makes a lot more sense to put items on layaway and pay for them over time, and the earlier you get it on layaway, the longer you have to pay. Work smarter, not harder! Don't force yourself to tangle with the masses, risk getting trampled, and still miss out on that special item your little loved one was hoping for. Get it done early and do it right the first time. Put it on layaway now while it's on the shelf and guarantee you get what you want! I made the mistake of mising out on one of my top items last year... this year I won't make the same mistake again. I'm putting my LEAP PAD on layaway!! What could make it better? Kmart's Big Layaway Giveaway, that's what! Once a week, every week until 11/17/12 K-Mart will pick a winner from their layaway customers and pay off their remaining balance! You could get your entire Christmas haul SEVERLY discounted that way! If you have never used layaway before, you are missing out - I'm sad to say it, but it's true. I've been in retail a long time and I can say that layaway is a smart way to do things. See how simple that is? Not only is it easy, but you can see the K-Mart is the smarter choice. Oh you're just so very smart now aren't you. I think you deserve a little pre-Christmas gift for having such a huge brain. ## September 6, 2012 ### Hurricane Isaac Devistation This was posted on my friend's facebook page. She is from Helena, MS which is a small town that hardly anyone knows was torn apart by Hurricane Isaac. After Katrina, the news media focuses mostly on New Orleans, not mentioning the other areas of the Gulf Coast that are affected by Hurricanes and Tropical Storms. The weather channel will tell you that all is well in New Orleans, but it doesn't mean that there aren't people out there who desperately need help. Written by Debbie Waltman ... I still can't believe the utter devastation due to flooding in our local community. I'm not sure most people who were not affected by the flooding even know how truly devastated the Helena, and surrounding areas was impacted. Yesterday the Red Cross came to Helena and served hot meals to people who haven't had hot meals since the storm. After serving over 100 meals, they ran out of food. The loc al ministry called Sisters also served hot meals when the Red Cross ran out. The sisters served over 500 hot meals! 500! And sat with these people and heart their heart breaking stories of how they can't cook because they've lost all appliances and are living out of Ice chests. Their homes are virtually uninhabitable. Many have lost everything. Most refuse to leave, some sleeping outdoors because they don't want to be looted. Children are being BOATED in to catch their school buses. Here are some ways you can help your hurting neighbors: • Get your church together to cook a hot meal one day. • Donations of furniture, appliances (refrigerators, stoves, microwaves and other household items are needed.) • Volunteer to do laundry for victims and pick up and return. • Organize an after spot for kids to come hang out to do homework and get a snack. ( remember, what they're going home to. Can you imagine doing home work in that? And some have no air conditioning!) • Some people are requesting camping tents and air mattresses because they won't leave but can't sleep indoors. • Cleaning supplies are in big demand as are workers to help with clean up. • Groceries that need no refrigeration. (one woman wept when she got a bag with Vienna sausages because she could feed her baby). Hand powered can openers. Paper goods. Water and other drinks. If you can help with any of these needs, you may drop items off at the Helena community center to the sisters group. If you want to do more, talk to sisters about how specifically you want to help. Inbox Tiki Simmons or contact Darlene Whittington or BJ Broome — with BJ Broome and Darlene Whittington. (Also on facebook, but links were not given) **PLEASE SHARE SO OTHERS CAN HELP TOO** ## August 27, 2012 ### My Decorating Style So apparently no one can figure out my decorating style, not even me. I just took a couple of tests online that were supposed to reveal to me the name of my decorating style. So far the results are .... • French Country • Which I really don't care for • Traditional • I can see this • Modern Contemporary • Well, I suppose, but just a little. I wouldn't say I would want it to be my main theme. So I guess I will just have to keep looking. Maybe it would be better to find inspirational pictures... but that is what I was looking for and having trouble finding when I started taking these quizzes. I was trying to find a way to narrow down my search results. Well, I guess it's just back to the old drawing board for me. ## August 17, 2012 ### Africa Hot I don't know how hot it is in Africa right now, but I am way too hot to look it up. It's been triple digits for 3 days in a row... or has it been 4? My sanity fails me. Alright Eastcoasters, Mid-USers, Southerners, New Englanders, and Rest-of-America-ers... I know you have been having a heat wave for like 3 months. I feel bad for you, I have since the start of it... But I am simply not used to this. No matter how hot it gets this week you won't hear me saying that I miss the rain! I do not miss the rain. I'm happy it's Summer. I have been waiting for this Summer since the first rain drop fell in November. I am enjoying it thoroughly and I can guarantee that I have made the most out of every sunny minute of it, but DANG it if isn't HOT outside! Topping off the reasons to feel sorry for myself is the fact that the air conditioner is broken at work and we can't get anyone to come fix it because no one wants to take on a commercial account. Seems silly to me, but maybe they are just too much work. I don't know a lot about the HVAC world... I know nothing about the HVAC world, actually. Things are supposed to cool down a little this weekend and we are going to relax at Timothy Lake with the family. Ah, the old watering hole... except it's not old to me because I've never been there, but I was trying to tie it in with the Africa thing... ## August 13, 2012 ### Disney Flight Booked! There are tiny little bubbles of joy bursting their happy juice all over the inside of my heart right now because I just booked the flight to Disneyland! In a few hours I will be booking the hotel and the park passes! I am SO EXCITED! It feels like a magical rainbow colored unicorn is dancing in my brain. I am so happy that his is really happening. I am taking my son to Disneyland for his 5th birthday! Just like I promised him on the day he was born. He will turn 5 years old to the second while we are sitting in Medieval Times. This is going to be the best Vacation ever! If you're wondering about all of my research and frugality, yes it was there and has been for the last 5 years. I booked a flight with Delta which was not only nonstop, but super cheap. I used Orbitz.com, but I didn't see much of a price difference from website to website. I checked Orbitz, Travelocity, Cheap-O air, and the Delta website. I ended up going with Orbitz because I like their mobile app. It has saved me several times in the past when gates have been changed last minute by sending me text message alerts. The travel plans were booked using Small World Vacations as I have mentioned in other posts about Disney. ## August 10, 2012 ### College Football Tickets It's only been about a year since I started following college football. College sports never really interested me before, but as my knowledge for the NFL grew and I became more aware of the intricacies of the pro sport, I was more interested in up and coming young talent. One thing about College Football is that the players and the fans are so passionate and that makes it more fun, I think. You never see a kicker doing as much in the NFL as you do in College football. The young men playing are spry and quick, there is a lot of fast action. I'm learning to appreciate that more, but I still love the hard hits of the NFL as well. You may also be starting to follow College sports or College football as well and wondering where you can get tickets to watch your favorite teams play. You can check out central florida golden knights football tickets, unlv rebels football tickets,ucla bruins football tickets. Me? Well I'm a New Orleans Saints fan, so naturally I would prefer to see LSU play... Hey, it's not selling out because my pro team isn't going to win a thing this year! Still, gotta love 'em though... WHO DAT! ## August 9, 2012 ### Make Money Fast on your Smart Phone It's no secret that I use my blog to make a little cash on the side. I get paid for some of the blogs I write; it's true. To try to keep a little integrity on my blog, I always mention if the post is sponsored or not. This one is not. For the past few months I have been using an iPhone app to make a little cash. I was first attracted to it because it is made by one of the companies that already pays me for blogging. This app is called WeReward. I have made$78.00 so far on WeReward and I'd make a lot more if I remembered to use it. All you do is take a picture of which business you are at. Today I went to Starbucks and snapped a shot of my coffee. I made 50 cents. Later, I went to McDonald's for lunch. A quick picture of my fries were worth another 50 cents. After work I went with my sister to the grocery store and took a picture of her with her purchase. Another 50 cents. That evening my dad took us all out for dinner and I checked in at the restaurant for 10 cents.

what I like about WeReward is that you don't have to spend money to make money. Like as the restaurant all I had to do was check in, and it's like that with a lot of places. You just check in and you earn money. The business gets the free advertising on your Facebook or Twitter and you get money. Not gift cards - real money. They deposit it into your Paypal account.

I suggest that if you have a smart phone you check out WeReward.


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## G = C9×F5order 180 = 22·32·5

### Direct product of C9 and F5

Aliases: C9×F5, C5⋊C36, C452C4, C15.C12, D5.C18, C3.(C3×F5), (C3×F5).C3, (C3×D5).2C6, (C9×D5).2C2, SmallGroup(180,5)

Series: Derived Chief Lower central Upper central

 Derived series C1 — C5 — C9×F5
 Chief series C1 — C5 — C15 — C3×D5 — C9×D5 — C9×F5
 Lower central C5 — C9×F5
 Upper central C1 — C9

Generators and relations for C9×F5
G = < a,b,c | a9=b5=c4=1, ab=ba, ac=ca, cbc-1=b3 >

Smallest permutation representation of C9×F5
On 45 points
Generators in S45
(1 2 3 4 5 6 7 8 9)(10 11 12 13 14 15 16 17 18)(19 20 21 22 23 24 25 26 27)(28 29 30 31 32 33 34 35 36)(37 38 39 40 41 42 43 44 45)
(1 29 22 16 40)(2 30 23 17 41)(3 31 24 18 42)(4 32 25 10 43)(5 33 26 11 44)(6 34 27 12 45)(7 35 19 13 37)(8 36 20 14 38)(9 28 21 15 39)
(10 32 25 43)(11 33 26 44)(12 34 27 45)(13 35 19 37)(14 36 20 38)(15 28 21 39)(16 29 22 40)(17 30 23 41)(18 31 24 42)

G:=sub<Sym(45)| (1,2,3,4,5,6,7,8,9)(10,11,12,13,14,15,16,17,18)(19,20,21,22,23,24,25,26,27)(28,29,30,31,32,33,34,35,36)(37,38,39,40,41,42,43,44,45), (1,29,22,16,40)(2,30,23,17,41)(3,31,24,18,42)(4,32,25,10,43)(5,33,26,11,44)(6,34,27,12,45)(7,35,19,13,37)(8,36,20,14,38)(9,28,21,15,39), (10,32,25,43)(11,33,26,44)(12,34,27,45)(13,35,19,37)(14,36,20,38)(15,28,21,39)(16,29,22,40)(17,30,23,41)(18,31,24,42)>;

G:=Group( (1,2,3,4,5,6,7,8,9)(10,11,12,13,14,15,16,17,18)(19,20,21,22,23,24,25,26,27)(28,29,30,31,32,33,34,35,36)(37,38,39,40,41,42,43,44,45), (1,29,22,16,40)(2,30,23,17,41)(3,31,24,18,42)(4,32,25,10,43)(5,33,26,11,44)(6,34,27,12,45)(7,35,19,13,37)(8,36,20,14,38)(9,28,21,15,39), (10,32,25,43)(11,33,26,44)(12,34,27,45)(13,35,19,37)(14,36,20,38)(15,28,21,39)(16,29,22,40)(17,30,23,41)(18,31,24,42) );

G=PermutationGroup([[(1,2,3,4,5,6,7,8,9),(10,11,12,13,14,15,16,17,18),(19,20,21,22,23,24,25,26,27),(28,29,30,31,32,33,34,35,36),(37,38,39,40,41,42,43,44,45)], [(1,29,22,16,40),(2,30,23,17,41),(3,31,24,18,42),(4,32,25,10,43),(5,33,26,11,44),(6,34,27,12,45),(7,35,19,13,37),(8,36,20,14,38),(9,28,21,15,39)], [(10,32,25,43),(11,33,26,44),(12,34,27,45),(13,35,19,37),(14,36,20,38),(15,28,21,39),(16,29,22,40),(17,30,23,41),(18,31,24,42)]])

45 conjugacy classes

 class 1 2 3A 3B 4A 4B 5 6A 6B 9A ··· 9F 12A 12B 12C 12D 15A 15B 18A ··· 18F 36A ··· 36L 45A ··· 45F order 1 2 3 3 4 4 5 6 6 9 ··· 9 12 12 12 12 15 15 18 ··· 18 36 ··· 36 45 ··· 45 size 1 5 1 1 5 5 4 5 5 1 ··· 1 5 5 5 5 4 4 5 ··· 5 5 ··· 5 4 ··· 4

45 irreducible representations

 dim 1 1 1 1 1 1 1 1 1 4 4 4 type + + + image C1 C2 C3 C4 C6 C9 C12 C18 C36 F5 C3×F5 C9×F5 kernel C9×F5 C9×D5 C3×F5 C45 C3×D5 F5 C15 D5 C5 C9 C3 C1 # reps 1 1 2 2 2 6 4 6 12 1 2 6

Matrix representation of C9×F5 in GL5(𝔽181)

 39 0 0 0 0 0 132 0 0 0 0 0 132 0 0 0 0 0 132 0 0 0 0 0 132
,
 1 0 0 0 0 0 180 180 180 180 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0
,
 19 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 180 180 180 180

G:=sub<GL(5,GF(181))| [39,0,0,0,0,0,132,0,0,0,0,0,132,0,0,0,0,0,132,0,0,0,0,0,132],[1,0,0,0,0,0,180,1,0,0,0,180,0,1,0,0,180,0,0,1,0,180,0,0,0],[19,0,0,0,0,0,1,0,0,180,0,0,0,1,180,0,0,0,0,180,0,0,1,0,180] >;

C9×F5 in GAP, Magma, Sage, TeX

C_9\times F_5
% in TeX

G:=Group("C9xF5");
// GroupNames label

G:=SmallGroup(180,5);
// by ID

G=gap.SmallGroup(180,5);
# by ID

G:=PCGroup([5,-2,-3,-2,-3,-5,30,66,1804,614]);
// Polycyclic

G:=Group<a,b,c|a^9=b^5=c^4=1,a*b=b*a,a*c=c*a,c*b*c^-1=b^3>;
// generators/relations

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