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Articles 9751 - 9780 of 17421

Full-Text Articles in Computer Sciences

Forensic State Acquisition From Internet Of Things (Fsaiot): A General Framework And Practical Approach For Iot Forensics Through Iot Device State Acquisition, Christopher S. Meffert, Devon R. Clark, Ibrahim Baggili, Frank Breitinger Aug 2017

Forensic State Acquisition From Internet Of Things (Fsaiot): A General Framework And Practical Approach For Iot Forensics Through Iot Device State Acquisition, Christopher S. Meffert, Devon R. Clark, Ibrahim Baggili, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

IoT device forensics is a difficult problem given that manufactured IoT devices are not standardized, many store little to no historical data, and are always connected; making them extremely volatile. The goal of this paper was to address these challenges by presenting a primary account for a general framework and practical approach we term Forensic State Acquisition from Internet of Things (FSAIoT). We argue that by leveraging the acquisition of the state of IoT devices (e.g. if an IoT lock is open or locked), it becomes possible to paint a clear picture of events that have occurred. To this end, …

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Research On Improving Navigation Safety Based On Big Data And Cloud Computing Technology For Qiongzhou Strait, Rui Wang Aug 2017

Research On Improving Navigation Safety Based On Big Data And Cloud Computing Technology For Qiongzhou Strait, Rui Wang

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

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The Future Is Coming : Research On Maritime Communication Technology For Realization Of Intelligent Ship And Its Impacts On Future Maritime Management, Jiacheng Ke Aug 2017

The Future Is Coming : Research On Maritime Communication Technology For Realization Of Intelligent Ship And Its Impacts On Future Maritime Management, Jiacheng Ke

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

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Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer Aug 2017

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …

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Improving Pure-Tone Audiometry Using Probabilistic Machine Learning Classification, Xinyu Song Aug 2017

Improving Pure-Tone Audiometry Using Probabilistic Machine Learning Classification, Xinyu Song

McKelvey School of Engineering Graduate Student Theses & Dissertations

Hearing loss is a critical public health concern, affecting hundreds millions of people worldwide and dramatically impacting quality of life for affected individuals. While treatment techniques have evolved in recent years, methods for assessing hearing ability have remained relatively unchanged for decades. The standard clinical procedure is the modified Hughson-Westlake procedure, an adaptive pure-tone detection task that is typically performed manually by audiologists, costing millions of collective hours annually among healthcare professionals. In addition to the high burden of labor, the technique provides limited detail about an individual’s hearing ability, estimating only detection thresholds at a handful of pre-defined pure-tone …

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Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang Aug 2017

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

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Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens Aug 2017

Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens

Michigan Tech Publications, Part 1

Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, …

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Visually Analyzing The Impacts Of Essential Air Service Funding Decisions, Rohan Kashuka, Chittayong Surakitbanharn, Calvin Yau, David S. Ebert Aug 2017

Visually Analyzing The Impacts Of Essential Air Service Funding Decisions, Rohan Kashuka, Chittayong Surakitbanharn, Calvin Yau, David S. Ebert

The Summer Undergraduate Research Fellowship (SURF) Symposium

Essential Air Service (EAS) is a U.S. government subsidy program which ensures maintenance of commercial airline services in small deregulated communities. The program’s budget currently is around $250 million annually, which is used as subsidy for airlines to maintain a minimal level of scheduled air service in relatively smaller airports. It is evident that 2% of the FAA budget is being spent to maintain air service in smaller communities, but there is not enough evidence to prove that all the current decisions made by Congress about EAS are advantageous. To understand these decisions, 15 years of data produced by the …

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Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico Aug 2017

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures …

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Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel Aug 2017

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model) …

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Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher Aug 2017

Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher

The Summer Undergraduate Research Fellowship (SURF) Symposium

Graphene is a 2-dimensional element of high practical importance. Despite its exceptional properties, graphene’s real applications in industrial or commercial products have been limited. There are many methods to produce graphene, but none has been successful in commercializing its production. Roll-to-roll plasma chemical vapor deposition (CVD) is used to manufacture graphene at large scale. In this research, we present a Bayesian linear regression model to predict the roll-to-roll plasma system’s electrode voltage and current; given a particular set of inputs. The inputs of the plasma system are power, pressure and concentration of gases; hydrogen, methane, oxygen, nitrogen and argon. This …

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Web-Based Interactive Social Media Visual Analytics, Diego Rodríguez-Baquero, Jiawei Zhang, David S. Ebert, Sorin A. Matei Aug 2017

Web-Based Interactive Social Media Visual Analytics, Diego Rodríguez-Baquero, Jiawei Zhang, David S. Ebert, Sorin A. Matei

The Summer Undergraduate Research Fellowship (SURF) Symposium

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones …

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Development Of A Water Quality Status And Trend Detection Tool*, Ruchir Aggarwal, Valeria Mijares, Margaret W. Gitau Aug 2017

Development Of A Water Quality Status And Trend Detection Tool*, Ruchir Aggarwal, Valeria Mijares, Margaret W. Gitau

The Summer Undergraduate Research Fellowship (SURF) Symposium

Water Quality Index (WQI) models have been developed since the early 1970s. They present a means by which water quality status and trends can be compared across time and space on the basis of a composite value computed using existing water quality data. There is a need for a tool that can bring the different water quality parameters together and calculate the WQIs so as to facilitate data use in predictive modeling and water quality management. We are developing a software tool that can be used by water quality managers and others with different technical backgrounds to calculate WQI of …

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Purdue Airsense: An Affordable Way To Measure And Study Air Pollution, Stephane Junior Nouafo Wanko, Shadi Tariq Azouz, Ruihang Du, Brandon Boor, Greg Michalski Aug 2017

Purdue Airsense: An Affordable Way To Measure And Study Air Pollution, Stephane Junior Nouafo Wanko, Shadi Tariq Azouz, Ruihang Du, Brandon Boor, Greg Michalski

The Summer Undergraduate Research Fellowship (SURF) Symposium

Air pollution is a major health hazard worldwide, accounting for one-eighth of all deaths in 2012 (World Health Organization). Globally, there is a severe lack of ground-based spatiotemporal monitoring of gaseous and particulate air pollutants, particularly in Africa, South and Central America, and the Middle East. This is in great part due to the high costs of air quality instrumentation that meet accuracy and reliability criteria set by monitoring agencies. The air quality data that is available is often not presented to the public in a user-friendly manner. Taking advantage of recent developments in low-cost sensing technologies, an integrated sensor …

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Structure-Force Field Generator For Molecular Dynamics Simulations, Carlos M. Patiño, Lorena Alzate, Alejandro Strachan Aug 2017

Structure-Force Field Generator For Molecular Dynamics Simulations, Carlos M. Patiño, Lorena Alzate, Alejandro Strachan

The Summer Undergraduate Research Fellowship (SURF) Symposium

Atomistic and molecular simulations have become an important research field due to the progress made in computer performance and the necessity of new and improved materials. Despite this, first principle simulations of large molecules are still not possible because the high computational time and resources required. Other methods, such as molecular dynamics, allow the simplification of calculations by defining energy terms to describe multiple atom interactions without compromising accuracy significantly. A group of these energy terms is called a force field, and each force field has its own descriptions and parameters. The objective of this project was to develop a …

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A Feature-Based Structural Measure: An Image Similarity Measure For Face Recognition, Noor A. Shnain, Zahir Hussain, Song F. Lu Aug 2017

A Feature-Based Structural Measure: An Image Similarity Measure For Face Recognition, Noor A. Shnain, Zahir Hussain, Song F. Lu

Research outputs 2014 to 2021

Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called …

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Breaking Into The Vault: Privacy, Security And Forensic Analysis Of Android Vault Applications, Xiaolu Zhang, Ibrahim Baggili, Frank Breitinger Aug 2017

Breaking Into The Vault: Privacy, Security And Forensic Analysis Of Android Vault Applications, Xiaolu Zhang, Ibrahim Baggili, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

In this work we share the first account for the forensic analysis, security and privacy of Android vault applications. Vaults are designed to be privacy enhancing as they allow users to hide personal data but may also be misused to hide incriminating files. Our work has already helped law enforcement in the state of Connecticut to reconstruct 66 incriminating images and 18 videos in a single criminal case. We present case studies and results from analyzing 18 Android vault applications (accounting for nearly 220 million downloads from the Google Play store) by reverse engineering them and examining the forensic artifacts …

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A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener Aug 2017

A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener

Electronic Theses and Dissertations

This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, …

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Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw Aug 2017

Semantic Visualization For Short Texts With Word Embeddings, Van Minh Tuan Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Semantic visualization integrates topic modeling and visualization, such that every document is associated with a topic distribution as well as visualization coordinates on a low-dimensional Euclidean space. We address the problem of semantic visualization for short texts. Such documents are increasingly common, including tweets, search snippets, news headlines, or status updates. Due to their short lengths, it is difficult to model semantics as the word co-occurrences in such a corpus are very sparse. Our approach is to incorporate auxiliary information, such as word embeddings from a larger corpus, to supplement the lack of co-occurrences. This requires the development of a …

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Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu Aug 2017

Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu

Research Collection School Of Computing and Information Systems

We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …

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Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham Aug 2017

Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that …

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An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo Aug 2017

An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo

Faculty Publications

The prediction of drug–target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug–target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, …

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Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng Aug 2017

Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng

Research Collection School Of Computing and Information Systems

Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set …

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Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis Aug 2017

Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision-making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.

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Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok Aug 2017

Toward Accurate Network Delay Measurement On Android Phones, Weichao Li, Daoyuan Wu, Rocky K. C. Chang, Ricky K. P. Mok

Research Collection School Of Computing and Information Systems

Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps-Ookla Speedtest and MobiPerf-have their RTT measurements inflated. We build three test apps that cover three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete …

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Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng Aug 2017

Hibs-Ksharing: Hierarchical Identity-Based Signature Key Sharing For Automotive, Zhuo Wei, Yanjiang Yang, Yongdong Wu, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

Equipped with various sensors and intelligent systems, modern cars turn into entities with connectivity, autonomy, and safety. Car rental/car sharing is an innovative transportation concept and integral in today's urban living. It enables users to access a fleet of vehicles located throughout cities. Complementing public transportation, the car-sharing service helps people to meet their transportation needs economically and in an environmentally responsible manner. When a customer wants to rent a car from a rental company or an owner wants to share a private car with his/her friends or family members, the customer or the user should gain admission to the …

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Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao Aug 2017

Embedding-Based Representation Of Categorical Data By Hierarchical Value Coupling Learning, Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao

Research Collection School Of Computing and Information Systems

Learning the representation of categorical data with hierarchical value coupling relationships is very challenging but critical for the effective analysis and learning of such data. This paper proposes a novel coupled unsupervised categorical data representation (CURE) framework and its instantiation, i.e., a coupled data embedding (CDE) method, for representing categorical data by hierarchical value-to-value cluster coupling learning. Unlike existing embedding- and similarity-based representation methods which can capture only a part or none of these complex couplings, CDE explicitly incorporates the hierarchical couplings into its embedding representation. CDE first learns two complementary feature value couplings which are then used to cluster …

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Visualization And 3d Printing Of A 3d Solar Tracker Model Using Mayavi And Pov-Ray, Aditya Mehra Aug 2017

Visualization And 3d Printing Of A 3d Solar Tracker Model Using Mayavi And Pov-Ray, Aditya Mehra

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

In this work, we have created a realistic model of a solar tracker using Mayavi: 3D scientific data visualization and plotting in Python, Enthought Canopy:a comprehensive Python analysis environment and Persistence of Vision Ray Tracer, or POV-Ray, a ray tracing program which generates photo-realistic images from a text-based scene description, a model of the solar tracker was also 3D printed.

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Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi Aug 2017

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …

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Improving Pattern Recognition And Neural Network Algorithms With Applications To Solar Panel Energy Optimization, Ernesto Zamora Ramos Aug 2017

Improving Pattern Recognition And Neural Network Algorithms With Applications To Solar Panel Energy Optimization, Ernesto Zamora Ramos

UNLV Theses, Dissertations, Professional Papers, and Capstones

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize …

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