Sanjay Ranka Ranka's Books

Video Based Machine Learning for Traffic Intersections

By: Tania Banerjee,Xiaohui Huang,Aotian Wu,Ke Chen,Anand Rangarajan,Sanjay Ranka

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Video Based Machine Learning for Traffic Intersections

By: Tania Banerjee,Xiaohui Huang,Aotian Wu,Ke Chen,Anand Rangarajan,Sanjay Ranka

Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

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Page Count

213

Publisher

CRC Press

Published Date

ISBN 10

1000969770

ISBN 13

9781000969771

Dynamic Reconfiguration in Real-Time Systems

Energy, Performance, and Thermal Perspectives

By: Weixun Wang,Prabhat Mishra,Sanjay Ranka

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Dynamic Reconfiguration in Real-Time Systems

Energy, Performance, and Thermal Perspectives

By: Weixun Wang,Prabhat Mishra,Sanjay Ranka

Given the widespread use of real-time multitasking systems, there are tremendous optimization opportunities if reconfigurable computing can be effectively incorporated while maintaining performance and other design constraints of typical applications. The focus of this book is to describe the dynamic reconfiguration techniques that can be safely used in real-time systems. This book provides comprehensive approaches by considering synergistic effects of computation, communication as well as storage together to significantly improve overall performance, power, energy and temperature.

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Page Count

232

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461402786

ISBN 13

9781461402787

Modeling and Optimization of Parallel and Distributed Embedded Systems

By: Arslan Munir,Ann Gordon-Ross,Sanjay Ranka

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Modeling and Optimization of Parallel and Distributed Embedded Systems

By: Arslan Munir,Ann Gordon-Ross,Sanjay Ranka

This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems. Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation. Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies. Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research.

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Page Count

399

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119086418

ISBN 13

9781119086413

Data Driven Approaches for Healthcare

Machine learning for Identifying High Utilizers

By: Chengliang Yang,Chris Delcher,Elizabeth Shenkman,Sanjay Ranka

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Data Driven Approaches for Healthcare

Machine learning for Identifying High Utilizers

By: Chengliang Yang,Chris Delcher,Elizabeth Shenkman,Sanjay Ranka

Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics

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Publisher

CRC Press

Published Date

ISBN 10

1000701255

ISBN 13

9781000701258

Elements of Artificial Neural Networks

By: Kishan Mehrotra,Chilukuri K. Mohan,Sanjay Ranka

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Elements of Artificial Neural Networks

By: Kishan Mehrotra,Chilukuri K. Mohan,Sanjay Ranka

Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.

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Page Count

376

Publisher

MIT Press

Published Date

ISBN 10

0262133288

ISBN 13

9780262133289

Hypercube Algorithms

with Applications to Image Processing and Pattern Recognition

By: Sanjay Ranka,Sartaj Sahni

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Hypercube Algorithms

with Applications to Image Processing and Pattern Recognition

By: Sanjay Ranka,Sartaj Sahni

Fundamentals algorithms for SIMD and MIMD hypercubes are developed. These include algorithms for such problems as data broadcasting, data sum, prefix sum, shift, data circulation, data accumulation, sorting, random access reads and writes and data permutation. The fundamental algorithms are then used to obtain efficient hypercube algorithms for matrix multiplication, image processing problems such as convolution, template matching, hough transform, clustering and image processing transformation, and string editing. Most of the algorithms in this book are for hypercubes with the number of processors being a function of problems size. However, for image processing problems, the book also includes algorithms for and MIMD hypercube with a small number of processes. Experimental results on an NCUBE/77 MIMD hypercube are also presented. The book is suitable for use in a one-semester or one-quarter course on hypercube algorithms. For students with no prior exposure to parallel algorithms, it is recommended that one week will be spent on the material in chapter 1, about six weeks on chapter 2 and one week on chapter 3. The remainder of the term can be spent covering topics from the rest of the book.

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Page Count

241

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1461396921

ISBN 13

9781461396925

Book's by Sanjay Ranka Ranka

Video Based Machine Learning for Traffic Intersections

Video Based Machine Learning for Traffic Intersections

Dynamic Reconfiguration in Real-Time Systems

Dynamic Reconfiguration in Real-Time Systems

Modeling and Optimization of Parallel and Distributed Embedded Systems

Modeling and Optimization of Parallel and Distributed Embedded Systems

Data Driven Approaches for Healthcare

Data Driven Approaches for Healthcare

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks

Hypercube Algorithms

Hypercube Algorithms

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