Download or read book Efficient deep neural network for intelligent robot system Focusing on visual signal processing written by Xiao Bai and published by Frontiers Media SA. This book was released on 2023-05-04 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Information Processing and VLSI written by Bing J. Sheu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.

Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Download or read book Efficient Deep Neural Networks Architectures for Video Analytics Systems written by Zeinab Hakimi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a remarkable surge in the volume of digital data across various formats and domains. For instance, modern camera systems leverage new technologies and the fusion of information from multiple views to capture high-quality images. As a result of this data explosion, there is a growing interest and demand for analyzing information using data-intensive machine learning algorithms, particularly deep neural networks (DNNs). However, despite the success of deep learning approaches in various domains, their performance on small edge devices with constrained computing power and memory are limited. The primary objective of this thesis is to design efficient intelligent vision systems that effectively overcome the limitations of deep neural networks (DNNs) when deployed on edge devices with limited resources. This work explores a variety of methods aimed at optimizing the utilization of information and context in the design of DNN architectures. By leveraging these techniques, the proposed systems aim to enhance the performance and efficiency of DNNs in resource-constrained environments. Specifically, the thesis proposes context-aware methods to differentiate between low and high quality sensors representations by incorporating the context into the CNN models and reduce the computation and communication costs of edge devices in a distributed camera system. The primary objective is to minimize the computation and communication costs associated with edge devices in a distributed camera system. In addition, the thesis proposes a fault-tolerant mechanism to address the challenges posed by abnormal and noisy data in the system, particularly due to unknown conditions. This mechanism serves as a solution to mitigate the adverse effects of such data, ensuring the reliability and robustness of the proposed system. Furthermore, a resolution-aware multi-view design is outlined to address data transmission and power challenges in embedded devices. Moreover, the thesis introduces a patch-based attention-likelihood technique, designed to enhance the recognition performance of small objects within high-resolution images. This technique effectively reduces the computational burden of handling high-resolution images on edge devices by processing sub-samples of the input patches. By selectively attending to relevant patches, the proposed approach significantly improves the overall efficiency of object recognition while maintaining a high level of accuracy. Finally, the thesis introduces an efficient task-adaptive visual transformer model specifically designed for fine-grained classification tasks on IoT devices. By optimizing the system's performance for IoT devices, it enables efficient and reliable fine-grained classification without compromising computational resources or compromising the accuracy of results. Overall, this thesis offers a comprehensive approach to overcoming the limitations associated with deploying deep neural networks (DNNs) on edge devices within visual intelligent systems.

Download or read book New Approaches for Multidimensional Signal Processing written by Roumen Kountchev and published by Springer Nature. This book was released on 2021-04-20 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2020), held at Technical University of Sofia, Sofia, Bulgaria, during 09–11 July 2020. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.

Download or read book Cognitive Systems and Signal Processing written by Fuchun Sun and published by Springer. This book was released on 2019-04-27 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 1005 and CCIS 1006) constitutes the refereed proceedings of the 4th International Conference on Cognitive Systems and Signal Processing, ICCSIP2018, held in Beijing, China, in November and December 2018. The 96 revised full papers presented were carefully reviewed and selected from 169 submissions. The papers are organized in topical sections on vision and image; algorithms; robotics; human-computer interaction; deep learning; information processing and automatic driving.

Download or read book From Line Drawings to Human Actions written by Fang Wang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, deep neural networks have been very successful in computer vision, speech recognition, and artificial intelligent systems. The rapid growth of data and fast increasing computational tools provide solid foundations for the applications which rely on the learning of large scale deep neural networks with millions of parameters. The deep learning approaches have been proved to be able to learn powerful representations of the inputs in various tasks, such as image classification, object recognition, and scene understanding. This thesis demonstrates the generality and capacity of deep learning approaches through a series of case studies including image matching and human activity understanding. In these studies, I explore the combinations of the neural network models with existing machine learning techniques and extend the deep learning approach for each task. Four related tasks are investigated: 1) image matching through similarity learning; 2) human action prediction; 3) finger force estimation in manipulation actions; and 4) bimodal learning for human action understanding. Deep neural networks have been shown to be very efficient in supervised learning. Further, in some tasks, one would like to group the features of the samples in the same category close to each other, in additional to the discriminative representation. Such kind of properties is desired in a number of applications, such as semantic retrieval, image quality measurement, and social network analysis, etc. My first study is to develop a similarity learning method based on deep neural networks for image matching between sketch images and 3D models. In this task, I propose to use Siamese network to learn similarities of sketches and develop a novel method for sketch based 3D shape retrieval. The proposed method can successfully learn the representations of sketch images as well as the similarities, then the 3D shape retrieval problem can be solved with off-the-shelf nearest neighbor methods. After studying the representation learning methods for static inputs, my focus turns to learning the representations of sequential data. To be specific, I focus on manipulation actions, because they are widely used in the daily life and play important parts in the human-robot collaboration system. Deep neural networks have been shown to be powerful to represent short video clips [Donahue et al., 2015]. However, most existing methods consider the action recognition problem as a classification task. These methods assume the inputs are pre-segmented videos and the outputs are category labels. In the scenarios such as the human-robot collaboration system, the ability to predict the ongoing human actions at an early stage is highly important. I first attempt to address this issue with a fast manipulation action prediction method. Then I build the action prediction model based on Long Short-Term Memory (LSTM) architecture. The proposed approach processes the sequential inputs as continuous signals and keeps updating the prediction of the intended action based on the learned action representations. Further, I study the relationships between visual inputs and the physical information, such as finger forces, that involved in the manipulation actions. This is motivated by recent studies in cognitive science which show that the subject's intention is strongly related to the hand movements during an action execution. Human observers can interpret other's actions in terms of movements and forces, which can be used to repeat the observed actions. If a robot system has the ability to estimate the force feedbacks, it can learn how to manipulate an object by watching human demonstrations. In this work, the finger forces are estimated by only watching the movement of hands. A modified LSTM model is used to regress the finger forces from video frames. To facilitate this study, a specially designed sensor glove has been used to collect data of finger forces, and a new dataset has been collected to provide synchronized streams of videos and finger forces. Last, I investigate the usefulness of physical information in human action recognition, which is an application of bimodal learning, where both the vision inputs and the additional information are used to learn the action representation. My study demonstrates that, by combining additional information with the vision inputs, the accuracy of human action recognition can be improved steadily. I extend the LSTM architecture to accept both video frames and sensor data as bimodal inputs to predict the action. A hallucination network is jointly trained to approximate the representations of the additional inputs. During the testing stage, the hallucination network generates approximated representations that used for classification. In this way, the proposed method does not rely on the additional inputs for testing.

Download or read book Computational Data and Social Networks written by Minh Hoàng Hà and published by Springer Nature. This book was released on with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book New Approaches for Multidimensional Signal Processing written by Roumen Kountchev and published by Springer Nature. This book was released on 2022-12-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2022), held at Technical University of Sofia, Sofia, Bulgaria, during 23–25 June 2022. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.

Download or read book Neuromorphic Computing Systems for Industry 4 0 written by Dhanasekar, S. and published by IGI Global. This book was released on 2023-07-19 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery-powered and deeply embedded devices are challenged to perform AI functions such as computer vision and voice recognition. Microchip Technology Inc., via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain Memory Solution. The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory computer solution is ideal for an AI application. Neuromorphic Computing Systems for Industry 4.0 covers the available literature in the field of neural computing-based microchip technology. It provides further research opportunities in this dynamic field. Covering topics such as emotion recognition, biometric authentication, and neural network protection, this premier reference source is an essential resource for technology developers, computer scientists, engineers, students and educators of higher education, librarians, researchers, and academicians.

Download or read book Robot Intelligence Technology and Applications 5 written by Jong-Hwan Kim and published by Springer. This book was released on 2018-05-31 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes papers from the 5th International Conference on Robot Intelligence Technology and Applications held at KAIST, Daejeon, Korea on December 13–15, 2017. It covers the following areas: artificial intelligence, autonomous robot navigation, intelligent robot system design, intelligent sensing and control, and machine vision. The topics included in this book are deep learning, deep neural networks, image understanding, natural language processing, speech/voice/text recognition, reasoning & inference, sensor integration/fusion/perception, multisensor data fusion, navigation/SLAM/localization, distributed intelligent algorithms and techniques, ubiquitous computing, digital creatures, intelligent agents, computer vision, virtual/augmented reality, surveillance, pattern recognition, gesture recognition, fingerprint recognition, animation and virtual characters, and emerging applications. This book is a valuable resource for robotics scientists, computer scientists, artificial intelligence researchers and professionals in universities, research institutes and laboratories.

Download or read book Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era written by Srinivasan, A. and published by IGI Global. This book was released on 2022-10-21 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, there has been an increasing interest in using machine learning and, in the last few years, deep learning methods combined with other vision and image processing techniques to create systems that solve vision problems in different fields. There is a need for academicians, developers, and industry-related researchers to present, share, and explore traditional and new areas of computer vision, machine learning, deep learning, and their combinations to solve problems. The Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era is designed to serve researchers and developers by sharing original, innovative, and state-of-the-art algorithms and architectures for applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, and more. It integrates the knowledge of the growing international community of researchers working on the application of machine learning and deep learning methods in vision and robotics. Covering topics such as brain tumor detection, heart disease prediction, and medical image detection, this premier reference source is an exceptional resource for medical professionals, faculty and students of higher education, business leaders and managers, librarians, government officials, researchers, and academicians.

Download or read book Deep Learning in Object Recognition Detection and Segmentation written by Xiaogang Wang and published by . This book was released on 2016 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a major breakthrough in artificial intelligence, deep learning has achieved very impressive success in solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This article provides a historical overview of deep learning and focus on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. The discussed research topics on object recognition include image classification on ImageNet, face recognition, and video classification. The detection part covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). On the segmentation side, the article discusses the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing and saliency detection. Object recognition is considered as whole-image classification, while detection and segmentation are pixelwise classification tasks. Their fundamental differences will be discussed in this article. Fully convolutional neural networks and highly efficient forward and backward propagation algorithms specially designed for pixelwise classification task will be introduced. The covered application domains are also much diversified. Human and face images have regular structures, while general object and scene images have much more complex variations in geometric structures and layout. Videos include the temporal dimension. Therefore, they need to be processed with different deep models. All the selected domain applications have received tremendous attentions in the computer vision and multimedia communities. Through concrete examples of these applications, we explain the key points which make deep learning outperform conventional computer vision systems. (1) Different than traditional pattern recognition systems, which heavily rely on manually designed features, deep learning automatically learns hierarchical feature representations from massive training data and disentangles hidden factors of input data through multi-level nonlinear mappings. (2) Different than existing pattern recognition systems which sequentially design or train their key components, deep learning is able to jointly optimize all the components and crate synergy through close interactions among them. (3) While most machine learning models can be approximated with neural networks with shallow structures, for some tasks, the expressive power of deep models increases exponentially as their architectures go deep. Deep models are especially good at learning global contextual feature representation with their deep structures. (4) Benefitting from the large learning capacity of deep models, some classical computer vision challenges can be recast as high-dimensional data transform problems and can be solved from new perspectives. Finally, some open questions and future works regarding to deep learning in object recognition, detection, and segmentation will be discussed.

Download or read book Advanced Planning Control and Signal Processing Methods and Applications in Robotic Systems written by Zhan Li and published by Frontiers Media SA. This book was released on 2022-02-22 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Smart Sensors and Devices in Artificial Intelligence written by Dan Zhang and published by MDPI. This book was released on 2021-04-07 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensors are the eyes or/and ears of an intelligent system, such as UAV, AGV and robots. With the development of material, signal processing, and multidisciplinary interactions, more and more smart sensors are proposed and fabricated under increasing demands for homes, the industry, and military fields. Networks of sensors will be able to enhance the ability to obtain huge amounts of information (big data) and improve precision, which also mirrors the developmental tendency of modern sensors. Moreover, artificial intelligence is a novel impetus for sensors and networks, which gets sensors to learn and think and feed more efficient results back. This book includes new research results from academia and industry, on the subject of “Smart Sensors and Networks”, especially sensing technologies utilizing Artificial Intelligence. The topics include: smart sensors biosensors sensor network sensor data fusion artificial intelligence deep learning mechatronics devices for sensors applications of sensors for robotics and mechatronics devices

Download or read book Neural Approaches to Dynamics of Signal Exchanges written by Anna Esposito and published by Springer Nature. This book was released on 2019-09-18 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces, it discusses processing of audio-visual signals for the detection of user-perceived states, the latest scientific discoveries in processing verbal (lexicon, syntax, and pragmatics), auditory (voice, intonation, vocal expressions) and visual signals (gestures, body language, facial expressions), as well as algorithms for detecting communication disorders, remote health-status monitoring, sentiment and affect analysis, social behaviors and engagement. Further, it examines neural and machine learning algorithms for the implementation of advanced telecommunication systems, communication with people with special needs, emotion modulation by computer contents, advanced sensors for tracking changes in real-life and automatic systems, as well as the development of advanced human–computer interfaces. The book does not focus on solving a particular problem, but instead describes the results of research that has positive effects in different fields and applications.

Download or read book Proceedings of 2019 Chinese Intelligent Systems Conference written by Yingmin Jia and published by Springer Nature. This book was released on 2019-09-07 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book showcases new theoretical findings and techniques in the field of intelligent systems and control. It presents in-depth studies on a number of major topics, including: Multi-Agent Systems, Complex Networks, Intelligent Robots, Complex System Theory and Swarm Behavior, Event-Triggered Control and Data-Driven Control, Robust and Adaptive Control, Big Data and Brain Science, Process Control, Intelligent Sensor and Detection Technology, Deep learning and Learning Control, Guidance, Navigation and Control of Aerial Vehicles, and so on. Given its scope, the book will benefit all researchers, engineers, and graduate students who want to learn about cutting-edge advances in intelligent systems, intelligent control, and artificial intelligence.