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Controlling light with light using coherent metadevices: all-optical transistor, summator and invertor
Although vast amounts of information are conveyed by photons in optical fibers, the majority of data processing is performed electronically, creating the infamous ‘information bottleneck’ and consuming energy at an increasingly unsustainable rate. The potential for photonic devices to directly manipulate light remains unfulfilled due largely to a lack of materials with strong, fast optical nonlinearities. In this paper, we show that small-signal amplifier, summator and invertor functions for optical signals may be realized using a four-port device that exploits the coherent interaction of beams on a planar plasmonic metamaterial, assuming no intrinsic nonlinearity. The redistribution of energy among ports can provide nonlinear input-output signal dependencies and may be coherently controlled at very low intensity levels, with multi-THz bandwidth and without introducing signal distortion, thereby presenting powerful opportunities for novel optical data processing architectures, complexity oracles and the locally coherent networks that are becoming part of the mainstream telecommunications agenda.
Photonic metamaterials: all-optical transistor, summator and inverter
An all-optical device based on a planar plasmonic metamaterial is proposed that has summator, inverter and small-signal amplifier functions. Optical processing of optical data signals is strongly needed to overcome the ‘electronic bottleneck’ in current optical communication systems. Now, researchers at the University of Southampton in the UK and Nanyang Technological University in Singapore have theoretically demonstrated the feasibility of exploiting the coherent interaction of light beams in an ultrathin (substantially subwavelength) plasmonic metamaterial to achieve this. As the proposed device does not use nonlinear optical media, it should be possible to operate it at very low power levels. The energy redistribution between the four ports of the device can provide nonlinear input-output signal dependencies and may be controlled at very low intensity levels with multi-terahertz bandwidth and without distorting the signal.
Journal Article
Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using Python
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You?ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You?ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you?ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.
Book
Crowdsourcing for Speech Processing
The concept of crowdsourcing is based on the observation that if a crowd of non-experts is asked an opinion, the aggregation of their individual opinions will be very close to the true value. Tasks such as collecting speech, labelling it, assessing systems and carrying out studies on the speech data are natural candidates for crowdsourcing. This book is a detailed and hands-on comprehensive reference for those who want to use crowdsourcing for speech applications. From the reader who has already used crowdsourcing and wants to refine their methods to the novice who has never used this technique before; this book will provide a practical introduction to crowdsourcing as a means of rapidly processing speech data with contributions from leading researchers in the field. Informs readers about how to collect and label speech using crowdsourcing; how to assess speech applications and run perception studies using crowdsourcing. Explains to readers about how to choose crowdsourcing platforms. Considers the ethical and legal implications of performing crowdsourcing for speech processing. Includes numerous real-life examples of how to implement crowdsourcing for various types of speech processing. Offers several options for each type of task enabling readers to choose which option best fits their individual needs. Provides an extensive overview of the literature on crowdsourcing for speech processing.
eBook
Robotics, Vision and Control : Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition
Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. The research community has developed a large body of such algorithms but for a newcomer to the field this can be quite daunting. For over 20 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in an accessible but informative style, easy to read and absorb, and includes over 1000 MATLAB and Simulink® examples and over 400 figures. The book is a real walk through the fundamentals of mobile robots, arm robots. then camera models, image processing, feature extraction and multi-view geometry and finally bringing it all together with an extensive discussion of visual servo systems. This second edition is completely revised, updated and extended with coverage of Lie groups, matrix exponentials and twists; inertial navigation; differential drive robots; lattice planners; pose-graph SLAM and map making; restructured material on arm-robot kinematics and dynamics; series-elastic actuators and operational-space control; Lab color spaces; light field cameras; structured light, bundle adjustment and visual odometry; and photometric visual servoing. \"An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished!\" OUSSAMA KHATIB, Stanford.
Book
Multimedia Image and Video Processing
As multimedia applications have become part of contemporary daily life, numerous paradigm-shifting technologies in multimedia processing have emerged over the last decade. Substantially updated with 21 new chapters, this second edition explores the most recent advances in multimedia research and applications. Written by some of the most prominent experts in the field, the book presents a comprehensive treatment of multimedia information mining, security, systems, coding, search, hardware, and communications as well as multimodal information fusion and interaction.
eBook
Techniques for Noise Robustness in Automatic Speech Recognition
Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field.
eBook
Biometric Recognition
Biometric recognition-the automated recognition of individuals based on their behavioral and biological characteristic-is promoted as a way to help identify terrorists, provide better control of access to physical facilities and financial accounts, and increase the efficiency of access to services and their utilization. Biometric recognition has been applied to identification of criminals, patient tracking in medical informatics, and the personalization of social services, among other things. In spite of substantial effort, however, there remain unresolved questions about the effectiveness and management of systems for biometric recognition, as well as the appropriateness and societal impact of their use. Moreover, the general public has been exposed to biometrics largely as high-technology gadgets in spy thrillers or as fear-instilling instruments of state or corporate surveillance in speculative fiction.
Now, as biometric technologies appear poised for broader use, increased concerns about national security and the tracking of individuals as they cross borders have caused passports, visas, and border-crossing records to be linked to biometric data. A focus on fighting insurgencies and terrorism has led to the military deployment of biometric tools to enable recognition of individuals as friend or foe. Commercially, finger-imaging sensors, whose cost and physical size have been reduced, now appear on many laptop personal computers, handheld devices, mobile phones, and other consumer devices.
Biometric Recognition: Challenges and Opportunities addresses the issues surrounding broader implementation of this technology, making two main points: first, biometric recognition systems are incredibly complex, and need to be addressed as such. Second, biometric recognition is an inherently probabilistic endeavor. Consequently, even when the technology and the system in which it is embedded are behaving as designed, there is inevitable uncertainty and risk of error. This book elaborates on these themes in detail to provide policy makers, developers, and researchers a comprehensive assessment of biometric recognition that examines current capabilities, future possibilities, and the role of government in technology and system development.
eBook
Graph classification and clustering based on vector space embedding
This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.
eBook