373 research outputs found

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Gurus and Media: Sound, image, machine, text and the digital

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    Gurus and Media is the first book dedicated to media and mediation in domains of public guruship and devotion. Illuminating the mediatisation of guruship and the guru-isation of media, it bridges the gap between scholarship on gurus and the disciplines of media and visual culture studies. It investigates guru iconographies in and across various time periods and also the distinctive ways in which diverse gurus engage with and inhabit different forms of media: statuary, games, print publications, photographs, portraiture, films, machines, social media, bodies, words, graffiti, dolls, sound, verse, tombs and more. The book’s interdisciplinary chapters advance, both conceptually and ethnographically, our understanding of the function of media in the dramatic production of guruship, and reflect on the corporate branding of gurus and on mediated guruship as a series of aesthetic traps for the captivation of devotees and others. They show how different media can further enliven the complex plurality of guruship, for instance in instantiating notions of ‘absent-present’ guruship and demonstrating the mutual mediation of gurus, caste and Hindutva. Throughout, the book foregrounds contested visions of the guru in the development of devotional publics and pluriform guruship across time and space. Thinking through the guru’s many media entanglements in a single place, the book contributes new insights to the study of South Asian religions and to the study of mediation more broadly

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Machine Learning Algorithms for Robotic Navigation and Perception and Embedded Implementation Techniques

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Flexible Hardware-based Security-aware Mechanisms and Architectures

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    For decades, software security has been the primary focus in securing our computing platforms. Hardware was always assumed trusted, and inherently served as the foundation, and thus the root of trust, of our systems. This has been further leveraged in developing hardware-based dedicated security extensions and architectures to protect software from attacks exploiting software vulnerabilities such as memory corruption. However, the recent outbreak of microarchitectural attacks has shaken these long-established trust assumptions in hardware entirely, thereby threatening the security of all of our computing platforms and bringing hardware and microarchitectural security under scrutiny. These attacks have undeniably revealed the grave consequences of hardware/microarchitecture security flaws to the entire platform security, and how they can even subvert the security guarantees promised by dedicated security architectures. Furthermore, they shed light on the sophisticated challenges particular to hardware/microarchitectural security; it is more critical (and more challenging) to extensively analyze the hardware for security flaws prior to production, since hardware, unlike software, cannot be patched/updated once fabricated. Hardware cannot reliably serve as the root of trust anymore, unless we develop and adopt new design paradigms where security is proactively addressed and scrutinized across the full stack of our computing platforms, at all hardware design and implementation layers. Furthermore, novel flexible security-aware design mechanisms are required to be incorporated in processor microarchitecture and hardware-assisted security architectures, that can practically address the inherent conflict between performance and security by allowing that the trade-off is configured to adapt to the desired requirements. In this thesis, we investigate the prospects and implications at the intersection of hardware and security that emerge across the full stack of our computing platforms and System-on-Chips (SoCs). On one front, we investigate how we can leverage hardware and its advantages, in contrast to software, to build more efficient and effective security extensions that serve security architectures, e.g., by providing execution attestation and enforcement, to protect the software from attacks exploiting software vulnerabilities. We further propose that they are microarchitecturally configured at runtime to provide different types of security services, thus adapting flexibly to different deployment requirements. On another front, we investigate how we can protect these hardware-assisted security architectures and extensions themselves from microarchitectural and software attacks that exploit design flaws that originate in the hardware, e.g., insecure resource sharing in SoCs. More particularly, we focus in this thesis on cache-based side-channel attacks, where we propose sophisticated cache designs, that fundamentally mitigate these attacks, while still preserving performance by enabling that the performance security trade-off is configured by design. We also investigate how these can be incorporated into flexible and customizable security architectures, thus complementing them to further support a wide spectrum of emerging applications with different performance/security requirements. Lastly, we inspect our computing platforms further beneath the design layer, by scrutinizing how the actual implementation of these mechanisms is yet another potential attack surface. We explore how the security of hardware designs and implementations is currently analyzed prior to fabrication, while shedding light on how state-of-the-art hardware security analysis techniques are fundamentally limited, and the potential for improved and scalable approaches

    The temporal pattern of impulses in primary afferents analogously encodes touch and hearing information

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    An open question in neuroscience is the contribution of temporal relations between individual impulses in primary afferents in conveying sensory information. We investigated this question in touch and hearing, while looking for any shared coding scheme. In both systems, we artificially induced temporally diverse afferent impulse trains and probed the evoked perceptions in human subjects using psychophysical techniques. First, we investigated whether the temporal structure of a fixed number of impulses conveys information about the magnitude of tactile intensity. We found that clustering the impulses into periodic bursts elicited graded increases of intensity as a function of burst impulse count, even though fewer afferents were recruited throughout the longer bursts. The interval between successive bursts of peripheral neural activity (the burst-gap) has been demonstrated in our lab to be the most prominent temporal feature for coding skin vibration frequency, as opposed to either spike rate or periodicity. Given the similarities between tactile and auditory systems, second, we explored the auditory system for an equivalent neural coding strategy. By using brief acoustic pulses, we showed that the burst-gap is a shared temporal code for pitch perception between the modalities. Following this evidence of parallels in temporal frequency processing, we next assessed the perceptual frequency equivalence between the two modalities using auditory and tactile pulse stimuli of simple and complex temporal features in cross-sensory frequency discrimination experiments. Identical temporal stimulation patterns in tactile and auditory afferents produced equivalent perceived frequencies, suggesting an analogous temporal frequency computation mechanism. The new insights into encoding tactile intensity through clustering of fixed charge electric pulses into bursts suggest a novel approach to convey varying contact forces to neural interface users, requiring no modulation of either stimulation current or base pulse frequency. Increasing control of the temporal patterning of pulses in cochlear implant users might improve pitch perception and speech comprehension. The perceptual correspondence between touch and hearing not only suggests the possibility of establishing cross-modal comparison standards for robust psychophysical investigations, but also supports the plausibility of cross-sensory substitution devices

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum
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