Systematic analysis of the decoding delay on MVC decoders

We present a framework for the analysis of the decoding delay and communication latency in Multiview Video Coding. The application of this framework on MVC decoders allows minimizing the overall delay in immersive video-conference systems

Systematic analysis of the decoding delay in multiview video

We present a framework for the analysis of the decoding delay in multiview video coding (MVC). We show that in real-time applications, an accurate estimation of the decoding delay is essential to achieve a minimum communication latency. As opposed to single-view codecs, the complexity of the multiview prediction structure and the parallel decoding of several views requires a systematic analysis of this decoding delay, which we solve using graph theory and a model of the decoder hardware architecture. Our framework assumes a decoder implementation in general purpose multi-core processors with multi-threading capabilities. For this hardware model, we show that frame processing times depend on the computational load of the decoder and we provide an iterative algorithm to compute jointly frame processing times and decoding delay. Finally, we show that decoding delay analysis can be applied to design decoders with the objective of minimizing the communication latency of the MVC system

Deep Learning-Based Robust Neural-Machine Interface for Dexterous Control of Robotic Hand

Neuromuscular injuries can impair hand function and impact the quality of life. To restore hand dexterity, numerous assistive devices have been developed. However, the lack of a robust neural-machine interface may limit functionality of these devices. Accordingly, a robust neural decoding approach was developed that can continuously decode the intended finger motor output. High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles. Convolutional neural networks were implemented to learn the mapping from HD-EMG features to finger-specific population neuron firing frequency, which was then used to control a prosthetic hand in real-time. In comparison with the HD-EMG amplitude approach, the network-based decoder predicted finger forces and angles with lower prediction errors. The network-based decoder also demonstrated better isolation with minimal predicted output in the unintended fingers. The outcomes offer a novel neural-machine interface technique that allows intuitive control of assistive robotic hands in a dexterous manner.Master of Scienc

Algorithms & implementation of advanced video coding standards

Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

Restoring Fine Motor Prosthetic Hand Control via Peripheral Neural Technology

Losing a limb can drastically alter a person’s way of life, and in some cases, brings great financial and emotional burdens. In particular, upper-limb amputations means losing the ability to do many daily activities that are normally simple with intact hands. Prosthesis technology has significantly advanced in the past decade to replicate the mechanical complexity of the human hand. However, current commercial user-to-prosthesis interfaces fail to provide users with full intuitive control over the many functionalities advanced prosthetic hands can offer. Research in developing new interfaces for better motor control has been on the rise, focusing on tapping directly into the peripheral nervous system. The aim of this work is to characterize and validate the properties of a novel peripheral interface called the Regenerative Peripheral Nerve Interface (RPNI) to improve fine motor skills for prosthetic hand control. The first study characterizes the use of RPNI signals for continuous hand control in non-human primates. In two rhesus macaques, we were able to reconstruct continuous finger movement offline with an average correlation of ρ = 0.87 and root mean squared error (RMSE) of 0.12 between actual and predicted position across both macaques. During real-time control, neural control performance was slightly slower but maintained an average target hit success rate of 96.7% compared to physical hand control. The second study presents the viability of the RPNI in humans who have suffered from upper-limb amputations. Three participants with transradial amputations, P1, P2 and P3, underwent surgical implantation of nine, three, and four RPNIs for the treatment of neuroma pain, respectively. In P1 and P2, ultrasound demonstrated strong contractions of P1 and P2’s median RPNIs during flexion of the phantom thumb, and of P1’s ulnar RPNIs during small finger flexion. In P1, the median RPNI and ulnar RPNIs produced electromyography (EMG) signals with a signal-to-noise ratio (SNR) of 4.62 and 3.80 averaged across three recording sessions, respectively. In P2, the median RPNI and ulnar RPNI had an average SNR of 107 and 35.9, respectively, while P3’s median RPNI and ulnar RPNIs had an average SNR of 22.3 and 19.4, respectively. The final study characterizes the capabilities of RPNI signals to predict continuous finger position in human subjects. Two participants, P2 and P3, successfully hit targets during a center-out target task with 92.4 ± 2.3% accuracy, controlling RPNI-driven one DOF finger movements. Comparably, non-RPNI driven finger movement had similar accuracy and performance. Without recalibrating parameter coefficients, no decreasing trend in motor performance was seen for all one DOF finger control across 300 days for P2 and 40 days for P3, suggesting that RPNIs can generate robust control signals from day to day. Lastly, using RPNI-driven control, P2 and P3 successfully manipulated a two DOF virtual and physical thumb with 96.4 ± 2.5% accuracy. These three studies demonstrated: (1) RPNIs provided robust continuous control of one DOF hand movement in non-human primates, an important step for human translation, (2) RPNIs were safely implemented in three participants, showing evidence of contraction and generation of EMG, and (3) in two participants, RPNIs can provide continuous control of one DOF finger movements and two DOF thumb movements. The results presented in this dissertation suggest RPNIs may be a viable option to advance peripheral nerve interfaces into clinical reality and enhance neuroprosthetic technology for people with limb loss.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149816/1/philipv_1.pd