Low-latency Cloud-based Volumetric Video Streaming Using Head Motion Prediction

Volumetric video is an emerging key technology for immersive representation of 3D spaces and objects. Rendering volumetric video requires lots of computational power which is challenging especially for mobile devices. To mitigate this, we developed a streaming system that renders a 2D view from the volumetric video at a cloud server and streams a 2D video stream to the client. However, such network-based processing increases the motion-to-photon (M2P) latency due to the additional network and processing delays. In order to compensate the added latency, prediction of the future user pose is necessary. We developed a head motion prediction model and investigated its potential to reduce the M2P latency for different look-ahead times. Our results show that the presented model reduces the rendering errors caused by the M2P latency compared to a baseline system in which no prediction is performed.Comment: 7 pages, 4 figure

Methods to detect and reduce driver stress: a review

Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver’s personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver’s attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver’s awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic

Uso de redes recorrentes para identificação automática de contaminantes e para a estimação de um sensor virtual de eletromiografia no contexto de um sistema tolerante a falhas

O desenvolvimento de sistemas inteligentes controlados por eletromiografia que possam se adaptar a possíveis contaminações extrínsecas e intrínsecas, que afetem a taxa de acerto do classificador de movimentos, leva a dispositivos mais robustos e seguros, vistos que evitariam acionamentos indevidos e inesperados. Esse trabalho apresenta uma solução para contaminações por Artefato de Movimento, Ruído de Linha Elétrica, Ruído Branco Aditivo e ECG em 9 diferentes níveis de SNR, de -40dB a 40dB, utilizando Redes Neurais Recorrentes (RNR) com unidades LSTM nas duas etapas deste trabalho. A primeira etapa é o sistema de identificação da contaminação, que traz como inovação a identificação do contaminante diretamente do sinal bruto de sEMG, deixando para a rede a extração das características temporais, onde os resultados apontaram uma taxa de mais de 90% de acerto do tipo de contaminante para SNR = -30dB. A segunda etapa é a geração de um Sensor Virtual a partir de 7 estudos de caso em falhas de eletrodos, que traz como inovação a regressão do sinal retificado e suavizado por um filtro AVT. A geração do sensor virtual é realizada a partir dos canais não contaminados também utilizando uma RNR - LSTM com o objetivo de recuperar a taxa de acerto em 18 classes de um classificador Extreme Learning Machine (ELM), aplicado nas bases NinaPro e IEE. Os resultados indicaram que foi possível recuperar a taxa média de acerto para 2 canais contaminados com ruído branco aditivo em -30dB, de um total de 12 canais, de 7,28% para 68,34% em 4 indivíduos não amputados e de 15,07% para 43,67% em 9 indivíduos amputados.The development of electromyographic controlled systems adaptable to possibles extrinsic and intrisec contaminations, affecting the movement classification hit rate, lead to more robust and secure devices avoiding unexpected situations. This work presents a solution for Movement Artifact, Electrical Noise, White Gaussian Noise and ECG in nine SNR levels, ranging from -40dB to 40dB in 10dB steps, using Recurrent Neural Networks with LSTM units in the two stages of this work. The first stage is an automatic contamination detector, that has the contaminant identification made direct from the raw sEMG signal as a novelty, where the the tests point to 90% correct identification for SNR = -30dB. The second stage is the development of a virtual sensor, that generates the corrupted channel using the non-corrupted ones using a RNR-LSTM with the objective to recover the 18 movement class classification hit rate for an Extreme Learning Machine (ELM). The results shows that was possible to recovery the classification hit rate for 2 contaminated channels from 7.28% to 63.34% in 4 non-amputee subjects and from 15,07% to 43.67% in 9 amputee subjects

Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date