Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Efficient Hardware Implementation of Deep Learning Networks Based on the Convolutional Neural Network

Image classification, speech processing, autonomous driving, and medical diagnosis have made the adoption of Deep Neural Networks (DNN) mainstream. Many deep networks such as AlexNet, GoogleNet, ResidualNet, MobileNet, YOLOv3 and Transformers have achieved immense success and popularity. However, implementing these deep and complex networks in hardware is a challenging feat. The growing demand of DNN applications in mobile devices and data centers have led the researchers to explore application specific hardware accelerators for DNNs. There have been numerous hardware and software based solutions to improve DNN throughput, latency, performance and accuracy. Any solution for hardware acceleration needs to optimize in a space confined by these metrics. Hardware acceleration of Deep Neural Networks (DNN) is a highly effective and viable solution for running them on mobile devices. The power of DNN is now available at the edge in a compact and power-efficient form factor because of hardware acceleration. In this thesis, we introduce a novel architecture that uses a generalized method called Single Input Partial Product 2-Dimensional Convolution (SIPP2D Convolution) which calculates a 2-D convolution in a fast and expedient manner. We present the exploration designs that have culminated into SIPP2D and emphasize its benefits. SIPP2D architecture prevents the re-fetching of input weights for the calculation of partial products. It can calculate the output of any input size and kernel size with a low memory-traffic while maintaining a low latency and high throughput compared to other popular techniques. In addition to being compatible with any input and kernel size, SIPP2D architecture can be modified to support any allowable stride. We describe the data flow and algorithmic modifications to SIPP2D which extends its capabilities to accommodate multi-stride convolutions. Supporting multi-stride convolutions is an essential feature addition to SIPP2D architecture, increasing its versatility and network agnostic character for convolutional type DNNs. Along with architectural explorations, we have also performed research in the area of model optimization. It is widely understood that any change on the algorithmic level of the network pays significant dividends at the hardware level. Compression and optimization techniques such as pruning and quantization help reduce the size of the model while maintaining the accuracy at an acceptable level. Thus, by combining techniques such as channel pruning with SIPP2D we can only boost its performance. In this thesis, we examine the performance of channel pruned SIPP2D compared to other compressed models. Traditionally, quantization of weights and inputs are used to reduce the memory transfer and power consumption. However, quantizing the outputs of layers can be a challenge since the output of each layer changes with the input. In our research, we use quantization on the output of each layer for AlexNet and VGGNet-16 to analyze the effect it has on accuracy. We use Signal to Noise Quantization Ratio (SQNR) to empirically determine the integer length (IL) as well as the fractional length (FL) for the fixed point precision that can yields the lowest SQNR and highest accuracy. Based on our observations, we can report that accuracy is sensitive to fractional length as well as integer length. For AlexNet, we observe deterioration in accuracy as the word length decreases. The Top -5 accuracy goes from 77% for floating point precision to 56% for a WL of 12 and FL of 8. The results are similar in the case of VGGNet-16. The Top-5 accuracy for VGGNet-16 decreases from 82% for floating point to 30% for a WL of 12 and FL of 8. In addition to the small word length, we observe the accuracy to be highly dependent on the integer length as well as the fractional length. We have also done analysis on the loss after retraining post quantization. We use polynomial fitting to achieve a relationship with fractional length and the drop in accuracy still sustained after retraining a quantized network. In summary, the winning combination of the enhanced SIPP2D architecture and compression techniques such as channel pruning and quantization techniques is highly advantageous and conducive to widespread adoption. SIPP2D architecture, with its flexible data flow and algorithmic modifications to support multi-stride convolutions, offers a powerful and versatile framework for deep neural networks

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage