Abstract

Abstract
Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Abstract

Abstract

Abstract
FPGAs are becoming a popular solution for accelerating the execution of software applications. The use of high level synthesis (HLS) tools intends to provide levels of abstraction comfortable to software developers when targeting FPGA-based hardware accelerators. However, the need to restructure the software code and to use adequate directives require both mastering the HLS tool used and FPGA hardware. This paper presents our efforts to provide a new approach for code restructuring, intended to help software developers in achieving efficient hardware implementations. Our approach uses an unfolded graph representation, which is generated from program execution traces, together with graph-based optimizations such as folding to generate suitable C code to input to HLS tools, such as Vivado HLS. The experiments show that our approach is capable of generating C code that results in efficient hardware implementations only otherwise achievable using manual restructuring of the input software code and manual insertion of adequate directives. © VDE VERLAG GMBH · Berlin · Offenbach

Abstract
MATLAB is a software based analysis environment that supports a high-level programing language and is widely used to model and analyze systems in various domains of engineering and sciences. Traditionally, the analysis of MATLAB models is done using simulation and debugging/testing frameworks. These methods provide limited coverage due to their inherent incompleteness. Formal verification can overcome these limitations, but developing the formal models of the underlying MATLAB models is a very challenging and time-consuming task, especially in the case of higher-order-logic models. To facilitate this process, we present a library of higher-order-logic functions corresponding to the commonly used matrix functions of MATLAB as well as a translator that allows automatic conversion of MATLAB models to higher-order logic. The formal models can then be formally verified in an interactive theorem prover. For illustrating the usefulness of the proposed library and approach, we present the formal analysis of a Finite Impulse Response (FIR) filter, which is quite commonly used in digital signal processing applications, within the sound core of the HOL Light theorem prover.