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Artificial Neural Networks are a widely used computing system implemented for a wide variety of tasks and problems. A common application of such networks is classification problems. However, a significant amount of this research focuses on one and two-dimensional information, such as vectorized data and images. There is limited research performed on three-dimensional media such as video clips. This...
Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Material-based computation is treated as an optimisation...
Cognitive computing - which learns to do useful computational tasks from data, rather than by being programmed explicitly - represents a fundamentally new form of computing. Unfortunately, Deep Neural Networks (DNNs) learn from repeated exposure to huge datasets, which currently requires extensive computation capabilities (such as many GPUs) working together over days or weeks of time. To accelerate...
The availability of intelligent embedded system to assist the classification application is a great challenge in machine learning field in last few decades. Extreme Learning Machine (ELM) is one of the best learning methods for the implementation due to its classification accuracy and speed. The main computational effort of ELM is to compute the pseudo-inverse of hidden layers output. This work presents...
Support Vector Machines (SVMs) are supervised learning models of the machine learning field whose performance strongly depended on its hyperparameters. The Bio-inspired Optimization Tool for SVM (BIOTS) tool is based on a Multi-Objective Particle Swarm Algorithm (MOPSO) to tune hyperparameters of SVMs. In this work, BIOTS is proposed along with a custom hardware design generator (VHDL) that implements...
The stencil pattern is important in many scientific and engineering domains, spurring great interest from researchers and industry. In recent years, various optimizations have been proposed for parallel stencil applications running on GPUs. However, most of the runtime systems that execute those applications often fail to fully utilize the parallelism of modern heterogeneous systems. In this paper,...
This paper considers architecture and functionality of the embedded data acquisition system for automated beehive monitoring. A description of constructed sensor subsystems is given. Proposed solution acquires hive temperature, humidity and weight referring this data to the mobile application via wireless network. The system also performs an analysis of collected bee noises with artificial neural...
This paper proposed an innovative education platform-VREX (Virtual Reality based Education eXpansion), with combination of online and offline, to improve the curriculum building and teaching experience. VREX is based on Virtual Reality (VR) and we believe VR can revolutionize the education ecosystem. With some trials, we found VR can be used to promote curriculum effectiveness in an immersive environment...
How to effectively cultivate students' practical ability and innovative spirit is the subject of Computer Science in Colleges and Universities, especially for the first-year or second-year undergraduate students. This paper introduces the experimental teaching reform trial of the Digital Logic courses, and sums up the experience of how to stimulate students' awareness of innovation in the hardware...
Hardware Trojans (HTs) have been generally inserted at the lower levels of the digital system design and fabrication process, where, due to the high complexity of the hardware model, their detection is more difficult. However, RTL models are becoming more and more complex, making difficult the identification of malicious behaviours also at this level. Unfortunately, only a few verification techniques...
The paper presents the system architecture, development and prototype implementation of a new integrated system for simulation of automated industrial processes using advanced technologies, in accordance with CPS/Industry 4.0 principles. The need to develop such a system is underscored by the interest of the educational stakeholders: students, faculty members, high-level industry partners, for an...
New trends in neural computation, now dealing with distributed learning on pervasive sensor networks and multiple sources of big data, make necessary the use of computationally efficient techniques to be implemented on simple and cheap hardware architectures. In this paper, a nonuniform quantization at the input layer of neural networks is introduced, in order to optimize their implementation on hardware...
A deep learning processor with 8 gated recurrent neural network (RNN) accelerators is proposed in this paper. It features on-chip incremental learning by numerical and local gradient computation enhancement. Extra precision of training is obtained without extending the bit-width. Tri-mode weight access (DMA/FIFO/RAM) improves the throughput during incremental learning. The number multipliers and activation...
Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions. This paper proposes a systematic methodology to address the challenge. It can transform an existing trained, unrestricted NN (usually for software execution substrate) into an equivalent network...
This work presents an embedded hardware architecture for real-time ultrasonic NDE applications that incorporate Hidden Markov Model (HMM) based statistical signal methods. HMM has been successfully used in applications like audio segment retrieval, speech/language recognition and image processing applications. Recently, we proposed a new Hidden Markov Model (HMM) based ultrasonic flaw detection algorithm...
This work presents an embedded hardware architecture for real-time ultrasonic NDE applications that incorporate Hidden Markov Model (HMM) based statistical signal methods. Proposed algorithm is a combination of Discrete Wavelet Transform (DWT) for pre-processing A-scan signals and HMM for classification of the flaw presence. For this study, a MicroZed FPGA with Xilinx Zynq-7020 System-on-Chip (SoC)...
An accurate detection of spectrum opportunities is a key factor in governing the efficient spectrum usage in a cognitive radio (CR) system. Energy detection based spectrum sensing has been widely used due to its ease of implementation with lower computational complexity; however, its robustness and performance are highly affected by the noise uncertainty. In the present work, a real time hardware...
In recent years, Deep Neural Networks (DNNs) have been of special interest in the area of image processing and scene perception. Albeit being effective and accurate, DNNs demand challenging computational resources. Fortunately, dedicated low bitwidth accelerators enable efficient, real-time inference of DNNs. We present an approximate evaluation method and a specialized multiplierless accelerator...
Support Vector Machine (SVM) is a linear binary classifier that requires a kernel function to handle non-linear problems. Most previous SVM implementations for embedded systems in literature were built targeting a certain application; where analyses were done through comparison with software implementations only. The impact of different application datasets towards SVM hardware performance were not...
The aim of this paper is present a comparative study examining the learning experiences of undergraduate students in the power engineering field, which have been designed to improve their employability following graduation. In particular, this paper will focus on using micro-work based learning to overcome threshold concepts. This paper describes an action based approach to look at the level of undergraduate...
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