Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Project Management: IS/IT Research Challenges

A major challenge in Information Systems and Information Technology is to improve the ability to conceptualize, design, develop and deliver information systems that meet customer requirements. Project management is often adopted to create solutions that work and meet customer needs. The principles of project management as defined by the Project Management Institute, can improve project success rates. Researchers in the project management need to help practitioners understanding the impact of different principles on the success of IS development. This study undertakes a survey of project management experts on the state of practice and research to examine the need for improving project management, and suggest areas that can be improved. Research may be the most effective means of defining opportunities for enhancing project success rates by tapping the wealth of literature and complementing it with the expertise of project management practitioners

Summary of a Topical Forum FAQ Based on the Chinese Composition Structure

An automatic multiple-document summarization system for producing frequently asked questions (FAQ) of a topical forum can save forum Webmasters a great deal of time in theory. This work will address summary composition issue of a previous work by proposing a structured presentation based on a four-part pattern of traditional Chinese articles. The result of the experiment shows that the enhanced system with both domain-terminology corpus methods produced a significantly better summary presentation than the original system. Recall rate and precision rate performance indices and user evaluations are also presented and discussed to show their practical implications

TCN AA: A Wi Fi based Temporal Convolution Network for Human to Human Interaction Recognition with Augmentation and Attention

The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequence-to-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.Comment: Published to IEEE Internet of things Journal but haven't been accepted yet (under review

A micromachined flow shear-stress sensor based on thermal transfer principles

Microhot-film shear-stress sensors have been developed by using surface micromachining techniques. The sensor consists of a suspended silicon-nitride diaphragm located on top of a vacuum-sealed cavity. A heating and heat-sensing element, made of polycrystalline silicon material, resides on top of the diaphragm. The underlying vacuum cavity greatly reduces conductive heat loss to the substrate and therefore increases the sensitivity of the sensor. Testing of the sensor has been conducted in a wind tunnel under three operation modes-constant current, constant voltage, and constant temperature. Under the constant-temperature mode, a typical shear-stress sensor exhibits a time constant of 72 μs