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EDOA: an efficient delay optimization approach for mixed-polarity Reed-Muller logic circuits under the unit delay model

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Frontiers of Computer Science 2019年第5期13卷 1102-1115页

作者： Zhenxue HE Limin XIAO Fei GU Li RUAN Zhisheng HUO Mingzhe LI Mingfa ZHU Longbing ZHANG Rui LIU Xiang WANG College of Information Science and Technology Hebei Agricultural University Baoding 071001 China State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China School of Computer Science and Engineering Beihang University Beijing 100083 China School of Electronic and Information Engineering Beihang University Beijing 100083 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100083 China

Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay op-timization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is ob-tained using a novel Reed-Muller expression simplification approach (RMESA) considering don't-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark cir-cuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most cir-cuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.

关键词： delay optimization mixed-polarity Reed-Muller logic circuits unit delay model don't-care terms,

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iLGaCo: Incremental learning of gait covariate factors

arXiv

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arXiv 2020年

作者： Mu, Zihao Castro, Francisco M. Marín-Jiménez, Manuel J. Guil, Nicolás Li, Yan-Ran Yu, Shiqi College of Computer Science and Software Engineering Shenzhen University China Department of Computer Architecture University of Málaga Spain Department of Computing and Numerical Analysis University of Córdoba Spain China Department of Computer Science and Engineering Southern University of Science and Technology China

Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using the whole training dataset. In fact, if new data (classes, view-points, walking conditions, etc.) need to be included, it is necessary to re-train again the model with old and new data samples. In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset. Instead, our approach performs a shorter training process with the new data and a small subset of previous samples. This way, our model learns new information while retaining previous knowledge. We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new view-points and adding new walking conditions. In both cases, our results are close to the classical ‘training-from-scratch’ approach, obtaining a marginal drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our approach. In addition, the comparison of iLGaCo with other incremental learning methods, such as LwF and iCarl, shows a significant improvement in accuracy, between 6% and 15% depending on the experiment. Copyright © 2020, The Authors. All rights reserved.

关键词： Gait analysis

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Evaluation and visualisation of Mexican mass housing thermal performance

Evaluation and visualisation of Mexican mass housing thermal...

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作者： Becerra-Santacruz, Habid Patlakas, Panagiotis Altan, Hasim Faculty of Architecture Universidad Michoacana de San Nicolas de Hidalgo Morelia Mexico Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom Architectural Engineering Department College of Engineering University of Sharjah Sharjah United Arab Emirates

Mass industrialised low-income housing is a building typology that is common in Mexico and possesses different characteristics in scale and social impact from other similar developments across the world. The business needs and socioeconomic factors behind these initiatives mean that the environmental performance is typically of low or no importance to developers, who instead are primarily concerned with the speed of erection and profit margins. However, such developments present particular interest to environmental design researchers;therefore, this paper presents some of the results from an extensive post-occupancy evaluation of field surveys conducted over a period of 2 years in Morelia, Mexico. The monitoring phase encompassed 12 dwellings with four different orientations, measuring internal temperature and relative humidity levels at 10min intervals over the two distinct seasons of the local climate. The results are discussed, and conclusions are drawn to help with future development. In addition, an important aspect of this work is the presentation and communication of the collected data for different stakeholders involved in the design, construction and operation phases. The monitoring results are presented not only in the standard graph formats, but also by way of four-dimensional interactive visualisation approaches, relying on a proprietary research software prototype. © 2019 ICE Publishing: All rights reserved.

关键词： Environmental management

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Adsorptive removal of arsenic from drinking water using KOH-modified sewage sludge-derived biochar

Cleaner Water

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Cleaner Water 2024年 2卷

作者： Ahsan Shah Arun Arjunan Anusha Thumma Julia Zakharova Timothy Bolarinwa Shreah Devi Maryam Batool School of Engineering Computing and Mathematical Sciences University of Wolverhampton WV1 1LY UK Department of Pharmaceutical Sciences College of Pharmacy Nova Southeastern University Fort Lauderdale FL USA School of Architecture and Built Environment University of Wolverhampton WV1 1LY UK Faculty of Science and Engineering University of Wolverhampton WV1 1LY UK Department of Chemistry University of Sahiwal Sahiwal Pakistan

This paper reveals a green chemistry approach to remove arsenic (As +3 ) from water using potassium hydroxide (KOH) modified sewage sludge-derived biochar (SSDB-KOH). Characterisation of the morphology, particle size and composition of the SSDB-KOH pre- and post-adsorption confirmed porous and heterogenous surface favouring adsorption. At ambient temperature (20 °C), the SSDB-KOH dose of 20 g/l achieved 98 % arsenite removal at nearly neutral solution pH (8). This compared favourably with pristine SSDB, where the performance was limited to 41.6 % removal. The enhanced arsenite adsorption of SSDB-KOH was informed by several mechanisms, including ionic interactions, pore filling and metal-pi interactions. The experimental data fits closely with the Langmuir isotherm and pseudo-second-order kinetic models, revealing monolayer adsorption and the chemical interaction between adsorbents and the adsorbate. The spontaneous and endothermic nature of the process was confirmed by the negative value of ΔG and the positive value of ΔH, respectively. The performance of the biochar was evaluated for four-cycle regeneration. Finally, a cost analysis was performed to demonstrate the economic feasibility of using SSDB-KOH to remove arsenic from water.

关键词： Adsorption KOH-modified biochar Arsenic Batch study Regeneration Drinking water

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Confidence Estimation Using Machine Learning in Immersive Learning Environments

Confidence Estimation Using Machine Learning in Immersive Le...

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IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Yudong Tao Erik Coltey Tianyi Wang Miguel Alonso Mei-Ling Shyu Shu-Ching Chen Hadi Alhaffar Albert Elias Biayna Bogosian Shahin Vassigh Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA School of Computing and Information Sciences Florida International University Miami FL USA College of Communication Architecture and the Arts Florida International University Miami FL USA

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

As the development of Virtual Reality and Augmented Reality (VR/AR) technology rapidly advances, learning in an artificial immersive environment becomes increasingly feasible. Such emerging technology not only facilitates and promotes an efficient learning process, but also reduces the cost of access to learning materials and environments. Current research mainly focuses on the development of immersive learning environments and the adaptive learning methods based on interactions between trainees and the environment. However, valuable human biometric data available in immersive environments, such as eye gaze and controller pose, have not been explored and utilized to help understand the affective state of the trainees. In this paper, we propose a machine-learning based research framework to estimate trainees' confidence about their decisions in immersive learning environments. Using this framework, we designed an experiment to collect biometric data from a multiple-choice question and answer session in an immersive learning environment. This includes collecting answers from 10 participants on 35 questions and their self-reported confidence in their answers. A Long Short-Term Memory neural network model was used to analyze the data and estimate the confidence with 85.6% accuracy.

关键词： Conferences Information processing Two dimensional displays

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On node ranking in graphs

arXiv

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arXiv 2021年

作者： Dudkina, Ekaterina Bin, Michelangelo Breen, Jane Crisostomi, Emanuele Ferraro, Pietro Kirkland, Steve Marecek, Jakub Murray-Smith, Roderick Parisini, Thomas Stone, Lewi Yilmaz, Serife Shorten, Robert Department of Energy Systems Territory and Constructions Engineering University of Pisa Pisa Italy Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Ontario Tech University Canada Dyson School of Design Engineering Imperial College London London United Kingdom Department of Mathematics University of Manitoba Canada Czech Technical University Prague Czech Republic School of Computing Science University of Glasgow Glasgow United Kingdom Department of Engineering and Architecture University of Trieste Trieste Italy KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus The George S. Wise Faculty of Life Sciences Tel Aviv University Israel Mathematics School of Science RMIT University Melbourne Australia

The ranking of nodes in a network according to their "importance" is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it is related to the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by the COVID-19 spreading dynamics, in this paper we review the most popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network, that takes into account the community-based structure of society. Also, we generalize a classic benchmark network originally proposed by Newman for ranking nodes in unweighted graphs, to show how ranks change in the weighted case. Copyright © 2021, The Authors. All rights reserved.

关键词： Benchmarking

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Torsional vibration solution of tapered pile considering stress diffusion effect of pile end soil 5

Torsional vibration solution of tapered pile considering str...

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2019 5th International Conference on Hydraulic and Civil engineering, ICHCE 2019

作者： Guan, Wenjie Wu, Wenbing Jiang, Guosheng Leo, Chin Jian Liu, Hao Engineering Research Center of Rock-Soil Drilling and Excavation and Protection Ministry of Education Faculty of Engineering China University of Geosciences Wuhan Hubei430074 China Guangxi Key Laboratory of Disaster Prevention and Engineering Safety College of Civil Engineering and Architecture Guangxi University Nanning Guangxi530004 China School of Computing Engineering and Mathematics University of Western Sydney Locked Bag 1797 SydneyNSW2751 Australia

Based on the torsional plane strain model and tapered fictitious soil pile model, the torsional vibration characteristics of tapered pile considering stress diffusion effect of pile end soil is investigated theoretically. Considering the coupling conditions of pile-soil system, the torsional impedance at the tapered pile head is obtained by means of Laplace transform and impedance transfer method. A parametric study is performed, the rationality of the tapered fictitious soil model is verified and the influence of the cone angle of tapered fictitious soil pile on the torsional vibration characteristics of tapered pile embedded in layered soil is studied. The results show that: the cone angle of tapered fictitious soil pile has obvious effect on the torsional vibration characteristics of tapered pile with the shorter length. © Published under licence by IOP Publishing Ltd.

关键词： Piles

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Intelligent Paving and Rolling Construction Technology of Asphalt Pavement

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Journal of Physics: Conference Series 2022年第1期2185卷

作者： Taiping Yuan Zheng Wang Qiaozhang Hong Jing Chen Jiechao Lei Yongjun Meng China Southern Power Grid Energy Development Research Institute College of Civil Engineering and Architecture Guangxi University Nanning People's Republic of China Key Laboratory of Disaster Prevention and Engineering Safety of Ministry of Education Nanning People's Republic of China National High- performance Computing Center Nanning Branch Nanning People's Republic of China

The intelligent construction system of asphalt pavement consists of an intelligent paver, driverless roller, mobile base station, intelligent control program, etc. By installing GPS(BDS)+ GNS satellite receiver, 5G communication chip, microwave communication host, radio signal receiving antenna on the construction equipment and connecting with the processor, switch, program software, and other components set on the equipment, integrate the laser obstacle avoidance radar, infrared temperature detection device, laser ranging sensor and other terminal equipment set on the equipment to realize the intellectualization of equipment use function; By running the intelligent control system, mobile 5G communication base station system and construction area acquisition system, command the linkage operation of multiple intelligent types of equipment to realize the intelligent control of pavement construction and meet the requirements of asphalt pavement design specifications.

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Context-Aware Driver Distraction Severity Classification using LSTM Network

Context-Aware Driver Distraction Severity Classification usi...

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International Conference on computing, Electronics & Communications engineering (iCCECE)

作者： Adebamigbe Fasanmade Suleiman Aliyu Ying He Ali H. Al-Bayatti Mhd Saeed Sharif Ahmed S. Alfakeeh Faculty of Computing Engineering and Media De Montfort University Leicester UK School of Architecture Computing and Engineering College of Arts Technology and Innovation UEL University Way Dockland Campus London UK Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

ISBN: (纸本)9781728121390

Advanced Driving Assistance Systems (ADAS) has been a critical component in vehicles and vital to the safety of vehicle drivers and public road transportation systems. In this paper, we present a deep learning technique that classifies drivers' distraction behaviour using three contextual awareness parameters: speed, manoeuver and event type. Using a video coding taxonomy, we study drivers' distractions based on events information from Regions of Interest (RoI) such as hand gestures, facial orientation and eye gaze estimation. Furthermore, a novel probabilistic (Bayesian) model based on the Long short-term memory (LSTM) network is developed for classifying driver's distraction severity. This paper also proposes the use of frame-based contextual data from the multi-view TeleFOT naturalistic driving study (NDS) data monitoring to classify the severity of driver distractions. Our proposed methodology entails recurrent deep neural network layers trained to predict driver distraction severity from time series data.

关键词： Vehicles Accidents Monitoring Task analysis Safety Visualization

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Predicting the Standard and Deviant Patterns in EEG Signals Based on Deep Learning Model

Predicting the Standard and Deviant Patterns in EEG Signals ...

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International Conference on computing, Electronics & Communications engineering (iCCECE)

作者： Mhd Saeed Sharif Ali H. Al-Bayatti Ahmed S. Alfakeeh School of Architecture Computing and Engineering College of Arts Technology and Innovation UEL University Way Dockland Campus London UK Facultiy of Computing Engineering and Media De montfort University The Gateway Leicester UK Faculty of Computing and Information Technology King Abdulaziz University Jeddah KSA

ISBN: (纸本)9781728121390

In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently, such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds.

关键词： Electroencephalography Principal component analysis Deep learning Feature extraction Brain modeling Wavelet transforms

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