2020 10th Annual Computing and Communication Workshop and Conference (CCWC)最新文献_第3页

Connected Home Automated Security Monitor (CHASM): Protecting IoT Through Application of Machine Learning 互联家庭自动安全监视器(CHASM):通过应用机器学习保护物联网

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031162

Jeffrey S. Chavis, A. Buczak, A. Rubin, Lanier A Watkins

引用次数: 2

Prediction of Cancer Drug Effectiveness Based on Multi-Fusion Deep Learning Model 基于多融合深度学习模型的癌症药物有效性预测

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031163

Qian Li, Jie Huang, Hongming Zhu, Qin Liu

{"title":"Prediction of Cancer Drug Effectiveness Based on Multi-Fusion Deep Learning Model","authors":"Qian Li, Jie Huang, Hongming Zhu, Qin Liu","doi":"10.1109/CCWC47524.2020.9031163","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031163","url":null,"abstract":"Oncogenomics is the premise of precision medicine, which has attracted the attention and research of many scholars in recent years. Among them, predicting the response of cell lines to drugs is a very important topic. Because understanding the response of cell lines to drugs can not only save a lot of time in drug screening, but also promote the reuse of existing drugs that have been approved by the Food and Drug Administration(FDA) and other regulatory agencies. Herein, this paper proposed a new multi-fusion neural network model. In this model, we first use the Convolutional Neural Network(CNN) to capture the gene expression features of the cell line and the molecular descriptor features of the drug, and then use the resulting abstract features as the input data of the long and short-term memory neural network(LSTM) for drug response prediction. By comparison with some traditional machine learning algorithms and CNN model, we found that our model can improve the prediction accuracy. In addition, compared with previous works on modeling a single drug or a single cancer type, in the design of this model, we extended the application categories to human tissues, where a tissue consists of multiple TCGA types. This paper provides a new method for drug response prediction and provides some guidance for the screening of effective anti-cancer drug.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121885566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Pulse on Pulse Deinterleaving Radar Algorithm 脉冲对脉冲脱交错雷达算法

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031148

Alex Erdogan, Jaron Lin, Jordan Juliano, K. George

引用次数: 9

Facial Expression Recognition with Convolutional Neural Networks 基于卷积神经网络的面部表情识别

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031283

Shekhar Singh, Fatma Nasoz

{"title":"Facial Expression Recognition with Convolutional Neural Networks","authors":"Shekhar Singh, Fatma Nasoz","doi":"10.1109/CCWC47524.2020.9031283","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031283","url":null,"abstract":"Emotions are a powerful tool in communication and one way that humans show their emotions is through their facial expressions. One of the challenging and powerful tasks in social communications is facial expression recognition, as in non-verbal communication, facial expressions are key. In the field of Artificial Intelligence, Facial Expression Recognition (FER) is an active research area, with several recent studies using Convolutional Neural Networks (CNNs). In this paper, we demonstrate the classification of FER based on static images, using CNNs, without requiring any pre-processing or feature extraction tasks. The paper also illustrates techniques to improve future accuracy in this area by using pre-processing, which includes face detection and illumination correction. Feature extraction is used to extract the most prominent parts of the face, including the jaw, mouth, eyes, nose, and eyebrows. Furthermore, we also discuss the literature review and present our CNN architecture, and the challenges of using max-pooling and dropout, which eventually aided in better performance. We obtained a test accuracy of 61.7% on FER2013 in a seven-classes classification task compared to 75.2% in state-of-the-art classification.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117211624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Design and Implementation of Cadastral Geo-spatial IoT Network Gateway Analyzer for Urban Scale Infrastructure Health Monitoring 城市规模基础设施健康监测地籍地理空间物联网网关分析仪的设计与实现

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031188

H. Tariq, Abderrazak Abdaoui, F. Touati, M. Al-Hitmi, D. Crescini, A. B. Mnaouer

引用次数: 2

Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment 挤压和激励:用于硬件部署的高效深度神经网络

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031119

R. T. N. Chappa, M. El-Sharkawy

{"title":"Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment","authors":"R. T. N. Chappa, M. El-Sharkawy","doi":"10.1109/CCWC47524.2020.9031119","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031119","url":null,"abstract":"Convolution neural network is being used in field of autonomous driving vehicles or driver assistance systems (ADAS), and has achieved great success. Before the convolution neural network, traditional machine learning algorithms helped the driver assistance systems. Currently, there is a great exploration being done in architectures like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN architectures and made it more suitable to implement on real-time embedded systems. This paper proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures. The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy. Further, explores the bottleneck module and squeezenext basic block structure. The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture. The proposed architecture is further trained on the CIFAR-10 dataset from scratch. All the training and testing results are visualized with live loss and accuracy graphs. Focus of this paper is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed. Having a model size of 0.595MB along with accuracy of 92.60% and with a satisfactory training and validating speed of 9 seconds, this model can be deployed on real-time autonomous system platform such as Bluebox 2.0 by NXP.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129687884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Integer vs. Floating-Point Processing on Modern FPGA Technology 现代FPGA技术上的整数与浮点处理

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031118

D. L. N. Hettiarachchi, Venkata Salini Priyamvada Davuluru, E. Balster

{"title":"Integer vs. Floating-Point Processing on Modern FPGA Technology","authors":"D. L. N. Hettiarachchi, Venkata Salini Priyamvada Davuluru, E. Balster","doi":"10.1109/CCWC47524.2020.9031118","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031118","url":null,"abstract":"Historically, FPGA designers have used integer processing whenever possible because floating-point processing was prohibitively costly due to higher logic requirements and speed reduction. Therefore, fixed-point processing was the norm. Recently, Intel introduced the Arria 10 FPGA which is the industry's first FPGA that includes single-precision hardened Floating-Point Units (FPUs) on DSP blocks. With the advent of hardened floating-point, FPGA designers have largely abandoned fixed-point processing. This paper introduces a series of arithmetic tests to evaluate whether fixed-point processing is obsolete considering the FPGA performance. A performance metric is developed to calculate the FPGA performance in terms of logic utilization and kernel speed. All programs are tested with Intel Stratix V FPGA which does not have hardened FPUs and Intel Arria 10 FPGA for comparison. The performance metric indicates that, on average, there is a 20.18% performance increase when Stratix V processes fixed-point operations and 27.17% performance increase when Arria 10 processes fixed-point operations. Even with hardened FPUs, it is shown that the Arria 10 FPGA exhibits a significant logic reduction when processing fixed-point operations. The results clearly indicate that the FPGAs perform better when processing converted fixed-point arithmetic operations compared to floating-point arithmetic regardless of whether they include hardened FPUs.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128298388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

CSI Feedback Overhead Reduction for 5G Massive MIMO Systems 5G大规模MIMO系统的CSI反馈开销降低

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031236

Ahmed Hindy, U. Mittal, T. Brown

{"title":"CSI Feedback Overhead Reduction for 5G Massive MIMO Systems","authors":"Ahmed Hindy, U. Mittal, T. Brown","doi":"10.1109/CCWC47524.2020.9031236","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031236","url":null,"abstract":"Enhancing the throughput of multi-user (MU) massive multiple-input multiple-output (MIMO) networks is one of the biggest promises that the fifth generation (5G) networks are expected to deliver. In the Third Generation Partnership Project (3GPP) New Radio (NR) standardization efforts, downlink precoding designs that balance performance and uplink feedback overhead are being investigated. Most recently, a high-resolution precoder (Type-II codebook) was specified for downlink NR Release (Rel.) 15 wherein the channel state information (CSI) feedback is compressed in the spatial domain via exploiting a Discrete Fourier Transform (DFT)-based codebook structure. An extension of the Type-II codebook for NR Rel. 16 which also exploits frequency correlation to reduce CSI feedback overhead is currently under study. In this paper, an overview of some of the recent developments for Rel. 16 Type-II codebook is provided. In addition, a practical approach is proposed that uses multi-stage quantization of codebook parameters with variable quantization resolution, where the resolution is proportional to the coefficients' amplitude values. This approach helps provide better utilization of the CSI feedback, compared with the case with the same quantization resolution for all coefficients. System-level simulation results are provided which show that the proposed approach significantly reduces the CSI feedback overhead without notable impact on performance.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129596612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Optical Communication and Positioning Method of Underwater Observation Apparatus for Environmental Monitor 环境监测水下观测装置的光通信与定位方法

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031130

Eriko Enomoto, Takahiro Kohno, Jun Yamada, M. Shimojo, S. Matsuo, U. Rajagopalan, So Yoon Lee, Xiaobin Zhang, Y. Koike, Hideki Yokoi, Hiroyuki Nishikawa, S. Nagasawa, N. Futai

{"title":"Optical Communication and Positioning Method of Underwater Observation Apparatus for Environmental Monitor","authors":"Eriko Enomoto, Takahiro Kohno, Jun Yamada, M. Shimojo, S. Matsuo, U. Rajagopalan, So Yoon Lee, Xiaobin Zhang, Y. Koike, Hideki Yokoi, Hiroyuki Nishikawa, S. Nagasawa, N. Futai","doi":"10.1109/CCWC47524.2020.9031130","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031130","url":null,"abstract":"This paper proposes an optical communication and positioning method of underwater observation apparatus for environmental monitor using distance image sensor. The glass ball underwater observation apparatus is expected to realize the low cost monitoring system. We tried to apply the distance image sensor by CMOS to ranging in underwater environment by remodeling a retailed sensor unit. In the distance image sensor by CMOS on land application, both the emitter unit and the receiver unit are installed in a same board. However, the emitter unit and the receiver have to be installed separately in the case that the ranging is employed to measure the distance between the emitter on the sea surface and the receiver inside the glass ball. In the separate setting of the distance image sensor by CMOS, the accuracy of the measurement result is investigated. Additionally, influence of both the receiver unit angle relative to the emitter unit and the existence of the glass ball is also examined on the distance position. Finally, it is found that the proposed CMOS image sensor installation is valid highly in the underwater application.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127443134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segregating Hazardous Waste Using Deep Neural Networks in Real-Time Video 基于深度神经网络的危险废物实时视频分离

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031194

Dorothy Hua, Julia Gao, R. Mayo, A. Smedley, Piyush Puranik, J. Zhan

引用次数: 3