2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)最新文献

ICCE-Berlin 2020 Technical Program Committee ICCE-Berlin 2020技术计划委员会

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352173

E. Grinenko, Wenlin Han, E. Hanada, L. Morra, Paolo Napoletano, Bogdan Pavković

引用次数: 0

The Use of Artificial Neural Networks for Predicting Response of Frequency Selective Surfaces 利用人工神经网络预测频率选择曲面的响应

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352189

R. Arya, Rahul Sawlani, Abhinav Gola, Animesh, Hulusi Açikgöz, Konark Sharma, M. Tripathy

引用次数: 0

Detecting DDoS Attacks Near The Edge with Router Canaries 利用路由器金丝雀检测边缘附近的DDoS攻击

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352164

Winston Howard, M. Borowczak

{"title":"Detecting DDoS Attacks Near The Edge with Router Canaries","authors":"Winston Howard, M. Borowczak","doi":"10.1109/ICCE-Berlin50680.2020.9352164","DOIUrl":"https://doi.org/10.1109/ICCE-Berlin50680.2020.9352164","url":null,"abstract":"As consumers place more devices within their local networks the ability to detect and disrupt Distributed Denial of Service (DDoS) attacks must move closer to the edge in order to provide resilient and effective decentralized protection. To move detection from centralized entities towards the edge a distributed technique to detect DDoS attacks through the use of entropy-based canaries located near edge devices (e.g., switches, and routers) is proposed. The benefit of this approach is that a set of infrastructure devices could prevent attacks using hijacked devices from ever leaving local networks. In order to evaluate this approach an open-source Python software package was built on top of the Common Open Research Emulator (CORE) in order to simulate and assess these entropy-based canaries. This distributed entropy-based detection technique, based on prior centralized entropy-techniques, is able to achieve 100% detection rate even when attacker-node comprise only 25% of the total nodes. While these distributed entropy-based canaries can rapidly detect simulated DDoS attacks with high accuracy these preliminary results motivate future investigation using more diverse typologies and real-world data.","PeriodicalId":438631,"journal":{"name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692740","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: 1

Traffic Sign Detection Using YOLOv3 使用YOLOv3进行交通标志检测

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352180

David Mijic, Matteo Brisinello, M. Vranješ, R. Grbić

{"title":"Traffic Sign Detection Using YOLOv3","authors":"David Mijic, Matteo Brisinello, M. Vranješ, R. Grbić","doi":"10.1109/ICCE-Berlin50680.2020.9352180","DOIUrl":"https://doi.org/10.1109/ICCE-Berlin50680.2020.9352180","url":null,"abstract":"Advanced driving assistance systems (ADASs) are increasingly being installed in modern vehicles because they make driving safer and more comfortable. With the implementation of cameras in the vehicle, the range of possible ADASs increases. One of such systems is the one aimed for traffic sign recognition, which alerts the driver about different road conditions such as excess of the speed limit or traffic ban. In this paper, a solution for detecting a specific set of 11 traffic signs typical for most European countries is presented. The algorithm used for detecting traffic signs is You Only Look Once (YOLO) v3, where the model parameters are trained on a train set acquired from the newly created dataset. The rest of the dataset images are used for creating a test set. The dataset is derived from the video signals that were capturing traffic with a front view camera mounted inside the vehicle, in the city of Osijek in different weather conditions (sunny, cloudy, rain, night). The dataset images are extracted from 28 different video sequences, which resulted in 5567 images with the total number of 6751 annotated traffic signs. The proposed solution for detecting a specific set of traffic signs achieves high performance when tested on the test set created from the proposed dataset.","PeriodicalId":438631,"journal":{"name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126334364","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: 6

Clustering Of Classroom Computers For Academic Research 用于学术研究的教室计算机聚类

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352160

Irisann-Maria Agius, Frankie Inguanez

引用次数: 0

Automated Hardware and Neural Network Architecture co-design of FPGA accelerators using multi-objective Neural Architecture Search 基于多目标神经网络结构搜索的FPGA加速器自动化硬件和神经网络结构协同设计

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352153

Philip Colangelo, Oren Segal, Alexander Speicher, M. Margala

{"title":"Automated Hardware and Neural Network Architecture co-design of FPGA accelerators using multi-objective Neural Architecture Search","authors":"Philip Colangelo, Oren Segal, Alexander Speicher, M. Margala","doi":"10.1109/ICCE-Berlin50680.2020.9352153","DOIUrl":"https://doi.org/10.1109/ICCE-Berlin50680.2020.9352153","url":null,"abstract":"State-of-the-art Neural Network Architectures (NNAs) are challenging to design and implement efficiently in hardware. In the past couple of years, this has led to an explosion in research and development of automatic Neural Architecture Search (NAS) tools. AutoML tools are now used to achieve state of the art NNA designs and attempt to optimize for hardware usage and design. Much of the recent research in the auto-design of NNAs has focused on convolution networks and image recognition, ignoring the fact that a significant part of the workload in data centers is general-purpose deep neural networks. In this work, we develop and test a general multilayer perceptron (MLP) flow that can take arbitrary datasets as input and automatically produce optimized NNAs and hardware designs. We test the flow on six benchmarks. Our results show we exceed the performance of currently published MLP accuracy results and are competitive with non-MLP based results. We compare general and common GPU architectures with our scalable FPGA design and show we can achieve higher efficiency and higher throughput (outputs per second) for the majority of datasets. Further insights into the design space for both accurate networks and high performing hardware shows the power of co-design by correlating accuracy versus throughput, network size versus accuracy, and scaling to high-performance devices.","PeriodicalId":438631,"journal":{"name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133206550","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

Soft-input decoding of concatenated codes based on the Plotkin construction and BCH component codes 基于Plotkin结构和BCH分量码的级联码软输入译码

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352179

Daniel Nicolas Bailon, Johann-Philipp Thiers, J. Freudenberger

引用次数: 0

Bit-Labeling and Page Capacities of TLC Non-Volatile Flash Memories TLC非易失性闪存的位标记和页容量

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352190

Johann-Philipp Thiers, Daniel Nicolas Bailon, J. Freudenberger

引用次数: 4

Extensible Augmented Reality Assisted Contact-Free Patient Surveillance in Emergency Context 在紧急情况下可扩展增强现实辅助无接触病人监测

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352178

Robert Roth, Thomas Pursche, Dena Farooghi, Reinhard Möller

引用次数: 0

ABCD: A Compact Object Detector Based on Channel Quantization and Tensor Decomposition ABCD:一种基于信道量化和张量分解的紧凑目标检测器

2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin) Pub Date : 2020-11-09 DOI: 10.1109/ICCE-Berlin50680.2020.9352200

Bingyi Zhang, Peining Zhen, Junyan Yang, Saisai Niu, Hang Yi, Hai-Bao Chen

{"title":"ABCD: A Compact Object Detector Based on Channel Quantization and Tensor Decomposition","authors":"Bingyi Zhang, Peining Zhen, Junyan Yang, Saisai Niu, Hang Yi, Hai-Bao Chen","doi":"10.1109/ICCE-Berlin50680.2020.9352200","DOIUrl":"https://doi.org/10.1109/ICCE-Berlin50680.2020.9352200","url":null,"abstract":"Object detection and tracking are critical computer vision tasks because of the broad needs in society; however, deep neural network-based methods cost many computational resources that hinder them from real scene applications. Quantization is a widely adopted technique to reduce the storage space and memory footprint which makes deep learning models more energy-efficient and resource-friendly. Traditional network quantization methods directly quantize neural networks layer-wise, which means the parameters in different channels take the same quantization range. In this paper, we propose a low-bit learning method for convolutional neural network object detector quantization. Different from previous methods, we quantize the detector channel-wisely to avoid accuracy loss in the low-bit framework. We use progressive quantization, progressive batch normalization fusion, and cut the unnecessary long-tail weights and activations to reduce quantization loss. Moreover, based on the object detector and long short-term memory network (LSTM), we develop a high-performance tracking system. We leverage the tensor decomposition to compress weights in LSTM for getting a higher compression ratio. Experiments are conducted on public datasets and our infrared aerial dataset for object detection and tracking. The experimental results show that our approach obtains better performance compared with the state-of-the-art methods in terms of accuracy and compression ratio.","PeriodicalId":438631,"journal":{"name":"2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125792339","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