{"title":"A low phase noise 0.9 / 1.8 GHz dual-band LC VCO in 0.18 μm CMOS technology","authors":"Jinhyun Kim, Jeongsoo Park, Jeong‐Geun Kim","doi":"10.23919/ELINFOCOM.2018.8330717","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330717","url":null,"abstract":"This paper presents a low phase noise dual-band LC voltage controlled oscillator (VCO) in 0.18 ßm CMOS technology. The proposed CMOS LC VCO is realized employing varactor diodes, a switched capacitor array and a switched differential inductor, which operates the dual-band operation. The CMOS LC VCO is also implemented with low phase noise performance using two series inductors at common source nodes. The measured phase noises at 0.9 GHz and 1.8 GHz frequency bands are −135 dBc/Hz and − 126 dBc/Hz at 1 MHz offset. The chip size is 1.3×1.4 mm2, including pads.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122603679","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}
{"title":"Optimizing a FPGA-based neural accelerator for small IoT devices","authors":"Seongmin Hong, Inho Lee, Yongjun Park","doi":"10.1109/isocc.2017.8368903","DOIUrl":"https://doi.org/10.1109/isocc.2017.8368903","url":null,"abstract":"As neural networks have been widely used for machine-learning algorithms such as image recognition, to design efficient neural accelerators has recently become more important. However, designing neural accelerators is generally difficult because of their high memory storage requirement. In this paper, we propose an area-and-power efficient neural accelerator for small IoT devices, using 4-bit fixed-point weights through quantization technique. The proposed neural accelerator is trained through the TensorFlow infrastructure and the weight data is optimized in order to reduce the overhead of high weight memory requirement. Our FPGA-based design achieves 97.44% accuracy with MNIST 10,000 test images.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121740159","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}
{"title":"Development of SISO-based scenarios for modeling and simulation of electronic warfare","authors":"Wooshik Kim, Sangha Choi, Sugjoon Yoon","doi":"10.23919/ELINFOCOM.2018.8330704","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330704","url":null,"abstract":"Scenario is one of the most important things and the very first thing to do in every simulation. This, however, is very problem specific and almost everyone has his own way of preparation, and this has caused lack of interoperability and reusability. SISO, an organization for working on research and standardization of Modeling and Simulation techniques, has proposed 3 steps of preparing scenarios. In this paper, we develop the first two steps of an operational scenario and a conceptual scenario of a simple simulation to check the feasibility of the SISO scenario steps and ultimately to use to develop various scenarios in Electronic Warfare.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123293053","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}
{"title":"A number recognition system with memory optimized convolutional neural network for smart metering devices","authors":"Dasol Han, Hyungwon Kim","doi":"10.23919/ELINFOCOM.2018.8330594","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330594","url":null,"abstract":"This paper presents a number recognition system based on a memory-optimized convolutional neural network for smart metering devices. Smart metering is one of the fastest growing applications for wireless sensor networks. Wireless sensor nodes are in general battery powered, and thus are often constrained by limited memory size and computation power. Due to the memory constraint, general architectures of convolutional neural networks are not suitable for smart metering devices. It is also challenging to recognize the number images of smart metering devices, since the numbers are rolling on mechanical wheels. We propose a memory-optimized architecture of convolutional neural network (MO-CNN) well suited to smart metering devices with a tight memory constraint. We implemented the proposed MO-CNN in a C program and conducted experiments with various rolling number images captured using real water meters. The proposed architecture demonstrate 100% recognition rate under the light condition of 2 ∼ 150 Lux, while it reduces the memory size by 30 times compared with the conventional CNN architecture.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122289923","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}
{"title":"Color-to-grayscale algorithms effect on edge detection — A comparative study","authors":"Ijaz Ahmad, I. Moon, Seokjoo Shin","doi":"10.23919/ELINFOCOM.2018.8330719","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330719","url":null,"abstract":"In image processing, color images are converted into grayscale to perform edge detection, without considering the color-to-grayscale algorithms in details. We have evaluated the impact of various color-to-grayscale algorithms in edge detection. This study shows that edges are not only dependent on the methods used for edge detection but also on the color-to-grayscale conversion algorithms. We have implemented ten different color-to-grayscale conversion algorithms inMATLABR2016a and the resultant grayscale images were further tested with eight different edge detection algorithms. The experimental results shows that the Lightness color-to-grayscale conversion algorithm achieves higher performance among evaluated methods.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132417150","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}
Muhammad Riaz ur Rehman, Hamed Abbasi Zadeh, Imran Ali, Kangyoon Lee
{"title":"LabVIEW based modeling of SWIPT system using BPSK modulation","authors":"Muhammad Riaz ur Rehman, Hamed Abbasi Zadeh, Imran Ali, Kangyoon Lee","doi":"10.23919/ELINFOCOM.2018.8330631","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330631","url":null,"abstract":"This paper presents a simultaneous wireless information and power transfer (SWIPT) system modeling in LabVIEW. Since SWIPT system is very active research topic, a complete system level modeling is indispensable. The LabVIEW based modeling of SWIPT system facilitate the research by providing the system level simulation in an efficient and comprehensive way. Both the SWIPT transmitter and receiver are modeled in LabVIEW environment. The SWIPT transmitter modulates digital data and up-convert to RF carrier frequency at 1 GHz. At SWPIT receiver, RF energy is harvested through RF rectifier in energy harvesting (EH) path. Simultaneously, digital information is recovered through information decoding (ID) path. The BPSK modulation is used for data transfer in SWIPT system. Presented modeling utilizes powerful system design capabilities of LabVIEW which assists in the development and testing of SWIPT system.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132530527","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}
{"title":"Depth image-based object segmentation scheme for improving human action recognition","authors":"Sungjoo Park, U. Park, Dongchil Kim","doi":"10.23919/ELINFOCOM.2018.8330654","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330654","url":null,"abstract":"Human action recognition using the 3D camera for surveillance applications is a promising alternative approach to the conventional 2D camera based surveillance. We propose a depth image-based object segmentation scheme for improving human action recognition. Experimental results show that the average accuracy of the dangerous event detection is improved by about 15% when using the proposed object segmentation scheme.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"608 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132917137","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}
B. Adithya, B. P. Kumar, Hanna Lee, Ji Yeon Kim, Jae Cheol Moon, Y. Chai
{"title":"An experimental study on relationship between foveal range and FoV of a human eye using eye tracking devices","authors":"B. Adithya, B. P. Kumar, Hanna Lee, Ji Yeon Kim, Jae Cheol Moon, Y. Chai","doi":"10.23919/ELINFOCOM.2018.8330605","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330605","url":null,"abstract":"Various methodologies have been scrutinized to model a human eye. Most of them have failed to consider aspects pertaining to free movement of the head and mainly focus on the gaze of a Human Eye. Today's eye trackers offer gaze data with respect to the normalized coordinate system. In this paper, experimental results are presented that infer that the point of gaze of a human eye, highly lies within the foveal view and drifts along the foveal view as the user traces the gaze points on the 2D plane.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129364349","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}
Young-Ha Hwang, Jun-Eun Park, Jiheon Park, D. Jeong
{"title":"A PVT-compensated sinusoidal wave generator with phase modulation for multi-channel sensor applications","authors":"Young-Ha Hwang, Jun-Eun Park, Jiheon Park, D. Jeong","doi":"10.23919/ELINFOCOM.2018.8330646","DOIUrl":"https://doi.org/10.23919/ELINFOCOM.2018.8330646","url":null,"abstract":"This paper presents a PVT-compensated sinusoidal wave generator exploiting phase modulation for multi-channel sensor applications. The sinusoidal wave is generated by a DDFS with a PVT-compensated relaxation oscillator. The sinusoidal wave generator is fabricated in 0.18 μm CMOS technology, occupying an active area of 1.099 mm2 with a power consumption of1.55 mW.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129930051","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}
{"title":"Implementation of EMG data-based rehabilitation assistance system","authors":"Ji-Yun Seo, Yun-Hong Noh, Do-Un Jeong","doi":"10.23919/elinfocom.2018.8330632","DOIUrl":"https://doi.org/10.23919/elinfocom.2018.8330632","url":null,"abstract":"Existing rehabilitation treatment, based on the experience base of experts, to do a lot of treatment and training. However, in this research, we implemented a rehabilitation support system based on data that can support efficient rehabilitation based on more objective data. Implemented system utilizes EMG, acceleration sensor and gyro sensor, it becomes a measurement, so it is possible to accumulate more objective data and plan a treatment when doing rehabilitation treatment. Also, in order to monitor this in real time, implemented a Bluetooth based application monitoring section. In order to evaluate the performance of the implemented system, we measured EMG signals, acceleration sensor signals and gyro sensor signals of 5 test subjects according to various rehabilitation exercise postures and analyzed them.","PeriodicalId":413646,"journal":{"name":"2018 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129132561","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}