2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献

Design and Implementation of Efficient RGB to Gray scale Converter Architectures Using Reversible Logic 基于可逆逻辑的高效RGB到灰度转换器架构的设计与实现

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278066

Swathi. U, Smitha. U

{"title":"Design and Implementation of Efficient RGB to Gray scale Converter Architectures Using Reversible Logic","authors":"Swathi. U, Smitha. U","doi":"10.1109/DISCOVER50404.2020.9278066","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278066","url":null,"abstract":"Reversible logic is considered to be a major promising alternative to traditional digital logic circuits. It eliminates the possibility of information loss and makes the system run in the backward direction there by allowing the recovery of the inputs from the generated outputs. Converting an image from RGB to gray scale is crucial for majority of the image and video processing applications and also various computer vision applications as they reduce the computational complexity, storage space and increase the system speed. In this paper we have proposed three efficient reversible logic based schemes for the implementation of the color space converter units. We have implemented the RGB to gray scale converter units using three different techniques namely average method, desaturation and luminosity methods. The implementation of the proposed design and its functionality verification was done on XILINX 14.7 ISE and using Verilog HDL. The proposed design finds applications in many of the fields including image processing, video processing and high speed low power digital signal processing applications as well as computer vision applications like face & gesture recognition, object detection, traffic monitoring systems and surveillance systems.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126352604","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

Risk Stratification of Neonates using Machine Learning Techniques 使用机器学习技术对新生儿进行风险分层

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278097

R. Shirwaikar, D. U, Tanvi Parate, Leslie Edward Simon Lewis

{"title":"Risk Stratification of Neonates using Machine Learning Techniques","authors":"R. Shirwaikar, D. U, Tanvi Parate, Leslie Edward Simon Lewis","doi":"10.1109/DISCOVER50404.2020.9278097","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278097","url":null,"abstract":"The process of classifying newly born babies into high-risk and low-risk is called risk stratification. Having a platform to stratify neonates according to severity of risk is the key to the success of any Neonatal Intensive Care Unit (NICU). The premature neonates are at a higher risk of developing the disabilities which could affect their future growth. However, the extent at which this can affect their entire life, strongly depends on how early they were born, the quality of care they received during and around birth and the days they follow in NICU. Establishing a decision support tool using machine learning algorithms will be useful for identifying neonates who are at high risk for proper diagnosis and efficient monitoring of neonates at NICU. The paper is focused on risk stratification of neonates using machine leaning algorithms such as Artificial Neural Network (ANN), K Nearest Neighbors (KNN) and Support Vector Machine (SVM). Furthermore, various evaluation parameters were used for comparing the results of the algorithms on the 66 cases of neonates admitted at Kasturba Medical College, Manipal. Based on Area Under Curve (AUC), ANN (0.91) performed better than KNN (0.83) and SVM (0.84). The result indicates the significant contribution of ANN with improved performance in identifying neonates who are at high risk better than other algorithms","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125700637","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

Smart assistive device for senior citizens 长者智能辅助装置

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278083

Seema S Kanagond, Supriya S Huddar, Sushma H Hoolageri, Vinutha Kapatakar, R. Itagi, Soumya Jolad

引用次数: 1

MEC S-box based PRESENT Lightweight Cipher for Enhanced Security and Throughput 基于MEC S-box的PRESENT轻量级密码，增强安全性和吞吐量

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278038

Premananda B.S., Nikhil K.J, Nirmal Jain C.M

{"title":"MEC S-box based PRESENT Lightweight Cipher for Enhanced Security and Throughput","authors":"Premananda B.S., Nikhil K.J, Nirmal Jain C.M","doi":"10.1109/DISCOVER50404.2020.9278038","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278038","url":null,"abstract":"Cyber Physical Systems (CPS) is essential for the integration of the physical world with the virtual electronic world. The only way to provide security to these constrained environment applications is through Lightweight Cryptography. To provide stronger security, with elevated performance and less power consumption, two PRESENT architectures are proposed in this paper. The first architecture provides the option to choose one of the three MEC S-boxes, whereas the second architecture makes use of a single MEC S-box to provide better security and increased performance for encryption and decryption processes. The Standard S-box is replaced with MEC S-box, which has its own advantages, such as, less power consumption, linear time and constant space complexity. To analyse the various parameters of the proposed architectures, they are synthesized using Xilinx Virtex-7 FPGA, in Xilinx Vivado IDE. With respect to Strict Avalanche Criterion (SAC), the standard usage of S-box, nearly gives 50% SAC whereas, at least two orders of MEC S-boxes used in the proposed architectures give more than 50% SAC for the entire algorithm, thereby increasing the security of the whole process. The results depict that the proposed architecture provides a throughput of 1564.02 Mbps whereas with a lesser power consumption.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124221681","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

Transmission of radiology images over an Unsecure Network Using Hybrid Encryption Schemes 使用混合加密方案在不安全网络上传输放射学图像

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278052

Rahul N, M. N, Manuel Manuel, Rajendra Kurady

{"title":"Transmission of radiology images over an Unsecure Network Using Hybrid Encryption Schemes","authors":"Rahul N, M. N, Manuel Manuel, Rajendra Kurady","doi":"10.1109/DISCOVER50404.2020.9278052","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278052","url":null,"abstract":"In this paper., we propose a hybrid encryption scheme to transmit the medical image dataset securely in radiology networks. The proposed methodology uses the RSA (Rivest-Shamir-Adleman) encryption technique., XOR technique, and the digitally reconstructed Radiograph (DRR) image from the 3D volume of MRI scan images. As a first step, the volume of interest (VOI) was segmented and then computed the DRR image on the segmented volume in the sagittal direction. The pixels of the DRR image was XORed with all the image slices. All the images and the DRR image were encrypted separately using the RSA technique and transmitted. At the receiver, the XOR was applied to all the received images, the original slices were retained, VOI was segmented again, and the DRR was recomputed. Now, the received DRR and the recomputed DRR were compared for the changes in the image content through histogram comparison, MSE, and Mean absolute deviation. The data integrity violation was tested by adding an image, deleting an image, and modifying the pixels of the image before sending it. The method was applied to fifty (n=50) samples. In all the above test cases performed, the method identified the data integrity violation correctly.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129845857","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

Smart Complaint Redressal System Using Ethereum Blockchain 使用以太坊区块链的智能投诉补救系统

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278122

Suhani Jattan, Vineet Kumar, Akhilesh R, Rachith R Naik, Sneha N S

引用次数: 4

Homing of a Robot 机器人的导航

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278080

Anusheel Goswami, R. Kashyap, Tinkumoni Kalita, Tabassum Rasul, Koena Mukherjee

引用次数: 0

Matrix based Error Detection and Correction using Minimal Parity Bits for Memories 基于矩阵的存储器最小奇偶校验错误检测与校正

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278030

Konda Nandan Kumar, N. A. Reddy, Peela Shanmukh, M. Vinodhini

{"title":"Matrix based Error Detection and Correction using Minimal Parity Bits for Memories","authors":"Konda Nandan Kumar, N. A. Reddy, Peela Shanmukh, M. Vinodhini","doi":"10.1109/DISCOVER50404.2020.9278030","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278030","url":null,"abstract":"Advancement in Complementary Metal Oxide Semiconductor (CMOS) technology causes Multiple Cell Upsets (MCUs). Due to the radiation particles, MCUs had been a challenging issue for data storage in memory for different applications. One of the techniques which is more often used to protect memories is Error Correction Code, because of their low complexity encoding and decoding. Generally, MCUs affect adjacent bits stored in the memory. Therefore, the technique which would detect and correct the adjacent bits as many as possible would be a productive technique. The only drawback with Matrix based code is the required number of parity bits which is used to support error correction in memories is very high. To resolve the drawback, we worked to minimise the number of parity bits in this paper. The proposed technique has equal error correction capability with smaller count of parity bits as compared to other existing techniques. Total parity bits has been reduced by 30% and area, power and delay time has been reduced by 1.12%, 33.59%, 55.46% respectively. These parameters make the proposed technique an efficient and productive one for protecting memories. This technique can be used in applications which have very strict constraints Of parity bits and speed.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127756606","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

Analysis of Agricultural Supply Chain Management for Traceability of Food Products using Blockchain-Ethereum Technology 基于区块链-以太坊技术的农产品可追溯性供应链管理分析

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278029

P. S., Meeradevi, Monica R. Mundada

{"title":"Analysis of Agricultural Supply Chain Management for Traceability of Food Products using Blockchain-Ethereum Technology","authors":"P. S., Meeradevi, Monica R. Mundada","doi":"10.1109/DISCOVER50404.2020.9278029","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278029","url":null,"abstract":"The current agricultural supply chain is a centralized system that has many issues related to integrity, tracking, organizing the transactions. There is lack of trust and transparency in the supply chain. There are many irregularities in the implementation of schemes. The consumers doubt the quality of the food supplied to them. The farmers, wholesalers are affected from the payment frauds by the middlemen. Blockchain helps us solve these issues by keeping track of all the different stages of food. The public blockchain being transparent, open, immutable, trackable helps in reducing the irregularities, frauds. This paper proposes the agricultural supply chain system model that uses the Ethereum platform. The smart contracts are developed for different stages of the supply chain. These contracts ensure that all the pre-decided conditions are satisfied before proceeding with the transactions. This system ensures security, reliability, trust, openness. It eases the transactions, administrative processes. It gives a fair chance to farmers to quote a price using smart contracts.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133074762","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

Development of Prediction and Forecasting Model for Dengue Disease using Machine Learning Algorithms 基于机器学习算法的登革热疾病预测模型的开发

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2020-10-30 DOI: 10.1109/DISCOVER50404.2020.9278079

Swapna Saturi

{"title":"Development of Prediction and Forecasting Model for Dengue Disease using Machine Learning Algorithms","authors":"Swapna Saturi","doi":"10.1109/DISCOVER50404.2020.9278079","DOIUrl":"https://doi.org/10.1109/DISCOVER50404.2020.9278079","url":null,"abstract":"Globally, Dengue is one of the most quickly spreading vector-borne viral sicknesses with an expanding number of territories in danger. Many researchers have worked on different measures to control and prevent the spread of disease. The main objective of the research is to develop a forecast model to control the outbreak of dengue disease that will give an opportunity for medical professionals in designing, planning and handling the disease at an early stage. Moreover, the improvement of the assortment of strategies for determining and predictive modeling utilizing measurable, numerical examination of machine learning was studied. There are mainly six issues need to be solved in determination of dengue disease, those are exploring data sources, analyzing data sources, techniques for data preparation, data representation, dengue forecasting models and evaluation approaches. A major limitation of the traditional methods is that these methods need large volumes of data for data processing, to improve the dynamic characteristics. From the review of existing methods, it can be clearly stated that the K-means clustering method with fuzzy based system has high accuracy and it significantly improves the analysis/prediction of dengue disease. The k-means clustering algorithm separates the dengue diseased patient records into k divisions. As the dengue dataset were fully clustered, k-means clustering method improves the analysis or prediction of dengue disease. Similarly, the fuzzy based system The input factors and changing over these informational factors into fuzzy membership functions will make a better decision making in predicting dengue forecasting model. Thus, the issues stated from comprehensive research provide a useful platform for public health research and epidemiology.","PeriodicalId":131517,"journal":{"name":"2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397866","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