International journal of sensor networks and data communications最新文献

Program Operators SIT, tS, S1 e, Set1 程序操作员SIT, tS, S1, e, Set1

International journal of sensor networks and data communications Pub Date : 2023-10-26 DOI: 10.33140/jsndc.03.01.07

引用次数: 0

Towards Ultraviolet Microbeam Scanning and Lens-Less UV Microbeam Microscopy with Mirror Galvanometric Scanners: From the History of Research Instrumentation to Engineering of Modern Mechatronic Optical Systems 紫外光微束扫描与无透镜紫外光微束镜振镜显微镜:从研究仪器的历史到现代机电光学系统的工程

International journal of sensor networks and data communications Pub Date : 2023-10-23 DOI: 10.33140/jsndc.03.01.06

引用次数: 0

Smart Surveillance: A Review & Survey Through Deep Learning Techniques for Detection & Analysis 智能监控:综述& &;深度学习技术在检测中的应用综述分析

International journal of sensor networks and data communications Pub Date : 2023-10-05 DOI: 10.33140/jsndc.03.01.05

{"title":"Smart Surveillance: A Review & Survey Through Deep Learning Techniques for Detection & Analysis","authors":"","doi":"10.33140/jsndc.03.01.05","DOIUrl":"https://doi.org/10.33140/jsndc.03.01.05","url":null,"abstract":"Big data applications are consuming most of the space in industry and research area. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. Surveillance videos have a major contribution in unstructured big data. CCTV cameras are implemented in all places where security having much importance. Manual surveillance seems tedious and time consuming. Security can be defending in different terms in different contexts like theft identification, violence detection, chances of explosion etc. In crowded public places the term security covers almost all type of abnormal events. Among them violence detection is difficult to handle since it involves group activity. The anomalous or abnormal activity analysis in a crowd video scene is very difficult due to several real world constraints. The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment. Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models. The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions. Paper discusses the underlying deep learning implementation technology involved in various crowd video analysis methods. Real time processing, an important issue which is yet to be explored more in this field is also considered. Not many methods are there in handling all these issues simultaneously. The issues recognized in existing methods are identified and summarized. Also, future direction is given to reduce the obstacles identified. The survey provides a bibliographic summary of papers from Science-direct, IEEE Xplore and ACM digital library.","PeriodicalId":91517,"journal":{"name":"International journal of sensor networks and data communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135547786","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

Deep Surveillance System 深度监控系统

International journal of sensor networks and data communications Pub Date : 2023-09-28 DOI: 10.33140/jsndc.03.01.04

{"title":"Deep Surveillance System","authors":"","doi":"10.33140/jsndc.03.01.04","DOIUrl":"https://doi.org/10.33140/jsndc.03.01.04","url":null,"abstract":"This study has been undertaken to investigate and implement multiple detection systems into a single surveillance system and check whether the input videos may comprise and capture a variety of realistic anomalies or not. In this paper, we propose to learn various anomalies by exploiting both normal and anomalous videos and implemented it to new model. Real time object detection is a vast, vibrant and sophisticated area of computer vision aimed towards object identification and recognition. Object detection detects the semantic objects of a class objects using Open source Computer Vision, which is a library of programming functions mainly trained towards real time computer vision in digital images and videos. The main aim behind this real time object detection is to help the peoples to overcome their difficulty. Real time object detection finds its uses in the areas like tracking objects, video surveillance, pedestrian detection, people counting, self-driving cars, face detection, tracking in sports and many more. This is achieved using Convolution, Probabilistic Neural Networks, etc. which are a representative tool of Deep learning. This project acts as an aiding tool for peoples who wants to take care of everything inside, outside, and around their house just for their full security expectations. Surveillance is a must for small houses to large-scale industries as they fulfil our safety aspects because theft and burglary have always been a problem. By combining this Surveillance idea to IoT and some Machine Learning stuff this will be a major product. The proposed project is a single autonomous surveillance system, based on analysis and detection technology. The proposed system is capable of monitoring all actions at once and alerts the concerned officials immediately and precisely.","PeriodicalId":91517,"journal":{"name":"International journal of sensor networks and data communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470902","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

Federated Learning for Collaborative Network Security in Decentralized Environments 分散环境下协同网络安全的联邦学习

International journal of sensor networks and data communications Pub Date : 2023-09-26 DOI: 10.33140/jsndc.03.01.03

引用次数: 0

The KI-ASIC Dataset KI-ASIC数据集

International journal of sensor networks and data communications Pub Date : 2023-09-22 DOI: 10.33140/jsndc.03.01.02

{"title":"The KI-ASIC Dataset","authors":"","doi":"10.33140/jsndc.03.01.02","DOIUrl":"https://doi.org/10.33140/jsndc.03.01.02","url":null,"abstract":"We present a novel dataset captured from a BMW X5 test carrier within the German research project KI-ASIC for use in radar sensor development and autonomous driving research. Our work aims at providing a blueprint for the process of creating labeled datasets for the development of neural networks for pattern recognition in radar data in the automotive environment. With a variety of different sensor types such as wide angle color cameras, a high-resolution color stereo camera, an Ouster OS1-64 laser scanner and three novel Infineon radar sensors, we recorded over 100,000 scenes of real traffic scenarios as well as defined test scenarios with a frequency of 10 Hz. The scenarios in real traffic contain inner-city situations, but also scenes from rural areas with static and dynamic objects. Besides, the defined test scenarios are based on the NCAP scenarios and focus mostly on turning, overtaking and follow-up maneuvers. The data from the different sensors is calibrated, synchronized and timestamped including raw and rectified information. Our dataset also contains labels for all detected objects from a defined class list with distance and angle properties. The content of the paper aims at the description of the recording test carrier, the format of the provided sensor data and the structure of the overall dataset","PeriodicalId":91517,"journal":{"name":"International journal of sensor networks and data communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136101109","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

Routing Algorithm for Efficient Packet Transmission in Manet Using T–Test Procedure 基于t -检验程序的无线网络高效分组传输路由算法

International journal of sensor networks and data communications Pub Date : 2023-09-20 DOI: 10.33140/jsndc.03.01.01

引用次数: 0

Managing Fake News on Social Media Through Machine Learning - A Comprehensive Analysis 通过机器学习管理社交媒体上的假新闻——一项综合分析

International journal of sensor networks and data communications Pub Date : 2023-09-07 DOI: 10.33140/jsndc.03.01.12

引用次数: 0

Loss Estimation in VANET Communications VANET通信中的损耗估计

International journal of sensor networks and data communications Pub Date : 2023-08-10 DOI: 10.33140/jsndc.03.01.11

{"title":"Loss Estimation in VANET Communications","authors":"","doi":"10.33140/jsndc.03.01.11","DOIUrl":"https://doi.org/10.33140/jsndc.03.01.11","url":null,"abstract":"Vehicle Ad Hoc Networks (VANETs) provide efficient and secure communications between vehicles and infrastructure. Reliable data exchange between vehicles and Roadside Units (RSUs) is the main objective of the Intelligent Transportation System (ITS). One of the main tasks associated with automotive communications is the development of methods for predicting the behavior of VANET communication channels in critical conditions. This article explored data transfer between service provider, vehicles, and infrastructure in ITS. Since VANET requires communication channels with low packet loss, minimal message travel time and high Quality of Service (QoS) with the least number of bit errors, we simulated the simplest wireless communication channel in VANET and obtained data about possible packet losses at the RSU unit, message travel time over the network, the load of the vehicle communication link with the infrastructure, as well as information about the effect of the packet loss in the Internet and the influence of bit errors. The importance and usefulness of the performed numerical simulation lies in the ability to set traffic parameters and observe the resulting channel load, packet loss, message travel time, the number of bit errors and QoS in VANET under certain transmission modes.","PeriodicalId":91517,"journal":{"name":"International journal of sensor networks and data communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135598645","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

Next-Generation OBS Architecture Transforms 5G Networks Powered by Machine Learning, Probabilistic Modeling and Algorithm Optimisation 下一代OBS架构通过机器学习、概率建模和算法优化改变5G网络

International journal of sensor networks and data communications Pub Date : 2023-08-10 DOI: 10.33140/jsndc.03.01.10

{"title":"Next-Generation OBS Architecture Transforms 5G Networks Powered by Machine Learning, Probabilistic Modeling and Algorithm Optimisation","authors":"","doi":"10.33140/jsndc.03.01.10","DOIUrl":"https://doi.org/10.33140/jsndc.03.01.10","url":null,"abstract":"Next-generation 5G networks require a high-speed, low-latency, and robust communication backbone to support new applications such as IoT, cloud computing, and virtual reality. Optical burst switching (OBS) is a promising method for 5G networks due to its ability to handle high-speed data transit and excellent bandwidth utilisation. Traditional OBS networks, on the other hand, have a high blocking probability, low resource utilisation, and limited scalability. To address these challenges, this work provides a unique OBS design that integrates machine learning, probabilistic modelling, and efficient algorithms. The usage of machine learning-based burst assembly algorithms, which dynamically predict the best resource allocation for each burst based on network conditions and QoS requirements, is a key component of the proposed architecture. A complete simulation analysis is performed using a typical Wavelength Division Multiplexing (WDM) traffic dataset to evaluate the performance of the proposed architecture. The simulation results show that, as compared to standard OBS networks, the suggested architecture reduces the likelihood of obstruction and improves resource utilisation significantly. Furthermore, when compared to previous OBS systems, the suggested design is more efficient at managing dynamic traffic and enables greater scalability. The simulation study's performance tests demonstrate that the suggested architecture has a blocking probability of less than 10-6, a throughput of more than 95%, and a latency of less than 4 milliseconds. These findings show that the suggested OBS design for next-generation 5G networks is both feasible and effective.","PeriodicalId":91517,"journal":{"name":"International journal of sensor networks and data communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135598647","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