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Revisiting computer networking protocols by wireless sniffing on brain signal/image portals

NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 11097-11109页

作者： Sathishkumar, B. R. Sundaravadivazhagan, B. Martin, Betty Sasi, G. Chandrasekar, M. Kumar, S. Rakesh Elamaran, v Balaji, v. S. Arunkumar, N. Sri Ramakrishna Engn Coll Dept ECE Coimbatore Tamil Nadu India Al Musanna Coll Technol Dept Informat Technol Musanna Oman SASTRA Deemed Univ Sch EEE Thanjavur Tamil Nadu India CVR Coll Engn Dept ECE Hyderabad India

Brain signal/image processing is of significant attention not only the physiologist carrying out analysis and probe and the clinician investigating patients but further to the biomedical engineer who is vital for acquisition, processing, and interpreting the electroencephalogram signals by designing systems and algorithms for their control. The precious, abundant materials or information in the field of brain signal/image processing is distributed in the diverse scientific, technological and physiological periodicals, magazines, journals, international conference proceedings, and also in various portals/databases. Security threats or attacks may happen for a portal using data interruption, information interception, content modification and fabrication with new data. This study interprets the protocol layering information for the captured packets, image reconstruction after sniffing the packets, and the DNS/rDNS response times for a given portal/IP address using a Wireshark open source tool. Also, the security assessment results such as OS fingerprinting and port sweeping on the remote machines are performed using Nmap open source tool. Results are analyzed on specific brain signal/image processing portals around the globe located in USA, UK, and other countries.

关键词： Brain signal image Computer networking protocols DNS image reconstruction HTTP response Nmap vulnerability Wireshark

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Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing

Deep Unified Model for Face Recognition based on Convolution...

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International Conference on Communication and Electronics systems (ICCES)

作者： v. Senthilkumar P. Saranya B Kezia Rani Srinivasan P Ramu Kuchipudi Md. Abul Ala Walid Department of CSE Kumaraguru College of Technology Coimbatore Tamilnadu India Department of ECE Dr. N.G.P. Institute of Technology Coimbatore Tamilnadu Department of Information Technology Vasavi College of Engineering Hyderabad Telangana India Department of ECE Sona College of Technology Salem Tamilnadu Inida Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

CCTv, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. The variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). This paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). The suggested system had 99.1% accuracy on test data.

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基于YOLO v8n改进的小麦病害检测系统

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农业机械学报 2024年第S1期55卷 280-287,355页

作者：刘梦姝张春琪晁金阳唐彬张鹏磊李民赞孙红中国农业大学烟台研究院烟台264670 中国农业大学智慧农业系统集成研究教育部重点实验室北京100083 中国农业大学农业农村部农业信息获取技术重点实验室北京100083

针对现有小麦病害检测算法精度低、处理速度缓慢、易受背景环境干扰、难以检测目标病害等问题,结合先进的智能手机硬件、便捷的微信小程序以及高效的云服务平台,设计一个基于云架构的小麦病害检测系统。系统主要包括云服务器模块和微信... 详细信息

针对现有小麦病害检测算法精度低、处理速度缓慢、易受背景环境干扰、难以检测目标病害等问题,结合先进的智能手机硬件、便捷的微信小程序以及高效的云服务平台,设计一个基于云架构的小麦病害检测系统。系统主要包括云服务器模块和微信小程序模块,云服务器端主要用于图像接收和模型处理;使用CSS和Java Script语言开发微信小程序,用于实现数据上传、信息反馈与信息显示。为保证模型在云服务器部署的可行性,提出一种基于YOLO v8n改进的小麦病害检测模型(C2f-Faster-Slim-Neck-YOLO v8n,CS-YOLO)。该模型结合FasterNet轻量化优点,使用FasterNet中的FasterNet Block替换C2f中Bottleneck模块,降低模型内存占用量的同时,提高模型特征融合能力和检测精度。在颈部网络使用GSConv并采用Slim-Neck设计范式中的vov-GSCSP模块对YOLO v8n的Neck进行改进,降低模型计算量的同时提高模型检测精度。试验结果表明,对于大田环境下所采集的小麦病害数据集,改进后模型浮点运算量及模型内存占用量相比YOLO v8n基线模型分别降低24.4%和17.5%,同时平均精度均值相较于原模型提高1.2个百分点,且优于YOLO v3-tiny、YOLO v5、YOLO v6、YOLO v7和YOLO v7-tiny算法。最后将轻量化检测模型CS-YOLO部署到云服务器上,将检测功能转化为API接口,小程序利用请求调用其接口调用服务器连接,服务器收到请求后,将数据传递给部署在云服务器上的模型,用户通过使用微信小程序调用检测模型对病害图像进行类型识别和病害位置检测,平均精度均值为89.2%,可为小麦病害识别类型和检测病害位置提供技术支持。

关键词：小麦病害检测深度学习 YOLO v8模型微信小程序

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A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DET...

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Recent Trends in Microelectronics, Automation, Computing and Communications systems (ICMACC), International Conference on

作者： Y. Chalapathi Rao P. Lavanya v. Sagar Reddy v. Naveen Kumar Department of ECE VNR VJIET Hyderabad Telangana India Department of ECE MLRITM Hyderabad Telangana India

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The “lost” regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray.

关键词： Deep learning image segmentation Solid modeling Pulmonary diseases Lung Lung cancer X-rays

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A Comparative Study of Artificial Intelligence based vehicle Classification algorithms used to Provide Smart Mobility

A Comparative Study of Artificial Intelligence based Vehicle...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Prarthana v Sushma Narayan Hegde Sushmitha T P Savithramma R M R Sumathi Computer Science & Engineering Siddaganga Institute of Technology Tumakuru India

Due to the rising number of vehicles on the road and the limited resources supplied by current infrastructures, traffic problems are becoming more prevalent. Signalized junctions are the prime locations of congestions where commuters need to wait for long time in front of the signals to get their turn to move. This leads to several issues including wastage of time, additional fuel consumption, and green gas emissions. Optimization of traffic signals based on traffic behavior is widely explored topic in which vehicle detection and classification is one of the leading areas of research of Intelligent Transportation System (ITS). Among the technologies Artificial Intelligence (AI) has emerged as a giant in which vehicle classification has developed as a prominent subject of study because of its usefulness in several applications such as traffic control and surveillance, security systems, traffic congestion, avoidance, and accident prevention. Numerous algorithms and techniques for classifying vehicles have been proposed and implemented so far globally which mimics human intelligence. The goal of the paper is to familiarize the reader with the existing AI-based vehicle classification algorithms and to give a comparison of various vehicle detection and classification methods. The existing vehicle classification algorithms are summarized under two categories based on input type i.e., image or video. When the technologies such as AI, image processing, data mining and sensors are combined, the ITS can observe the road, initiate autonomous vehicle detection and thereby control traffic on road efficiently.

关键词： Support vector machines Roads image processing Surveillance Urban areas Transportation Traffic control

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Enhanced security feature of ATM's through facial recognition 5

Enhanced security feature of ATM's through facial recognitio...

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5th International Conference on Intelligent Computing and Control systems, ICICCS 2021

作者： Soundari, D.v. Aravindh, R. Edwin Raj, K. Abishek, S. Sri Krishna College of Engineering and Technology Department of ECE Coimbatore India

ISBN: (纸本)9781665412728

Automated Teller Machines also known as ATM's are widely used nowadays by each and everyone. The ATM machine (Automated Teller Machine) is an electronic device that is used by the banks to perform banking tasks like withdrawal of money, transferring of money, and many to get many information about a user's bank account without the need to visit a bank. This System revolutionised the way of transactions. There were no long lines of queue in front of the bank for a simple withdrawal of money. The number of ATM's a bank has can be a factor in considering the strength of a bank. As there is increase in the number of ATM's, there is also increase in the fraudulent activities in the ATM. The main motivation of this project is to increase the security feature of the use of ATM. The current method uses static key (PIN) for security. The proposed method uses Face-id as a key incorporated with current method. The advantages can be found as that the face-id is unique for everybody;it cannot be used by anybody other than the user. For the implementation of the face-id scan, the machine learning and image processing algorithms (Eigenface algorithm) are used. © 2021 IEEE.

关键词： Machine learning

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An Investigation on the Effect of Color Model for Natural image processing 6

An Investigation on the Effect of Color Model for Natural Im...

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6th International Conference on Advanced Computing and Communication systems, ICACCS 2020

作者： Ganesan, P. Sivakumar, v.G. vadivel, M. Thulasi Prasad, S. Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Hyderabad Telangana India

ISBN: (纸本)9781728151977

Color model is also referred to as a mathematical organization or arrangement of colors as numerical values as three or four color components or channels. Every color space has unique features and application oriented. The paper presented an extravagant investigation on color spaces for image processing applications. Fuzzy c-means clustering is one of the widely applied algorithms for image segmentation. This work too utilized FCM to test the influence of color space on image processing applications. © 2020 IEEE.

关键词： image segmentation

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Deep Learning Model for Face Mask Based Attendance System in the Era of the Covid-19 Pandemic 7

Deep Learning Model for Face Mask Based Attendance System in...

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7th International Conference on Advanced Computing and Communication systems, ICACCS 2021

作者： Kumar, R. Satheesh Rajendran, Anagha Amrutha, v. Raghu, Gopika.T. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9781665405201

Nowadays we are facing a pandemic, there is a situation where people are not ready to wear face masks, or they do not wear them properly, so, in this research, we are introducing an automatic mask detection system using image processing and soft computing techniques to tackle this problem. In the midst of the pandemic, covering our faces with a mask has become a new normal, as face masks are active in preventing the spread of the virus. Other precautionary measures are also advocated by the government apart from covering faces, to ensure protection and hygiene. In addition, because of the limited supply of masks in the industry, millions of people are learning to make their face masks. On the opposite, identifying faces with masks on any surveillance devices would be demanding while ensuring less access control in buildings. Face coverage with masks is a problem for algorithms and success in face detection. Currently, the authorities have to manually ask people to wear masks even then they tend to fool the authorities, to avoid that we are proposing a face Machine learning-based model of recognition. In the field of computer vision, this is a common research direction by extracting features directly from the detection region and then using machine detection learning algorithms to identify and recognize. In this Face mask detection-based attendance System, people will be only able to mark their attendance only if they wear a mask, besides if they do not wear a mask, they are given an alert and they would have to wear a mask. © 2021 IEEE.

关键词： Face recognition

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Classification of brain tumours in MRI images using convolutional neural network through Cat Swarm Optimization

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EXPERT systems 2022年第9期39卷 e13021-e13021页

作者： Deepak, v. K. Sarath, R. Noorul Islam Ctr Higher Educ Dept Elect & Commun Thuckalay Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Elect & Instrumentat Thuckalay Tamil Nadu India

In humans, a brain abnormality is a serious illness. Cancer which is the greatest cause of mortality can develop from the tumour. Magnetic resonance imaging (MRI) is among a more extensively utilized medical imaging modalities in brain tumours, then it has become the primary diagnosing mechanism for the treatment and evaluation of brain tumours. Computer-assisted diagnosis has become a requirement due to the exponential expansion in the quantity of MRIs acquired because of these programmes. Computer-assisted diagnosis strategies created to increase detection without many systematized readings failed to produce significant improvements in performance measurements. In this regard, the usage of deep learning-based automatic image processing algorithms appears to be a viable route for identifying brain cancer. In this research, introduce a Cat Swarm Optimization (CSO) algorithm based upon a convolutional neural network (CNN) model utilized to segmentation in a classification of brain tumour. Results of experiments on MRI images using the BRATS dataset show that the CSO algorithm-CNN model achieved high-performance in term of 98% of accuracy, precision, specificity, sensitivity and F-score in the proposed classification task when compared to other classification approaches like support vector machines (SvM) as well as back propagation neural networks (BPNN).

关键词： brain tumour Cat Swarm Optimization algorithm convolutional neural network MRI image

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Implementation of fast ICA using memristor crossbar arrays for blind image source separations

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IET CIRCUITS DEvICES & systems 2020年第4期14卷 484-489页

作者： Boppidi, Pavan Kumar Reddy Louis, victor Jeffry Subramaniam, Arvind Tripathy, Rajesh K. Banerjee, Souri Kundu, Souvik Birla Inst Technol & Sci BITS Pilani Dept Elect & Elect Engn Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Technol & Sci BITS Pilani Dept Phys Hyderabad Campus Hyderabad 500078 Telangana India

Independent component analysis (ICA) is an unsupervised learning approach for computing the independent components (ICs) from the multivariate signals or data matrix. The ICs are evaluated based on the multiplication of the weight matrix with the multivariate data matrix. This study proposes a novel Pt/Cu:ZnO/Nb:STO memristor crossbar array for the implementation of both ACY ICA and Fast ICA for blind source separation. The data input was applied in the form of pulse width modulated voltages to the crossbar array and the weight of the implemented neural network is stored in the memristor. The output charges from the memristor columns are used to calculate the weight update, which is executed through the voltages kept higher than the memristor Set/Reset voltages (+/- 1.30 v). In order to demonstrate its potential application, the proposed memristor crossbar arrays based fast ICA architecture is employed for image source separation problem. The experimental results demonstrate that the proposed approach is very effective to separate image sources, and also the contrast of the images are improved with an improvement factor in terms of percentage of structural similarity as 67.27% when compared with the software-based implementation of conventional ACY ICA and Fast ICA algorithms.

关键词： memristors independent component analysis neural nets blind source separation unsupervised learning matrix multiplication pulse width modulation image processing Fast ICA algorithms blind image source separations independent component analysis unsupervised learning multivariate signals weight matrix multivariate data matrix neural network memristor columns ICA architecture ACY ICA Pt Cu:ZnO Nb:STO memristor crossbar array pulse width modulated voltages

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