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Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

computer Systems Science & engineering 2023年第7期46卷 181-193页

作者： V.S.Vaisakhi D.Surendran Department of Electronics and Communication Engineering Nehru Institute of Engineering and TechnologyCoimbatore641105India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641105India

In signal processing and communication systems,digital filters are widely *** some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter *** strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the *** technology advances,more complicated systems with several filters become *** of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input ***,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was *** fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the *** primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty ***,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image *** approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR *** the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of *** were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

关键词： Fault tolerance filters efficiency redundancy image denoising error correction codes double modular redundancy

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Thin film ferroelectric photonic-electronic memory

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Light(Science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Adaptive Flowing Traffic Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in B5G/5G New Radio and Core Network

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IEEE Transactions on Network Science and engineering 2024年第2期11卷 1934-1946页

作者： Chang, Ben-Jye Lin, Yu-Ting National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu City64002 Taiwan

For differentiating and customizing different classes of traffic and virtualizing physical resources of networks and machines, B5G/5G specifies several novel mechanisms, including VNF, SDN, Service Function Chaining, Network Slicing, MEC, etc. In B5G/5G, the out-of-band contention based random access using PHY subframe preambles is specified before data transmissions. However, B5G/5G requires dynamically efficient approaches for realizing real 5G, including randomly contending limited sharing channel preambles, dynamically determining flow slicing of SFC, dynamically determining PM/VNFi/VNFc resources, while minimizing carrying cost and energy consumption via multi-link/multi-path 5G network. This paper thus proposes the efficient Adaptive Slice Flowing Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in 5G (APROR) consisting of two phases: Dynamic Flow Random Contention of 5G (dFRC) and Adaptive Real-time Predictive Flow Data Rate of diverse types of slicing (aRPF). Numerical results show APROR outperforms all approaches in performance metrics: contention collision probability, access delay, E2E delay, loss, throughput, etc. Consequently, several contributions are definitely achieved: 1) to dynamically determine the preamble configuration modes and dynamic flow backoff, 2) to adaptively differentiate collision domains for different types of flows and minimize collision probability, and 3) to determine the optimal Virtual/Physical Resource achieving optimal SFC corresponding to network slices. © 2013 IEEE.

关键词： Real time systems

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The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

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computer Systems Science & engineering 2023年第7期46卷 125-135页

作者： S.Navaneethan P.Siva Satya Sreedhar S.Padmakala C.Senthilkumar Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Information Technology Seshadri Rao Gudlavalleru Engineering CollegeKrishna521356India Department of Computer Science Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennai602105India

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so *** pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and *** Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of *** improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil *** proposed method is based on very deep architecture and many tricks from recently developed popular *** results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge ***,the region-based black box method for determining pupil center coordinates was *** proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

关键词： BAT algorithm IRIS datasets DCNN pupil detection black box method

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Hybrid Sampling Technique for Imbalanced Android Malware Family Classification

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SN computer Science 2025年第3期6卷 1-16页

作者： Chauhan, Kshamta Gandotra, Ekta Department of Computer Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

The rapid explosion of Android-based devices has led to a disturbing surge in the volume and sophistication of Android malware. Effective classification of these malicious applications is essential for safeguarding user security and privacy. However, the imbalanced nature of the Android malware data, where some of the families outnumber the other ones, poses a significant challenge to traditional machine learning algorithms. The issue of imbalanced data can be handled by oversampling and undersampling techniques. Oversampling raises the number of instances in the minority class and undersampling decreases the number of instances in the majority class in order to balance the class distribution. Oversampling and undersampling can lead to overfitting and loss of information. To combine the strengths of both techniques, hybrid sampling is used. This paper proposes hybrid sampling techniques that combine oversampling and undersampling methods. Two oversampling techniques, i.e., synthetic minority oversampling technique (SMOTE), and random oversampling (ROS), and three undersampling techniques, i.e., tomek's links (TOMEK), edited nearest neighbours (ENN), and neighborhood cleaning rule (NCR), are used. The proposed hybrid sampling methods are obtained after combining each oversampling technique with an undersampling one. All the hybrid sampling techniques are evaluated on a self-created malware dataset that consists of multiple imbalanced classes of malware families. Four machine learning models, i.e., support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbours (K-NN), are trained after applying the hybrid sampling techniques. From the experimental results, it is concluded that ROS-NCR outperforms other methods in handling imbalanced Android malware families. It achieves 95.81% accuracy and an F-measure of 0.95 using RF. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android malware Hybrid sampling Imbalanced data Machine learning Multi-class classification

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Multisensor information Fusion for Condition Based Environment Monitoring

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Intelligent Automation & Soft Computing 2023年第4期36卷 1013-1025页

作者： A.Reyana P.Vijayalakshmi Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India Department of Electronics and Communication Engineering Hindusthan College of Engineering and TechnologyCoimbatore641050India

Destructive wildﬁres are becoming an annual event,similar to climate change,resulting in catastrophes that wreak havoc on both humans and the *** result,however,is disastrous,causing irreversible damage to the *** location of the incident and the hotspot can sometimes have an impact on earlyﬁre detection *** the advancement of intelligent sen-sor-based control technologies,the multi-sensor data fusion technique integrates data from multiple sensor *** primary objective to avoid wildﬁre is to identify the exact location of wildﬁre occurrence,allowingﬁre units to respond as soon as *** to predict the occurrence ofﬁre in forests,a fast and effective intelligent control system is *** proposed algorithm with decision tree classiﬁcation determines whetherﬁre detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex *** experimental results of the proposed model have a detection rate of ***,providing real-time monitoring of certain environ-mental variables for continuous situational awareness and instant responsiveness.

关键词： Decision tree communication wildﬁre data fusion wireless sensor networks

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Preventing 51% Attack by Using Consecutive Block Limits in Bitcoin

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IEEE Access 2024年 12卷 77852-77869页

作者： Mahmood Babur, Sohail Ur Rehman Khan, Shafiq Yang, Jing Chen, Yen-Lin Soon Ku, Chin Yee Por, Lip Government Murray College Department of Information Technology Sialkot51010 Pakistan Capital University of Science and Technology Department of Computer Science Islamabad44730 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker's private chain. The modified algorithm introduces a 'Safe Mode Detection Algorithm' that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker's chain with those from the miner's own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker's chain, establishing a double-spend-free environment and thwarting 51% attacks. © 2013 IEEE.

关键词： Blockchain

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Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques

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Environmental Science and Pollution Research 2025年第1期32卷 234-247页

作者： Thangarasu, Thenmozhi Aldehim, Ghadah Alruwais, Nuha Kandasamy, Anguraj Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Salem India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia P.O.Box 22459 Riyadh11495 Saudi Arabia Department of Electronics & Communication Engineering Sona College of Technology Tamil Nadu Salem India

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for wastewater treatment plants using a combination of machine learning, remote sensing, and GIS-based multicriteria decision analysis (MCDA). Several datasets were analysed, with the analytical hierarchy process (AHP) assigning weights to factors such as slope (18.51) and elevation (31.39), which were found to be crucial in site selection. The study classified the suitability of sites into five categories, with the western region being the most favourable due to its low elevation (147 to 200 m), gentle slopes (0–3%), and substantial land availability (approximately 309.00 sq. km). Overall, the site suitability analysis revealed that 14.48% (110.2 sq. km) of the area falls within "Very High Favourable Zones," while 11.21% (85.3 sq. km) is categorized as "High Favourable Zones." Moderate and low favourable zones account for 9.63% (73.3 sq. km) and 24.04% (182.9 sq. km), respectively. The remaining 40.57% (308.7 sq. km) is considered "Very Low Favourable Zones." These results can guide urban planning decisions, highlighting the importance of factors such as land availability, population growth, wastewater volume, and flood vulnerability. Integrating GIS technology with decision-making processes enhances the strategic placement of urban utilities, ensuring long-term sustainability for Coimbatore South's wastewater management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Site selection

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Gene Variant Analysis for the Detection of Hemophilia: A Literature Review

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Recent Patents on engineering 2024年第9期18卷 48-62页

作者： Sumathi, N. Anitha Kumari, K. Department of Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Information Technology PSG College of Technology Tamil Nadu Coimbatore641004 India

Background: Proteins act as clotting factors to stop bleeding at the lesion site. This implies that people with hemophilia tend to bleed longer after an injury and are more prone to internal bleeding. Depending on the type of hemophilia, individuals with hemophilia will have lesser amounts of factor VIII or factor IX than people without it. Objective: By analyzing the gene variant of hemophilia affected patient we can predict the severity of disease at earlier stage which helps to avoid further complications. Methods: Predicting hemophilia can be achieved through potential technologies like machine learning. Using these technologies, one can detect and predict the severity of hemophilia, such as mild, moderate, or severe. Results: By comparing the methods used in protein structure analysis, the advantages and limitations of methods used in protein structure analysis are discussed. Conclusion: The best practices in predicting hemophilia are highlighted in this patent study and particularly aim at the basic understanding of applying the potential technologies in the prediction of hemophilia and its severity. This study represents recent research on hemophilia and the use of different machine learning techniques (MLT) in this area. © 2024 Bentham Science Publishers.

关键词： Proteins

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