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Delaying the occurrence of instability of the HSG-FD-FDTD method using spatial filtering

AIP Conference Proceedings 2022年第1期2644卷

作者： Atheel Nowfal Alkhayyat Hayder Wathik Altameemi Ruslan Saad Abdulrahman Aymen Jaber Salman 1Department of Computer Engineering College of Engineering Al-Nahrain University Baghdad Iraq 2Department of Electronic Services Industrial Bank Baghdad Iraq

The main goal of Huygens subgridding (HSG) technique is to increase the computational efficiency by decreasing the memory usage and computational time. A high value (larger than ten) of subgridding ratio can be used. However, the HSG method experiences the late-time instability. In this paper, an algorithm was proposed and implemented to overcome the stability problem. 2D frequency dependent (Debye model) simulations were conducted, and the results were analyzed and discussed. In the proposed method, it is discovered that the occurrence of late-time instability was delayed about four times the occurrence of instability in the conventional HSG method. Furthermore, two parameters play a vital role on the occurrence of late-time instability and the selection of their values determines the delay of instability.

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Centralized network infrastructure and virtualization of inter wireless networks

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AIP Conference Proceedings 2023年第1期2523卷

作者： Narendhar Mulugu Channapragada Rama Seshagiri Rao Yeligeti Raju L. N. C. Prakash K. 1Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India. 2Department of Computer Science and Engineering Vignana Bharathi Engineering College Hyderabad Telangana India. 3Department of Information Technology Vignana Bharathi Institute of Technology Hyderabad Telangana India. 4Department of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India.

Wireless network computing has been suggested as a voracious development by isolating and exchanging network infrastructure across various software systems in future wireless networks to counter the contradiction amongst the rapid development of the demand for information services besides the small bandwidth of resources. In this article, the multi-service virtualized wireless system consists of three broad-based situations and suggests a two-dimensional hierarchic resource distribution system. The resource division and allotment dilemma are composed of two-time scales with a large time frame before the equivalent lateral assets and a limited time frame for the intra-slice resource preparation. In the long run, the problem of pre-allotting inter-slice services is conceived by modelling the packets that arrive and serve each slice as a queuing device. In short, the source and energy management in each share is described as a stochastic optimization problem because of variable time and complex traffic arrivals. This problem aims to maximize overall output relative to various service quality criteria, including network stability, latency, throughput, and control restrictions. By applying the Lyapunov objective function, the stochastic optimization problem can be turned into a delay-aware optimization problematic methodology comprising a probabilistic and concave algorithm. The simulation findings show that the suggested systems will minimize uncertainty with an output loss of up to 0.017 percent relative to the optimum solution, which also allows for a balance in terms of the regulation coagulation Factor quality.

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Evaluation of smart metering network and billing system using bluetooth, ZigBee, and GSM

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AIP Conference Proceedings 2022年第1期2644卷

作者： Ahmed Oleiwi Abdulrida Al-Ubodi Israa Badr Nasser Al-Mashhadani 1Department of Computer Engineering College of Engineering Al-Nahrain University Baghdad Iraq 2Department of Biomedical Engineering College of Engineering University of Warith Al-Anbiyaa Iraq

The necessity of managing energy through smart devices has become a significant issue in recent years. Though, the intelligent use of smart devices is an essential component in creating smart cities. Hence, it leads to establishing a smart grid as an infrastructure for smart cities. The smart grid infrastructure consists of smart meters, which is more reliable than digital meters for billing generation. A real-time smart meter system design is proposed and tested in this work. The measured voltage, current, and power factor for energy are multiplied every second and summed up for overall energy consumption, and this process applies to every consumer. Then, the energy consumption of each house is collected from a single region by sending them to the DCU (Data Concentrator Unit). Methods of transferring data in the proposed system consist of three technologies. The first one is the Bluetooth used between the smart socket and the smart meter. This technology was used due to the short distance between the two devices. The second one is Xbee, and it connects the smart meter and the DCU. This technology is used due to the distance between the two devices and the number of smart meters connected in one DCU. The last technology is GSM (Global System for Mobile Communications), and it is used between the DCU and the server to transfer data since this technology can cover long distances. Energy providers will have to implement innovative Time-of-Use (TOU) pricing strategies to implement the above process. A smart meter has the potential to encourage consumers to shift demand from peak hours to off-peak hours or reduce peak demand. And upon the real experiment of the system, it was found that the error rate in the voltage sensor used in the system was 0.26%, and the error rate in the current sensor used was 0.6%. These numbers indicate the reliability of the system because they are acceptable values.

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Healthcare Android application for elderly and disabled people

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AIP Conference Proceedings 2023年第1期2523卷

作者： M. Kumarasamy Jaiprakash Narain Dwivedi Balachandra Pattanaik 1Department of Computer Science College of Engineering and Technology Wollega University P.O. Box. 395 Nekemte Ethiopia 2Department-ECE Chandigarh University Mohali Punjab India 3Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Africa

Machine learning and big data are influencing almost every area of modern life, including entertainment, commerce, and healthcare. Netflix knows which films and series people love to watch, Amazon knows which things people want to buy when and where, and Google knows which symptoms and illnesses they search for. All of this data may be utilized for highly thorough personal profiling, which may be useful for behavioral understanding and marketing but also has the ability to anticipate healthcare trends. There is a lot of hope that the use of artificial intelligence (AI) could result in significant advances in all aspects of healthcare, from diagnosis to therapy. It is widely assumed that AI technologies would support and enhance human labor rather than completely replacing the employment of physicians and other healthcare professionals. AI is ready to assist healthcare workers with a wide range of activities, from administrative workflow to clinical documentation and patient outreach, as well as specialized assistance in image analysis, medical device automation, and patient monitoring. In this chapter, we will look at some of the key applications of AI in healthcare, including those that are directly related to healthcare as well as those that are part of the healthcare value chain, such as drug research and ambient assisted living.

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A review of computer vision techniques for the analysis of vehicles

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AIP Conference Proceedings 2022年第1期2481卷

作者： Aastha Gour Durgaprasad Gangodkar Vikas Tripathi 1Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India 2Department of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India

Due to the increasing number of vehicles on the roads, a traffic analysis system is of peak importance. Various computer vision techniques and video analytics are used in the traffic analysis system to get useful information. This acquired information can be later used to make crucial judgements in real-time environment. This paper presents a comprehensive literature review in traffic analysis especially in the domains like moving vehicle detection, object identification and traffic volume estimation. The tabulated data presents the different algorithms along with their achieved performances. The paper also provides the limitations and the future scope of the discussed approaches.

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Generic object detection using deep learning

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AIP Conference Proceedings 2022年第1期2444卷

作者： M. Malathi C. Paramasivam K. Nagalakshmi Gospeline Christiana S. Sangeetha 1Department of Computer Science and Engineering Sethu Institute of Technology Virudhu Nagar Tamilnadu India 2Department of Mechanical Engineering Thiagarajar College of Engineering Madurai Tamilnadu India

Object discovery is one of the very important and challenging problems in computer computing, seeks to obtain the properties of objects from a large number of categories previously described in natural images. In particular, image classification that aims to see the semantic categories of objects in a given image. Object discovery is not only identifies item categories but also predicts the location of each item with a mandatory box. Semantic separation process aims to predict intelligent pixel separation to assign a specific category label to each pixel, thus providing a richer and more comprehensive understanding of the image. In depth learning strategies have emerged as a powerful strategy for reading presentations directly from the data and have led to significant success in the field of general acquisition. Given this period of rapid evolution, the aim of this paper is to provide a comprehensive analysis of the latest achievements in this field brought about in-depth learning strategies. Here, the deep learning object detection algorithm is implemented for accuracy and time effectiveness to show the correct label for a character of interest in a movie which will be more useful in surveillance of building, ATM centres etc. Finally, it concludes with a promising indication for future research.

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Student’s academic performance prediction by using ensemble techniques

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AIP Conference Proceedings 2022年第1期2555卷

作者： Nidhi Mukesh Kumar Disha Handa Shweta Agarwal 1Department of Computer Science and Engineering Chandigarh University Punjab India 140413 2Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Himachal Pradesh India 173212

Data Mining is a discipline of Machine Learning which is used to find or extract the information from the huge database of any organization by using some informative techniques. These informative techniques are then divided into different categories like clustering, classification, regression and association rule mining etc. Data Mining is widely used techniques in different fields like education, telecommunication, hospital, hospitality industry etc. As education plays a crucial role in the life of a human being therefore its proper monitoring is also very important. Thus, to predict the academic performance of any student on time and to help students improve their academic perform different researcher is working in the field and tried to develop a system which help to improve the prediction result. In this paper, different classification algorithms are used on academic dataset of the student in conjunction with different ensemble learning algorithm to improve the prediction result as compared to prediction result given by simple classification algorithm. At the end a comparison is also given which show the performance improvement by ensemble learning as compared to simple classification. The maximum improvement is shown by Multilayer perceptron algorithm and it is up to 15%.

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Theoretical investigation of a nano scale all-optical 1 × 2 demultiplexer

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AIP Conference Proceedings 2023年第1期2651卷

作者： Saif Hasan Abdulnabi Mohammed Nadhim Abbas 1Department of Electronic and Communications Engineering Faculty of Engineering University of Kufa Iraq 3Department of Computer Technical Engineering College of Information Technology Imam Ja'afar Alsadiq University / Al-Najaf Al-Ashraf Iraq 2Department of Electrical Engineering College of Engineering University of Baghdad Iraq

In this paper, we have suggest, design, and simulate an all optical 1*2 demultiplexer (Demux) depended on Insulator-Metal-Insulator (IMI) plasmonic multilayer structure. The proposed structure offers an operating wavelength at 1550 nm and the design area is (350 nm* 350 nm). In this work, Finite Element Method (FEM) is used by using COMSOL Multiphasic software (version 5.4). The working principle of the proposed device (plasmonic Demux) depends on destructive and constructive interference, which occurs on input signal, selector signal, and control signal. The borderline between Logic 0 and Logic 1 at the output is 0.5 that is called transmission threshold. The performance of the proposed structure is measured by three parameters; Transmission, Extension Ratio, and Modulation Depth (MD). This devise shows, in some cases, the transmission value reaches more than 80%, Extension Ratio (3.5 dB), and very high MD value (97.6%). The proposed structure has a major role in building the arithmetic logic unit (ALU) and photonics integrated circuits (ICs).

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Energy-management system for plug-in hybrid electric vehicle using optimized artificial neural networks

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AIP Conference Proceedings 2022年第1期2519卷

作者： Balachandra Pattanaik Mukil Alagirisamy 1Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Africa 2Department of Electrical Engineering Faculty of Engineering Lincoln University College Wisma Lincoln Malaysia

Hybrid Plug-in Electric vehicles (HPEV) are new automobiles that can operate on gasoline and electricity stored in a battery pack. As a result, by utilizing the cheaper renewable and nonrenewable energy sources accessible at the household electric outlet, these cars may considerably cut their fuel usage. As a result, PHEVs have the potential to reduce overall greenhouse gas emissions from vehicles dramatically. A simplified power train of a power split PHEV is modeled in this study. The primary goal of the research is to improve the PHEV’s fuel economy. To accomplish this, the aforementioned simplified model has been used to implement the Optimized Neural Network approach. An optimization problem is developed with Equivalent Fuel Consumption Minimization (EFCM) as the primary objective function and various constraints. The particle swarm optimization (PSO) methodology is used to improve the efficiency of HPEV, estimates by optimizing the process parameters of the ANN. In comparison to existing approaches, the suggested method can produce optimistic outcomes based on the simulation studies.

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Ensemble learning-based deep neural network model for face recognition

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AIP Conference Proceedings 2022年第1期2463卷

作者： G. Priyanka J. Senthil Kumar 1Department of Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi-626005 India 2Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi- 626005 India

Due to the poor regularization capability of single Convolutional Neural Network (CNN), the performance of face recognition system is severely affected. Also, the input facial image data for recognition is influenced by surrounding information like light, expression, brightness, contrast, pose variations and other factors. This issue is overcome by using ensemble-based feature learning ofCNN and local binary patterns (LBP). It also aids in improving the occlusion-related low pedestrian detection rate. To begin, the LBP operator is used to extract texture features from the face and by using different 10 CNN architectures, different part of facial information is learnt. This is mainly used to improve the performance of underlying networking attributes and thereby it leads to enhanced classification results using the Softmaxactivation function. With the introduced new form of face recognition focused on parallel ensemble learning of convolutional neural network and local binary pattern for feature extraction, the obtained results outperforms the existing state-of-art techniques. Finally, utilizing majority voting, the approach of parallel ensemble learning is employed to obtain the final result of face recognition. The ORL and Yale-B face datasets identification rates climb to 98.1% and 99.2 percent, respectively, for our propose system. The proposed approach is demonstrated in the experiments to improve not only the model’s immunity to withstand with different illumination, expression, lighting, brightness and posture conditions, but also the accuracy of face recognition with the poor regularization metrics, which denies in reaching the global solution in the solution space.

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