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A Quadrilateral Shaped Fractal Slot Planar Antenna for Ultra-Wide Band Applications

Australian Journal of Electrical and Electronics engineering 2022年第3期19卷 270-282页

作者： Kumar, Ranjeet Sinha, Rashmi Choubey, Arvind Mahto, Santosh Kumar Department of Electronics and Communication Engineering National Institute of Technology Jamshedpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India

This paper presents a low-profile quadrilateral-shaped fractal slot planar antenna using a novel fractal geometry where circular, triangular and regular hexagonal motifs are iterated to form antenna elements. It consists of a partial ground plane and a triangular fractal-shaped patch fed by a microstrip line having an I-shape rectangular slot to increase the bandwidth. A compact 32 × 22 mm2 fractal antenna proposed here is investigated to evaluate its performance in ultra-wideband (UWB) applications. The bandwidth of 15.5 GHz ranging from 3.57 GHz to 19.07 GHz with voltage standing wave ratio (VSWR) less than 2 in the entire operating band is achieved. Simulated values of realisable gain (6.8 dBi), impedance bandwidth (15.5 GHz), radiation efficiency (90%), radiation patterns and group delay evaluation indicate that the discussed antenna is favourable for UWB applications such as world interoperability microwave access, wireless local area network, 5 G, X-band, C-band, S-band and Ku-band. The proposed antenna is fabricated, and measured results are compared with simulated results as well as with those reported in the literature. ©, Engineers Australia.

关键词： Microstrip antennas

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Development and Creation of Ancient Sandstone Carvings Using 3D Software Tools and Mobile/ Tablet Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 20-35页

作者： Sookkaew, Jirawat Kaenmueang, Wisoot Chaikaew, Nakarin Department of Computer Graphic and Multimedia School of Information and Communication Technology University of Phayao Phayao Thailand Research Unit of Spatial Innovation Development School of Information and Communication Technology University of Phayao Phayao Thailand

In the area of Phayao Province that used to be part of the ancient Lanna Kingdom, there are unique works of art that can be found until today, which are sandstone carvings caused by religious beliefs resulting in works such as Buddha statues. These are art related to religion. Nowadays, these works of art have started to fade away and receive less and less attention from people. The creation of art objects in this research therefore uses the interpretation of the creative objects in line with people’s lifestyles by selecting the lotus, which is a plant related to rivers, and is important in religion. In the lotus, which is interconnected and aligned with Buddhism, and in harmony with a way of life that aligns with the river, creative works are found in various forms, connecting people in Buddhism, including sandstone carving. In this creation, digital technology tools and methods are used to collect data to create a 3D work piece. Photogrammetry is used to record detailed proportions and information and customization of 3D work pieces. In these steps, mobile phone-type tools are used to collect image data to create the 3D work or an application is used to customize 3D work pieces from a tablet device to create prototypes of sandstone sculptures from the artisans that remain today. This can be seen in creating, maintaining, and recording digital data, creation, and the integration of knowledge. This demonstrates that today’s tools and portable devices can help create more creative pieces of work and preserve art and culture. © 2023 by the authors of this article. Published under CC-BY.

关键词： Sandstone

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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Features Responsible for Spread of Zoonotic Disease Brucella: A Study on Central India Population

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SN computer Science 2025年第1期6卷 1-19页

作者： Namdev, Nishant Makhijani, Richa Dorshetwar, Kirti Parate, Mayur Shukla, Jayshree L. Husain, Aliabbas A. Kashyap, Rajpal S. Department of Basic Sciences Indian Institute of Information Technology Maharashtra Nagpur India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur India Research Centre Central India Institute of Medical Sciences Maharashtra Nagpur India

Zoonotic diseases pose a growing health risk in developing nations because of the ongoing animal-human interaction. India has experienced significant urbanization in recent years, with a large population migration from rural to urban areas. One of the most common and feared risks to which humans are exposed is zoonotic diseases. Numerous studies conducted in India have identified specific zoonoses, such as Listeriosis, Brucellosis, and Bovine tuberculosis, as extremely important to human health. The rough set method is appropriate for several real-world applications and is a component of the computing strategy. Rough sets offer several noteworthy benefits. The rough sets encounter small data sets and provide helpful techniques for revealing hidden patterns in data. In this work, a cross-sectional study was conducted to investigate the prevalence of zoonosis in the Central India Population. Three states were studied for zoonotic infection prevalence, including Maharashtra, Madhya Pradesh, and Chhattisgarh. A total of 7026 samples were recruited by inclusion and exclusion criteria for the study. The lifestyle and animal exposure data of this population was collected and tested for Brucella. On this data, the rough set approach yielded an accuracy of 87.5% for the Brucella-infected case. Our approach has resulted in selecting features responsible for diagnosing the lifestyle parameters which could result in the spread of Brucella. This research also follows the UNSDG goal of good health, essential to sustainable development. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brucella Feature selection Pattern recognition Rough sets Zoonotic disease

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Enhancing Blurred Image Processing with IoT Integration for Improved Clarity 2

Enhancing Blurred Image Processing with IoT Integration for ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Tummalapalli, Geetamma Summia Parveen, H. Ramesh, S.M. Monica, P. Santhiya, B. Rajasekaran, Arun Sekar GMR Institute of Technology Department of Electronics and Communication Engineering Rajam India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India GITAM Deemed to be University Department of Electronics and Communication Engineering GST Visakhapatnam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India SR University Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798331534950

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains. © 2024 IEEE.

关键词： Edge computing

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Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

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computer Systems Science & engineering 2023年第2期44卷 1531-1549页

作者： N.Krishnammal C.Kalaiarasan A.Bharathi Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia School of Engineering Presidency UniversityBengaluruIndia Department of Information Technology Bannari Amman Institute of TechnologySathyamangalamIndia

Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as signiﬁcant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identiﬁed that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efﬁciency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.

关键词： Wireless mesh access networks mobile ad-hoc network reinforcement learning multi-objective constraint asymmetric link

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A deep regression convolutional neural network using whole image-based inferencing for dynamic visual crowd estimation

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International Journal of Business Intelligence and Data Mining 2022年第1-2期22卷 100-114页

作者： Teoh, Shen Khang Yap, Vooi Voon Nisar, Humaira Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Malaysia Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Malaysia

As intelligent surveillance system applications become ubiquitous, automated crowd counting solutions must be made continually faster and accurate. This paper presents an improved convolutional neural network (CNN) architecture for accurate visual crowd counting in crowd images. Multi-column convolutional neural network (MCNN) is widely used in previous works to predict the density map for visual crowd counting. However, this method has limitations in predicting a quality density map. Instead, the proposed model is architected using powerful CNN layers, dense layers, and one regressor node with whole image-based inference. Therefore, it is less computationally intensive and inference speed can be increased. Tested on the mall dataset, the proposed model achieved 2.01 mean absolute error and 8.53 mean square error. Moreover, benchmarking on different CNN architectures has been conducted. The proposed model shows promising counting accuracy and reasonable inference speed against the existing state-of-art approaches. © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Prediction of impacts and outbreak of COVID-19 on the society using distinct machine learning algorithms

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Iran Journal of computer Science 2024年第2期7卷 155-175页

作者： Chaity, Taspia Tazri Khan, Md. Ashikur Rahman Rahman, Fardowsi Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh India

The COVID-19 pandemic has substantially impacted daily wage workers in various professions due to restrictions such as lockdowns and social distancing measures. COVID-19 severely affects those people with weakened immune systems. The duration of isolation, lockdown, social distancing, and economic instability changes people’s lifestyles. Thus, COVID-19 has some implicit effects on daily livelihood. Considering this matter, this study desires to analyze and forecast COVID-19’s effects. We collected data on 1665 daily wage workers from eight individual professions to analyze and predict these impacts through an offline survey. Distinct Machine Learning techniques are used for prediction. This research compares distinct machine learning methods for selecting an excellent model to forecast the COVID-19 impact on daily wage workers. Among the seven ML models investigated, Random Forest and XGBoost showed promising results with 92.4% and 91.2% accuracy, respectively. This research will serve as a manual for government employees and regulators to help them comprehend the implications of COVID-19 and implement measures to mitigate these adverse effects. Furthermore, the predictive model developed in this study can help predict the impact of future pandemics or crises on these workers and support the development of targeted policies and interventions to help them. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Economic instability Impact of COVID-19 Isolation Machine learning Random forest XGBoost

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An Algorithm to Reduce Compression Ratio in Multimedia Applications

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computers, Materials & Continua 2023年第1期74卷 539-557页

作者： Dur-e-Jabeen Tahmina Khan Rumaisa Iftikhar Ali Akbar Siddique Samiya Asghar Department of Electronic Engineering Sir Syed University of Engineering&TechnologyKarachiPakistan Department of Information&Computer Science Sir Syed University of Engineering&TechnologyKarachiPakistan

In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the *** purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual *** transmission,this massive data necessitates a lot of channel *** order to overcome this problem,an effective visual compression approach is required to resize this large amount of *** work is based on lossy image compression and is offered for static color *** quantization procedure determines the compressed data quality *** images are converted from RGB to International Commission on Illumination CIE La^(∗)b^(∗);and YCbCr color spaces before being *** the transform domain,the color planes are encoded using the proposed quantization *** improve the efficiency and quality of the compressed image,the standard quantization matrix is updated with the respective image *** used seven discrete orthogonal transforms,including five variations of the Complex Hadamard Transform,Discrete Fourier Transform and Discrete Cosine Transform,as well as thresholding,quantization,de-quantization and inverse discrete orthogonal transforms with CIE La^(∗)b^(∗);and YCbCr to RGB *** to signal noise ratio,signal to noise ratio,picture similarity index and compression ratio are all used to assess the quality of compressed *** the relevant transforms,the image size and bits per pixel are also *** the(n,n)block of transform,adaptive scanning is used to acquire the best feasible compression *** of these characteristics,multimedia systems and services have a wide range of possible applications.

关键词： Color image compression color spaces discrete orthogonal transforms(DOTs) peak-to-signal noise ratio(PSNR) similarity index

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