Advances in Technology Innovation

The increasing popularity of compact, chip-based devices has spurred interest in developing on-chip antennas (OCAs). However, OCAs suffer from low gain and poor radiation efficiency due to the silicon substrate’s low resistivity and high permittivity, influencing antenna performance. To avert these challenges, this study aims to enhance an OCA’s gain and radiation efficiency by incorporating a partially reflective surface (PRS) into the antenna structure. The antenna is simulated using 3D CST software, and its performance is evaluated. To validate the simulation, an antenna prototype is fabricated using sputtering and chemical vapor deposition (CVD) technologies. The prototype demonstrates a peak gain of 2.14 dB and radiation efficiency of 72.2%, showing a 24.3% gain increase and a 16.25% efficiency increase compared to the design without PRS. Additionally, it achieves an impedance bandwidth of 0.63 GHz, making it suitable for WiMAX, RFIC, and Wi-Fi 6 applications.

None Ahmadu Girgiri

None Mohd Fadzil Ain

None Mohd Zamir Bin Pakhuruddin

None Bello Abdullahi Muhammad

None Abdullahi Sarkin Bauchi Mohammed

Enhanced Design of On-Chip Monopole Antenna Inspired by Partially Reflective Surface at 5.8 GHz

5.8 GHz部分反射表面启发的片上单极天线增强设计

This study aims to leverage a promising avenue for the precise and early detection of Autism. Autism is a multifaceted neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Traditional diagnosis relies on time-consuming behavioral assessments, necessitating reliable and non-intrusive biomarkers for early and accurate detection. This paper analyzes eleven linear and non-linear features across time and frequency domains from an EEG dataset. Four neural network models, such as convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM), and a custom neural network are employed for classification. The CNN achieves the lowest accuracy at 89.02%, while the custom neural network reaches the highest accuracy at 94.02%, and the DNN and LSTM achieve 91.98% and 93.83% accuracy, respectively. Other metrics such as precision, recall, specificity, and F1-score, are also evaluated. This research underscores the efficacy of neural network in detecting Autism, advancing diagnostic tools.

None Nazmul Hasan

None Priyasha Paul

None Manisha Jitendra Nene

Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data

使用EEG数据进行自闭症检测的神经网络模型性能评估

This research proposes a method that randomizes the subcarrier as a physical layer security (PLS) in the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) communication system, aiming to secure information data. The research procedure incorporates the Elliptic Curve Cryptography (ECC) algorithm during subcarrier randomization, including processes such as public key generation, encryption, and decryption, and compares these with the Rivest Shamir-Adleman (RSA) method. The proposed method is validated through real-time laboratory experiments, yielding significant results. The RSA algorithm’s average time is 6.73 and 53.21 seconds, while the ECC algorithm requires only 0.71 and 1.21 seconds for security bits of 80 and 112, respectively. The performance of bit error rate (BER) versus signal-to-noise ratio (SNR) is 0.123 times 10 to the power of negative 3, demonstrating that the subcarrier randomization and reconstruction system is successfully implemented and working correctly to ensure security based on the MIMO-OFDM system.

None I Gede Puja Astawa

None Melki Mario Gulo

None Amang Sudarsono

A Secure and Robust Data Transmission for 2 × 2 MIMO-OFDM System Using Subcarrier Randomization with Elliptical Curve Cryptography

使用椭圆曲线加密的子载波随机化实现2×2 MIMO-OFDM系统的安全稳健数据传输

Wireless networks manage performance by adjusting the contention window, as they cannot directly detect collisions. Traditional contention window adjustment algorithms, such as the Binary Exponential Backoff (BEB) algorithm, may lead to lower throughput when multiple services with varying bandwidth demands coexist. To address this issue, this study aims to enhance network throughput by enabling differentiated bandwidth allocation for various services. Using deep reinforcement learning, the state space, action space, and reward functions are defined to optimize this differentiation. These definitions are integrated into the Deep Deterministic Policy Gradient (DDPG) technique, implemented in the Access Point (AP) to intelligently adjust the contention window. Leveraging DDPG’s capability for continuous actions, the proposed method provides Quality of Service (QoS) differentiation, ensuring that each service at its respective priority level meets its transmission requirements. Compared to the BEB algorithm, the proposed approach offers improved traffic allocation and higher network bandwidth utilization.

None Ming-Chu Chou

None Guang-Jhe Lin

None Chih-Heng Ke

None Yeong-Sheng Chen

Differentiated QoS Provisioning in Wireless Networks Based on Deep Reinforcement Learning

基于深度强化学习的无线网络差异化服务质量保障

Early detection of cardiac risk is crucial for accurate diagnosis and treatment of fatal cardiovascular diseases. Selecting relevant features is essential for machine learning in building an effective decision support system of cardiovascular risk assessment, ensuring accuracy of high-dimensional data. This study aims to propose a novel hybrid feature selection approach, termed ant colony optimization with hill climbing (ACOHC), integrating ant colony optimization (ACO) and hill climbing (HC) algorithms. The accuracy metric and various classifiers are deployed to evaluate the effectiveness. Additionally, comparisons are made with nine alternative feature selection techniques. The feature subset identified through the ACOHC attains a classification accuracy of 95.1% with the support vector machine classifier.

None Ankush Hutke

None Jyoti Deshmukh

A Novel Hybrid Approach for Feature Selection in Cardiovascular Risk Assessment

心血管风险评估中特征选择的新型混合方法

This research aims to develop a photovoltaic (PV) project assessment method by integrating four simulation tools to maximize potential benefits from multidimensional scopes of projects. The proposed method combines output parameters and the cost databases of selected tools to overcome individual limitations by facilitating complementary strengths. Most simulations require more analytical results while using single or multiple tools separately. Also, it combines HelioScope, RETScreen, HOMER, and PVsyst software to simulate entire generation export, self-consumption, and impact of load shedding with sensitivity analysis. The method employs the capability of HelioScope to find maximum installation capacity based on available space, the carbon-trading feature of RETScreen, HOMER’s optimization, and PVsyst’s viability analysis. The results demonstrate that carbon trading shortens the project’s payback period while maximizing installation capacity and performance improvement by energy export with a stable capacity factor and performance ratio. The method proffers a promising technique for PV system assessment.

None Gour Chand Mazumder

None Sanjay Kumar Sarker

None Tamim Hossain

None Md. Shahariar Parvez

None Md. Rifat Hazari

None Chowdhury Akram Hossain

None Md. Saniat Rahman Zishan

None Nowshad Amin

Integration of Multiple Simulation Tools for Photovoltaic System Design and Analysis

光伏系统设计与分析中多仿真工具的集成

Publication Information	Publisher: Taiwan Association of Engineering and Technology Innovation，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2020，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Average review cycle	网友分享经验：12 Weeks

Advances in Technology Innovation

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Enhanced Design of On-Chip Monopole Antenna Inspired by Partially Reflective Surface at 5.8 GHz

Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data

A Secure and Robust Data Transmission for 2 × 2 MIMO-OFDM System Using Subcarrier Randomization with Elliptical Curve Cryptography

Differentiated QoS Provisioning in Wireless Networks Based on Deep Reinforcement Learning

A Novel Hybrid Approach for Feature Selection in Cardiovascular Risk Assessment

A Secure and Robust Data Transmission for 2 × 2 MIMO-OFDM System Using Subcarrier Randomization with Elliptical Curve Cryptography

Differentiated QoS Provisioning in Wireless Networks Based on Deep Reinforcement Learning

A Novel Hybrid Approach for Feature Selection in Cardiovascular Risk Assessment

Integration of Multiple Simulation Tools for Photovoltaic System Design and Analysis

Performance Evaluation of Neural Network Models for Autism Detection Using EEG Data

ISSN	2415-0436, 2518-2994
h-index	8