World Academy of Science, Engineering and Technology

Authors: Abdulrahman Yarali, Machica McClain

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In the context of the increasing use of Cyber-Physical Systems (CPS) across critical infrastructure sectors, this paper addresses a crucial and emerging topic: the integration of Identity and Access Management (IAM) with Internet of Things (IoT) devices in Medical Cyber-Physical Systems (MCPS). It underscores the significance of robust IAM solutions in the expanding interconnection of IoT devices in healthcare settings, leveraging AI, ML, DL, Zero Trust Architecture (ZTA), biometric authentication advancements, and blockchain technologies. The paper advocates for the potential benefits of transitioning from traditional, static IAM frameworks to dynamic, adaptive solutions that can effectively counter sophisticated cyber threats, ensure the integrity and reliability of CPS, and significantly bolster the overall security posture. The paper calls for strategic planning, collaboration, and continuous innovation to harness these benefits. By emphasizing the importance of securing CPS against evolving threats, this research contributes to the ongoing discourse on cybersecurity and advocates for a collaborative approach to foster innovation and enhance the resilience of critical infrastructure in the digital era.

Keywords: CPS, IAM, IoT, AI, ML, authentication, models, policies, healthcare

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Authors: Abdulrahman Yarali

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The advancement of technology never stops because the demands for improved internet and communication connectivity are increasing. Just as 5G networks are rolling out, the world has begun to talk about the sixth-generation networks (6G). The semantics of 6G are more or less the same as 5G networks because they strive to boost speeds, machine-to-machine (M2M) communication, and latency reduction. However, some of the distinctive focuses of 6G include the optimization of networks of machines through super speeds and innovative features. This paper discusses many aspects of the technologies, architectures, challenges, and opportunities of 6G wireless communication systems.

Keywords: 6G, characteristics, infrastructures, technologies, AI, ML, IoT, applications

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Authors: Tai-Hsing Lee, Zhe-Wei Lin

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This paper demonstrates an ultra-wide tuning VCO implemented by CMOS 0.18μm process technology. By employing the proposed technique of magnetic energy adjustment in the oscillator tank, our proposed VCO achieves a wide frequency tuning range of 69.46% from 0.9 GHz to 1.86 GHz. The phase noise at an operating frequency of 1.86 GHz is -110 dBc/Hz (Offset frequency=1MHz). Furthermore, it achieves an excellent FOMT of 190.03 dBc/Hz.

Keywords: VCO, Ultra-wide tuning, Frequency tuning range, phase noise, Magnetic energy adjustment

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Authors: Basak Can

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This paper introduces an iterative and weighted localization method that utilizes a unique cost function formulation to significantly enhance the performance of positioning systems. The system employs locators, such as Gateways (GWs), to estimate and track the position of an End Node (EN). Performance is evaluated relative to the number of locators, with known locations determined through calibration. Performance evaluation is presented utilizing low cost single-antenna Bluetooth Low Energy (BLE) devices. The proposed approach can be applied to alternative Internet of Things (IoT) modulation schemes, as well as Ultra WideBand (UWB) or millimeter-wave (mmWave) based devices. In non-line-of-sight (NLOS) scenarios, using four or eight locators yields a 95th percentile localization performance of 2.2 meters and 1.5 meters, respectively, in a 4,305 square feet indoor area with BLE 5.1 devices. This method outperforms conventional RSSI-based techniques, achieving a 51% improvement with four locators and a 52 % improvement with eight locators. Future work involves modeling interference impact and implementing data curation across multiple channels to mitigate such effects.

Keywords: lateration, least squares, Levenberg-Marquardt algorithm, localization, path-loss, RMS error, RSSI, sensors, shadow fading, weighted localization

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Authors: Rowan P. Martnishn

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During the execution of a National Endowment of the Arts grant, approximately 55 interviews were collected from professionals across various fields. These interviews were used to create deliverables – historical connections for creations that began as art and evolved entirely into computing technology. With dozens of hours’ worth of transcripts to be analyzed by qualitative coders, a quantitative methodology was created to sift through the documents. The initial step was to both clean and format all the data. First, a basic spelling and grammar check was applied, as well as a Python script for normalized formatting which used an open-source grammatical formatter to make the data as coherent as possible. 10 documents were randomly selected to manually review, where words often incorrectly translated during the transcription were recorded and replaced throughout all other documents. Then, to remove all banter and side comments, the transcripts were spliced into paragraphs (separated by change in speaker) and all paragraphs with less than 300 characters were removed. Secondly, a keyword extractor, a form of natural language processing where significant words in a document are selected, was run on each paragraph for all interviews. Every proper noun was put into a data structure corresponding to that respective interview. From there, a Bidirectional and Auto-Regressive Transformer (B.A.R.T.) summary model was then applied to each paragraph that included any of the proper nouns selected from the interview. At this stage the information to review had been sent from about 60 hours’ worth of data to 20. The data was further processed through light, manual observation – any summaries which proved to fit the criteria of the proposed deliverable were selected, as well their locations within the document. This narrowed that data down to about 5 hours’ worth of processing. The qualitative researchers were then able to find 8 more connections in addition to our previous 4, exceeding our minimum quota of 3 to satisfy the grant. Major findings of the study and subsequent curation of this methodology raised a conceptual finding crucial to working with qualitative data of this magnitude. In the use of artificial intelligence there is a general trade off in a model between breadth of knowledge and specificity. If the model has too much knowledge, the user risks leaving out important data (too general). If the tool is too specific, it has not seen enough data to be useful. Thus, this methodology proposes a solution to this tradeoff. The data is never altered outside of grammatical and spelling checks. Instead, the important information is marked, creating an indicator of where the significant data is without compromising the purity of it. Secondly, the data is chunked into smaller paragraphs, giving specificity, and then cross-referenced with the keywords (allowing generalization over the whole document). This way, no data is harmed, and qualitative experts can go over the raw data instead of using highly manipulated results. Given the success in deliverable creation as well as the circumvention of this tradeoff, this methodology should stand as a model for synthesizing qualitative data while maintaining its original form.

Keywords: B.A.R.T.model, keyword extractor, natural language processing, qualitative coding

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Authors: Seong Jun Yoon, Jasurbek Doliev, Sang Min Oh, Rodi Hartono, Kyoojae Shin

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Electric power steering (EPS), which is commonly used in electric vehicles recently, is an electric-driven steering device for vehicles. Compared to hydraulic systems, EPS offers advantages such as simple system components, easy maintenance, and improved steering performance. However, because the EPS system is a nonlinear model, difficult problems arise in controller design. To address these, various machine learning and artificial intelligence approaches, notably artificial neural networks (ANN), have been applied. ANN can effectively determine relationships between inputs and outputs in a data-driven manner. This research explores two main areas: designing an EPS identifier using an ANN-based backpropagation (BP) algorithm and enhancing the EPS system controller with an ANN-based Levenberg-Marquardt (LM) algorithm. The proposed ANN-based BP algorithm shows superior performance and accuracy compared to linear transfer function estimators, while the LM algorithm offers better input angle reference tracking and faster response times than traditional PID controllers. Overall, the proposed ANN methods demonstrate significant promise in improving EPS system performance.

Keywords: ANN backpropagation modelling, electric power steering, transfer function estimator, electrical vehicle driving system

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Authors: Mohammad Tawhid Kawser, Ishrak Kabir, Sadia Sultana, Tanjim Ahmad

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A promising service category of 5G, popularly known as Ultra-Reliable Low-Latency Communications (URLLC), is devoted to providing users with the staunchest fail-safe connections in the splits of a second. The reliability of data transfer, as offered by Hybrid ARQ (HARQ), should be employed as URLLC applications are highly error-sensitive. However, the delay added by HARQ ACK/NACK and retransmissions can degrade performance as URLLC applications are highly delay-sensitive too. To improve latency while maintaining reliability, this paper proposes the use of blind transmissions of redundancy versions exploiting the frequency diversity of wide bandwidth of 5G. The blind HARQ retransmissions proposed so far consider narrow bandwidth cases, for example, dedicated short range communication (DSRC), shared channels for device-to-device (D2D) communication, etc., and thus, do not gain much from the frequency diversity. The proposal also combines blind and ACK/NACK based retransmissions for different multiplexing options between time and frequency depending on the current radio channel quality and stringency of latency requirements. The wide bandwidth of 5G justifies that the proposed blind retransmission, without waiting for ACK/NACK, is not palpably extravagant. A simulation is performed to demonstrate the improvement in latency of the proposed scheme.

Keywords: 5G, URLLC, HARQ, latency, frequency diversity

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Authors: Mridula Korde

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The terahertz band is the main pillar of 6G wireless communication system. It is difficult to meet the high data rate of 1Tbps by millimeter frequency support systems. The terahertz band suffers huge propagation loss limiting wireless distance. Terahertz band imposes ultra massive multiple input multiple output antenna (UM-MIMO) systems which produce high array gain with narrow beamforming. The conventional methods for MIMO beamforming are Analog and Digital beamforming. The fully digital beamforming methods utilize dedicated structure of DAC/ADC and RF chains. These structures increase hardware complexity and are power hungry. The analog beamforming structures utilize ADC/DAC with phase shifters with less hardware complexity but support less data rates. As a result, a hybrid beamforming method can be adapted for UM-MIMO systems. This paper will investigate challenges in hybrid beamforming architecture which will address the low spatial degrees of freedom (SDoF) limitation in Terahertz (THz) Communication. The flexible hardware connections are proposed, in order to switch the system in an adaptive manner so as to minimize the power requirements.

Keywords: 6G, terahertz communication, beamforming, challenges

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Authors: Akinyemi Tomiwa

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The microstructure of rainfall is important for predicting and modeling various environmental processes, such as rainfall interception by vegetation, soil erosion, and radar signals in rainfall. This rain microstructure was studied with a vertically pointing Micro Rain Radar (MRR) located at a tropical location in Akure South West Nigeria (7o 15’ N, 5o 15’ E). This research utilizes two years of data (2018 and 2019), and the data obtained comprises rainfall parameters such as Rain rates, radar reflectivity, liquid water content, fall velocity and Drop Size Distribution (DSD) based on vertical profiles. The measurement and variations of rain microstructure of these parameters with heights for different rain types were presented from ground level up to the height of 4800 m at 160 m range gates. It has been found that the convective, stratiform and mixed, which are the three major rain types, have different rain microstructures at different heights and were evaluated in this research. The correlation coefficient and the regression line equation were computed for each rain event. The highest rain rate and liquid water content were observed within the height range of 160-4800. It was found that a good correlation exists between the measured parameters. Hence it shows that specific liquid water content increases with increasing rain rate for both stratiform and convective rain types in this part of the world. The results can be very useful for a better understanding of rain structure over tropical regions.

Keywords: rain microstructure, drop size distribution, rain rates, stratiform, convective.

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: Bahattin Kanal, Nursel Akçam

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Data transmission size and frequency requirements are increasing rapidly in electronic communication protocols. Increasing data transmission speeds have made the design of printed circuit boards much more important. It is important to carefully examine the requirements and make analyses before and after the design of the digital electronic circuit board. It delves into impedance matching techniques, signal trace routing considerations, and the impact of layer stacking on signal performance. The paper extensively explores techniques for minimizing crosstalk issues and interference, presenting a holistic perspective on design strategies to optimize the quality of high-speed signals. Through a comprehensive review of these design methodologies, this study aims to provide insights into achieving reliable and high-performance printed circuit board layouts for these signals. In this study, the effect of different design methods on Ethernet signals was examined from the type of S parameters. Siemens company HyperLynx software tool was used for the analyses.

Keywords: HyperLynx, printed circuit board, s parameters, ethernet

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Authors: Parham Bakhtiari

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This document follows the development of the prelude genre by analyzing specific pieces from a representative selection of composer from Bach to Shostakovich in every era. The research aims to prove the existence of an evolutionary axis in the genre of prelude's history, which is believed to be fading. In this research, Bach, Chopin, and Debussy's works are demonstrated and explored by creating a definition of the genre on his own terms and having an impact on future composers in the following generations. Taking into account, the educational aspect of the prelude and its connection to the genre of study, a brief conversation about it is also provided with an assessment of shorter versions of the genre, for instance, Chopin's preludes.

Keywords: music, piano, prelude, Bach, Chopin, Debussy

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Authors: Cornellia Ayu, Putrianti Herni, Saptoto Robertus

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In the context of global tourism and creative economies, digital maturity has become a crucial factor for the sustainable development of small and medium enterprises (SMEs). This paper explores the journey toward digital maturity among SMEs in Sleman, Yogyakarta, Indonesia, focusing on their empowerment to foster sustainable tourism and creative economy growth. The study adopts a mixed-methods approach, integrating qualitative interviews with SME owners and quantitative surveys to assess their digital capabilities and readiness. Data were collected from a diverse sample of SMEs engaged in various sectors, including crafts and culinary services. Findings reveal significant gaps in digital literacy and infrastructure, impeding the full realization of digital benefits. However, targeted interventions, such as digital training programs and the provision of affordable technology, have shown promise in bridging these gaps. The study concludes that enhancing digital maturity among SMEs is vital for their competitiveness and sustainability in the modern economy. The insights gained can inform policymakers and stakeholders aiming to bolster the digital transformation of SMEs in similar contexts.

Keywords: digital maturity, small medium enterprises, digital literacy, sustainable tourism, creative economy

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Ding Liangxiao

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The technological advancements in Micro-Electro-Mechanical Systems (MEMS) have significantly contributed to the development of new, flexible capacitive pressure sensors,which are pivotal in transforming wearable and medical device technologies. This study employs the sophisticated simulation tools available in COMSOL Multiphysics® to develop and analyze a MEMS-based sensor with a tri-layered design. This sensor comprises top and bottom electrodes made from gold (Au), noted for their excellent conductivity, a middle dielectric layer made from a composite of Silver Nanowires (AgNWs) embedded in Thermoplastic Polyurethane (TPU), and a flexible, durable substrate of Polydimethylsiloxane (PDMS). This research was directed towards understanding how changes in the physical characteristics of the AgNWs/TPU dielectric layer—specifically, its thickness and surface area—impact the sensor's operational efficacy. We assessed several key electrical properties: capacitance, electric potential, and membrane displacement under varied pressure conditions. These investigations are crucial for enhancing the sensor's sensitivity and ensuring its adaptability across diverse applications, including health monitoring systems and dynamic user interface technologies. To ensure the reliability of our simulations, we applied the Effective Medium Theory to calculate the dielectric constant of the AgNWs/TPU composite accurately. This approach is essential for predicting how the composite material will perform under different environmental and operational stresses, thus facilitating the optimization of the sensor design for enhanced performance and longevity. Moreover, we explored the potential benefits of innovative three-dimensional structures for the dielectric layer compared to traditional flat designs. Our hypothesis was that 3D configurations might improve the stress distribution and optimize the electrical field interactions within the sensor, thereby boosting its sensitivity and accuracy. Our simulation protocol includes comprehensive performance testing under simulated environmental conditions, such as temperature fluctuations and mechanical pressures, which mirror the actual operational conditions. These tests are crucial for assessing the sensor's robustness and its ability to function reliably over extended periods, ensuring high reliability and accuracy in complex real-world environments. In our current research, although a full dynamic simulation analysis of the three-dimensional structures has not yet been conducted, preliminary explorations through three-dimensional modeling have indicated the potential for mechanical and electrical performance improvements over traditional planar designs. These initial observations emphasize the potential advantages and importance of incorporating advanced three-dimensional modeling techniques in the development of Micro-Electro-Mechanical Systems (MEMS)sensors, offering new directions for the design and functional optimization of future sensors. Overall, this study not only highlights the powerful capabilities of COMSOL Multiphysics® for modeling sophisticated electronic devices but also underscores the potential of innovative MEMS technology in advancing the development of more effective, reliable, and adaptable sensor solutions for a broad spectrum of technological applications.

Keywords: MEMS, flexible sensors, COMSOL Multiphysics, AgNWs/TPU, PDMS, 3D modeling, sensor durability

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Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: Siminfar Samakoush Galougah

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In this paper, we study two power allocation problems for an uplink user-centric (UC) cell-free massive multiple-input multiple-output (CF-mMIMO) system. Besides, we assume each access point (AP) is connected to a central processing unit (CPU) via a fronthaul link with limited capacity. To efficiently use the fronthaul capacity, two strategies for transmitting signals from APs to the CPU are employed, namely, compress-forward estimate (CFE), estimate-compress-forward (ECF). The capacity of the aforementioned strategies in user-centric CF-mMIMO is drived. Then, we solved the two power allocation problems with minimum Spectral Efficiency (SE) and sum-SE maximization objectives for ECF and CFE strategies.

Keywords: cell-free massive MIMO, limited capacity fronthaul, spectral efficiency

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Authors: Huang Haoxin

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Large dark current at room temperature has long been the major bottleneck that impedes the development of high-performance infrared photodetectors towards miniaturization and integration. Although infrared photodetectors based on layered 2D narrow bandgap semiconductors have shown admirable advantages compared with those based on conventional compounds, which typically suffer from expensive cryogenic operations, it is still urgent to develop a simple but effective strategy to further reduce the dark current. Herein, a tellurium (Te) based infrared photodetector is reported with a specifically designed asymmetric electrical contact area. The deliberately introduced asymmetric electrical contact raises the electric field intensity difference in the Te channel near the drain and the source electrodes, resulting in spontaneous asymmetric carrier diffusion under global infrared light illumination under zero bias. Specifically, the Te-based photodetector presents promising detector performance at room temperature, including a low dark current of≈1 nA, an ultrahigh photocurrent/dark current ratio of 1.57×10⁴, a high specific detectivity (D*) of 3.24×10⁹ Jones, and relatively fast response speed of ≈720 μs at zero bias. The results prove that the simple design of asymmetric electrical contact areas can provide a promising solution to high-performance 2D semiconductor-based infrared photodetectors working at room temperature.

Keywords: asymmetrical contact, tellurium, dark current, infrared photodetector, sensitivity

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Authors: Yunpeng Xia, Jiajia Zha, Haoxin Huang, Hau Ping Chan, Chaoliang Tan

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Although the crystal phase of two-dimensional (2D) transition metal dichalcogenides (TMDs) has been proven to play an essential role in fabricating high-performance electronic devices in the past decade, its effect on the performance of 2D material-based flash memory devices still remains unclear. Here, we report the exploration of the effect of MoTe₂ in different phases as the charge trapping layer on the performance of 2D van der Waals (vdW) heterostructure-based flash memory devices, where the metallic 1T′-MoTe₂ or semiconducting 2H-MoTe₂ nanoflake is used as the floating gate. By conducting comprehensive measurements on the two kinds of vdW heterostructure-based devices, the memory device based on MoS2/h-BN/1T′-MoTe₂ presents much better performance, including a larger memory window, faster switching speed (100 ns) and higher extinction ratio (107), than that of the device based on MoS₂/h-BN/2H-MoTe₂ heterostructure. Moreover, the device based on MoS₂/h-BN/1T′-MoTe₂ heterostructure also shows a long cycle (>1200 cycles) and retention (>3000 s) stability. Our study clearly demonstrates that the crystal phase of 2D TMDs has a significant impact on the performance of nonvolatile flash memory devices based on 2D vdW heterostructures, which paves the way for the fabrication of future high-performance memory devices based on 2D materials.

Keywords: crystal Phase, 2D van der Waals heretostructure, flash memory device, floating gate

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Authors: Twinkle Sara Joseph

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Virtual reality technology has emerged as a revolutionary power that can transform the education sector in many ways. VR environments can break the boundaries of the traditional classroom setting by immersing the students in realistic 3D environments where they can interact with virtual characters without fearing being judged. Communication skills are essential for every profession, and studies suggest the importance of implementing basic-level communication courses at both the school and graduate levels. Interpersonal communication is a skill that gains prominence as it is required in every profession. Traditional means of training have limitations for trainees as well as participants. The fear of being judged, the audience interaction, and other factors can affect the performance of a participant in a traditional classroom setting. Virtual reality offers a unique opportunity for its users to participate in training that does not set any boundaries that prevent the participants from performing in front of an audience. Specialised applications designed in VR headsets offer a range of training and exercises for participants without any time, space, or audience limitations. The present study aims at measuring the effectiveness of VR training in improving interpersonal communication skills among students. The study uses a mixed-method approach, in which a pre-and post-test will be designed to measure effectiveness. A preliminary selection process involving a questionnaire and a screening test will identify suitable candidates based on their current communication proficiency levels. Participants will undergo specialised training through the VR application Virtual Speech tailored for interpersonal communication and public speaking, designed to operate without the traditional constraints of time, space, or audience. The training's impact will subsequently be measured through situational exercises to engage the participants in interpersonal communication tasks, thereby assessing the improvement in their skills. The significance of this study lies in its potential to provide empirical evidence supporting VR technology's role in enhancing communication skills, thereby offering valuable insights for integrating VR-based methodologies into educational frameworks to prepare students more effectively for their professional futures.

Keywords: virtual reality, VR training, interpersonal communication, communication skills, 3D environments

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Authors: Getaneh Berie Tarekegn

Abstract:

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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Authors: Koirala Abarodh, Maghaiya Ujjwal, Guragain Phani Raj

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This analysis focuses on defining the spectrum evaluation model for telecom operators in terms of total cost of ownership (TCO). A quantitative approach for specific case analysis research methodology was used for identifying the results. Specific input parameters like target User experience, year on year traffic growth, capacity site limit per year, target additional spectrum type, bandwidth, spectrum efficiency, UE penetration have been used for the spectrum evaluation process and desired outputs in terms of the number of sites, capex in USD and required spectrum bandwidth have been calculated. Furthermore, this study gives a comparison of capex investment for target growth with and without addition spectrum. As a result, the combination of additional spectrum bands of 700 and 2600 MHz has a better evaluation in terms of TCO and performance. It is our recommendation to use these bands for expansion rather than expansion in the current 1800 and 2100 bands.

Keywords: spectrum, capex planning, case study methodology, TCO

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Zunnurain Ahmad

Abstract:

This work presents design of a dual band antenna for the 5G communications in the millimeter wave band. As opposed to conventional patch antennas which are limited to single narrow band operation a shared aperture concept is utilized for this antenna. The patch aperture is coupled through two rectangular slots etched on a thin printed circuit board (100μm). The patch is elevated in air thus avoiding excitation of surface waves and minimizing dielectric losses at millimeter wave frequencies. With this approach the radiator can cover lower band of 28 GHz and upper band of 37/ 39 GHz dedicated for the fifth generation communications. The simulated radiation efficiency of the antenna stays above 90%.

Keywords: antenna, millimeter wave, 5G, 3D

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Authors: Fang Liu, Jia Jia Yao, Guan Lin Wu, Ren Jie Liu, Zhuang Guo

Abstract:

Leveraging plasma-assisted molecular beam epitaxy (PA-MBE) on c-plane free-standing GaN substrates, this work demonstrates high-performance AlN/GaN double-barrier resonant tunneling diodes (RTDs) featuring stable and repeatable negative differential resistance (NDR) characteristics at room temperature. By scaling down the barrier thickness of AlN and the lateral mesa size of collector, a record peak current density of 1551 kA/cm2 is achieved, accompanied by a peak-to-valley current ratio (PVCR) of 1.24. This can be attributed to the reduced resonant tunneling time under thinner AlN barrier and the suppressed external incoherent valley current by reducing the dislocation number contained in the RTD device with the smaller size of collector. Statistical analysis of the NDR performance of RTD devices with different AlN barrier thicknesses reveals that, as the AlN barrier thickness decreases from 1.5 nm to 1.25 nm, the average peak current density increases from 145.7 kA/cm2 to 1215.1 kA/cm2, while the average PVCR decreases from 1.45 to 1.1, and the peak voltage drops from 6.89 V to 5.49 V. The peak current density obtained in this work represents the highest value reported for nitride-based RTDs to date, while maintaining a high PVCR value simultaneously. This illustrates that an ultra-scaled RTD based on a vertical quantum-well structure and lateral collector size is a valuable approach for the development of nitride-based RTDs with excellent NDR characteristics, revealing their great potential applications in high-frequency oscillation sources and high-speed switch circuits.

Keywords: GaN resonant tunneling diode, peak current density, peak-to-valley current ratio, negative differential resistance

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Authors: Raunak Munjal, Mohammad Akif Ali, Prithiv Raj

Abstract:

This study investigates the relative effectiveness of various flight controllers for drone obstacle avoidance. To assess ultrasonic sensors' performance in real-time obstacle detection, they are integrated with ESP32 and Arduino Nano controllers. The study determines which controller is most effective for this particular application by analyzing important parameters such as accuracy (mean absolute error), standard deviation, and mean distance range. Furthermore, the study explores the possibility of incorporating state-driven algorithms into the Arduino Nano configuration to potentially improve obstacle detection performance. The results offer significant perspectives for enhancing sensor integration, choosing the best flight controller for obstacle avoidance, and maybe enhancing drones' general environmental navigation ability.

Keywords: ultrasonic distance measurement, accuracy and consistency, flight controller comparisons, ESP32 vs arduino nano

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

Abstract:

In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

Procedia PDF Downloads 62

Authors: Jiajia Yao, Guanlin Wu, Fang Liu, Junshuai Xue, Yue Hao

Abstract:

This study reports on the epitaxial growth of a GaN-based resonant tunneling diode (RTD) structure with stable and repeatable double negative differential resistance (NDR) characteristics at room temperature on a c-plane GaN-on-sapphire template using plasma-assisted molecular beam epitaxy (PA-MBE) technology. In this structure, two independent AlN/GaN RTDs are epitaxially connected in series in the vertical growth direction through a silicon-doped GaN layer. As the collector electrode bias voltage increases, the two RTDs respectively align the ground state energy level in the quantum well with the 2DEG energy level in the emitter accumulation well to achieve quantum resonant tunneling and then reach the negative differential resistance (NDR) region. The two NDR regions exhibit similar peak current densities and peak-to-valley current ratios, which are 230 kA/cm² and 249 kA/cm², 1.33 and 1.38, respectively, for a device with a collector electrode mesa diameter of 1 µm. The consistency of the NDR is much higher than the results of on-chip discrete RTD device interconnection, resulting from the smaller chip area, fewer interconnect parasitic parameters, and less process complexity. The methods and results presented in this paper show the brilliant prospects of GaN RTDs in the development of multi-value logic digital circuits.

Keywords: MBE, AlN/GaN, RTDs, double NDR

Procedia PDF Downloads 56

Authors: Jiajia Yao, Guanlin Wu, Fang Liu, Junshuai Xue, Yue Hao

Abstract:

As the production process of self-standing GaN substrates evolves, the commercialization of low dislocation density, large-scale, semi-insulating self-standing GaN substrates is gradually becoming a reality. This advancement has given rise to increased interest in GaN materials' homoepitaxial technology. However, at the homoepitaxial interface, there are considerable concentrations of impurity elements, including C, Si, and O, which generate parasitic leakage channels at the re-growth junction. This phenomenon results in leaked HEMTs that prove difficult to switch off, rendering them effectively non-functional. The emergence of leakage channels can also degrade the high-frequency properties and lower the power devices' breakdown voltage. In this study, the uniform epitaxy of AlGaN/GaN heterojunction with high electron mobility was accomplished through the surface treatment of the GaN substrates prior to growth and the design of the AlN isolation layer structure. By employing a procedure combining gallium atom in-situ cleaning and plasma nitridation, the C and O impurity concentrations at the homoepitaxial interface were diminished to the scale of 10¹⁷ cm-³. Additionally, the 1.5 nm nitrogen-rich AlN isolation layer successfully prevented the diffusion of Si impurities into the GaN channel layer. The result was an AlGaN/GaN heterojunction with an electron mobility of 1552 cm²/Vs and an electron density of 1.1 × 10¹³ cm-² at room temperature, obtained on a Fe-doped semi-insulating GaN substrate.

Keywords: MBE, AlGaN/GaN, homogenerous epitaxy, HEMT

Procedia PDF Downloads 60

Authors: Mahmut Yildirim

Abstract:

This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

Procedia PDF Downloads 63