Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Post-Synthesis Area, Delay, and Leakage Power of Standard Cell Based FPGA Tiles

The area, timing, and power characteristics of an FPGA tile are important metrics that should be measured accurately in order to accurately measure the performance of a chosen FPGA architecture. This work investigates the accuracy of classical statistical and machine learning models in predicting the post-synthesis cell count, cell area, net area, total area, worst-case delay and leakage power of 7 nm FinFET standard cell based FPGA tiles. We find that the prior mentioned metrics can be predicted with a maximum percentage deviation of less than 11%.
The Effect of E-Service Quality, Customer Experience, Price and Privacy on Customer Satisfaction on Telemedicine Application

During the pandemic, Covid-19, the level of public health awareness increased. Telemedicine has become one of the things all people are looking for to do consultations or purchase medicines and health products. This research aims to determine the effect of e-service quality, customer experience, price, and privacy on customer satisfaction of the user of Halodoc. The data collection method used by researchers was a questionnaire distributed to 180 Halodoc users through the application. Multiple Regression analysis was performed to examine the effect of e-service quality, customer experience, price, and privacy on customer satisfaction and to obtain significant results simultaneously using the F-test.
DeepTweet: Leveraging Transformer-based Models for Enhanced Fake News Detection in Twitter Sentiment Analysis

With the increase in the popularity of the social media sites, there arises lot of adverse effects including the spread of fake news. This research study proposes a novel algorithm, called DeepTweet, which leverages transformer-based models for enhanced fake news detection in Twitter sentiment analysis. The aim is to improve the accuracy and efficiency of identifying fake news in real-time by exploiting the contextual information and semantic representations captured by the transformer-based models. The proposed DeepTweet algorithm utilizes a pre-trained transformer model, such as BERT or GPT, to encode the textual content of tweets and extract rich contextual embeddings. These embeddings capture both local and global dependencies, enabling a more comprehensive understanding of the sentiment expressed in a tweet. Furthermore, a novel attention mechanism tailored for fake news detection is introduced to focus on key textual features that are indicative of potential misinformation. To assess the efficiency of DeepTweet, extensive simulations using a large dataset of labelled tweets containing both genuine and fake news are done. Further, this study compares the performance of DeepTweet with existing algorithms, including LSTM, CNN, and traditional machine learning approaches. Various simulation metric measures such as accuracy, precision, recall, and F1 score are employed to analyze the algorithm's performance. The simulation grades demonstrate that DeepTweet outperforms the prevailing algorithms by means of accuracy and overall detection performance. The proposed attention mechanism proves to be particularly effective in identifying subtle linguistic cues and deceptive patterns associated with fake news. The enhanced contextual embeddings captured by the transformer-based models contribute to improved sentiment analysis and subsequently lead to more accurate fake news detection.
Evaluation of a U-Shaped Convolutional Neural Network for RCS based Chipless RFID Systems

In this paper, for the first time, a one-dimensional convolutional neural network using a U-shaped architecture is evaluated in the context of radar cross section (RCS) based chipless RFID (CRFID) systems. A 3-bit CRFID tag is utilised to create eight discernible RCS signatures representing identification numbers. A dataset of 9,600 measured RCS signatures was utilised for training, validating, and testing the model. The dataset was collected by placing the tag on varying surface shapes, orientations, and read ranges to enable robust detection. The root mean square error (RMSE) metric was used to assess the model’s performance. The achieved RMSE was 0.11 (1.5%). The low RMSE score demonstrates the effectiveness that this type of architecture has in accurately detecting and generalizing the encoded information from the RCS signatures.
Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.
An Intelligent Occupancy Detection System for Smart Tourism based on RFID Passive Tag Antenna Array and Random Forest

This paper presents a passive UHF RFID (Ultra High-Frequency Radio Identification) tag array-based occupancy detection of smart tourism using the AI (Artificial Intelligence) method of (RF) Random Forest. To illustrate the randomness of occupancy in different scenarios, this paper compromises UHF RFID tag array sensing and AI computing into an open platform for occupancy detection in different scenarios. Our proposed system aims at predicting population density classification from 0% to 75%. To demonstrate the feasibility of our approach, we conducted a small-scale experiment using a carpet embedded with nine passive UHF RFID tags on average. We deployed two different tag placement patterns, Circular and Square, to assess their impact on classification accuracy. During the experiment, one person walked on the carpet, covering approximately 25% of its area. We assigned each participant to a corresponding occupancy class based on the carpet percentage covered in 4 classes, corresponding to 0, 1, 2, and 3 people walking on the carpet, e.g., class 1 represents 0 individuals, class 2 represents one person, and so on one at a time. We could accurately classify each participant into the appropriate occupancy class by analyzing the data collected from the RFID tags. The employed AI method of RF achieves high classification accuracy of 91.82%, which is much higher than other common classifiers of SVM and BP Neural Network.
Hybrid Harmony Search Based D-TIF Controller for Load Frequency Control of Multi Area Power System

The focus of the current effort is load frequency control (LFC), which aims to supply consumers with high-quality power with little variation in frequency and tie line power. Strong intelligent control mechanisms have been used to fine-tune the parameters of the suggested derivative with tilt integral filter controller (D-TIF) in order to achieve this goal. An illustrative and practical solution for the LFC of two area-interconnected power systems is obtained using a novel optimization technique known as the hybrid harmony search differential evolution algorithm (HHSDE). Workable models for the investigated power systems include nonlinearities such generator rate limitations, boiler dynamics, and governor dead band (GDB). In comparison to other current traditional systems like the proportional-derivative-integral (PID) controller and the tilt-integral-derivative (TID) controller, the recommended D-TIF controllers are more resilient. In terms of peak overshoot, integral time square error (ITSE), and settling time, the preferred D-TIF controller provides a better dynamic response. By contrasting the HHSDE algorithm with other algorithms that have been reported in the literature, its efficacy is demonstrated. To test the HHSDE algorithm's capabilities, the robust and penetrative behaviour of the upset power system is also examined.
FFT Based Angle Detection of Fiber Glass Layers

We propose an FFT based method to detect the orientation of fiber glass layers. To this end we apply the 2D FFT transform to the radar image data in surface orthogonal coordinates and apply a filtering in frequency space. Finally, we use a CFAR detector to calculate the relevant fiber directions. The method is verified by a test setup, where we use a sample with changing fiber orientation. We observe that the method is able to detect the correct fiber orientation angles up to an error of less than 0.1 degree.
LumiBench: A Benchmark Suite for Hardware Ray Tracing

Ray tracing as a graphics rendering method is becoming increasingly popular in real-time applications, supported by dedicated accelerator cores in the latest generation GPUs. However, the high computational intensity of the ray tracing algorithm still limits the visual effects that can be produced while maintaining a high frame rate. To improve ray tracing hardware, it is important to understand the underlying characteristics of ray tracing workloads and identify performance bottlenecks. In this paper, we present LumiBench, the first benchmark suite for evaluating ray tracing hardware performance in modern GPUs designed to execute on the Vulkan-Sim GPU simulator. LumiBench features a diverse set of scenes and shaders that are representative of real applications but simple enough to be simulated in a reasonable amount of time. We first evaluate LumiBench against Rodinia to highlight the difference between ray tracing versus general purpose workloads and demonstrate the need for a dedicated benchmark suite. Then, we characterize the workloads included in LumiBench, which are organized into several clusters targeting different aspects of the ray tracing pipeline, and provide insights for future architectural research.
Análise de trilha para carotenoides em milho

Path analysis for carotenoids in maize Although aspects of magnitude and significance are considered, the study of correlations between traits, alone, does not necessarily guarantee a cause and effect relationship between them. Therefore, the objective of this study was to use path analysis to allow the unfolding of correlations in direct and indirect effects, considering the carotenoids profile of maize genotypes. Data was obtained from the national assay of maize cultivars conducted by Embrapa Maize and Sorghum in the 2004/2005 growing season, with means of ten genotypes in five environments. The contents of total carotenoids (CT), alpha and -carotene, lutein, zeaxanthin and -criptoxantin were evaluated. Xanthophyll zeaxanthin provided the highest direct effect on -carotene. The high correlations between -carotene and total carotenoids and between -carotene and -criptoxanthin are due the indirect effect via zeaxanthin. The direct selection of genotypes with high levels of -carotene appears as a more effective alternative, but if other fractions of carotenoids were also considered, methods of simultaneous selection of characters using selection indices are more efficient in
Increased ranging accuracy of RFID tags in ETSI regions using 53MHz wide dual bands

Passive RFID tag localization has numerous applications, which can benefit from improved accuracy. Ranging methods based on phase difference of arrival become more accurate with higher frequency bandwidth. European regions, following ETSI regulation, historically allowed a single ultra-high frequency band (865–868 MHz) with limited accuracy. Recently allowed, the second band (915–921 MHz) increases the frequency range from 1.8 MHz to 53 MHz, but with a significant gap between the bands. In a dual-band experiment, distance estimates were obtained between two reader antennas and 17 tags across 6 arrays. The results revealed a new 3.07 m ambiguity cycle due to the frequency gap, that adds to the existing 0.17 m phase ambiguity. Solving the new ambiguity on 88% of tag-antenna combinations and 100% of the arrays resulted in a maximum error of 0.55 m (median 0.26 m), significantly improving accuracy (×6.5) compared to the former single-band ranging. Looking ahead, the adoption of dual-band ranging could enhance the accuracy of various tag localization methods in ETSI-regulated regions.
Improved Indoor Semi-Static Human Target Detection and Localization using FMCW Radar

This paper presents a novel processing approach for semi-static human indoor perception and localization with Frequency modulated continuous wave (FMCW) radar. When analyzing semi-static targets with FMCW radar, minimal phase variations of standing or sitting human targets in slow-time can result in missed detections under traditional processing schemes. The common approach of studying micro-Doppler signatures over an extended period of time can, however, be costly in terms of both memory and compute resources. In this work, we introduce an approach with low memory and computational footprint, which relies on signatures beyond micro-Doppler for a novel presence score. We show on real-world recordings how this maximal variance based presence score can bring improvement in detection of static humans. To further increase detections in the range angle space, we introduce a novel angle of arrival estimation by creating a synthetic spatial signal from all phase differences between antennas over time. We show a 22.9% gain in human detection performance on a large dataset of humans in semi-static applications and highlight the reduction in computational complexity of both of these concepts.
Real-time Prostate Cancer Detection via YOLO-Tiny Variants

Prostate cancer (PCa) stands as the most prevalent cancer diagnosis and ranks as the second leading contributor to cancer-related mortality among men worldwide. This research introduces a swift and accurate detection technique based on YOLO tiny models for identifying aberrant prostate cancer cells. With the use of this approach, aberrant cells can be promptly and precisely located and identified, enabling timely intervention and improved patient outcomes. Moreover, in pursuit of realizing an integrated deep prostate cancer detection system (PCDS), the proposed framework leverages the Nvidia Jetson Nano developer kit to evaluate the real-time efficacy of YOLO tiny detectors. The investigation draws upon a dataset comprising 3585 instances for both training and testing phases. Experimental results demonstrate that YOLO v8s achieves a mean average precision (mAP) of 99%, Precision of 99.5%, Recall of 99% and F1-score of 99.2% which indicates the effectiveness of this approach in achieving real-time prostate cancer detection thus holding great promise for enhancing medical diagnostics. When considering single-image predictions, the inferred time on the edge computing device showcases slight variations, ranging from 128ms with YOLO v5s to 32ms using YOLONASs.
Fracture detection from X-ray images using different Machine Learning Techniques

In this period, X-rays are the main instruments used to examine suspected human fractures. It takes a lot of time to manually examine X-rays, which calls for experienced radiologists or skilled orthopedic surgeons. The primary care physicians' excessive referrals contribute to the challenging diagnostic environment and the shortage of radiologists at primary/community health centre. Additionally, the lack of skilled radiologists and orthopedic surgeons in medically underdeveloped regions, such as rural India, has driven us to create an automated fracture identification model. Digital image processing is a technique of processing an image with the help of a digital computer. In computer image processing, an edge detection algorithm looks for any kind of edge modifications. In this work, edge detection is used to first identify edges, and it is discovered that canny edge detection is the best option. In second stage, HOG feature extraction method is used to extract features from X-ray images. The images are then categorise using classifiers to distinguish between bones with and without fractures. For classification, the classifiers like SVM (Support Vector Machine), KNN (K-Nearest Neighbour), and RF (Random Forest) are utilised. This study strongly suggests SVM for bone fracture detection.
DC Voltages Regulation for Renewable Sources using Machine Learning

Renewable energy sources stand out as a chance for a sustainable future due to many problems. One significant kind of renewable energy power system is solar energy storage systems. Renewable energy generation is inherently volatile and may include unacceptably large fluctuations. Therefore, to use renewable energy later, it is now stored in batteries or super capacitors, but this demands a hefty maintenance charge. The dependability assurances and pliability of the interconnected micro grid are greatly strengthened when relatively many micro grids that are geographically close to one another are all connected through a single distribution line. The key objective of the suggested model is to regulate renewable energy during fluctuations to provide a steady supply of electricity, so the solar panel power generation and load usage will be monitored continuously, and send these values to a machine learning model to categorize the switching mode of the voltage stabilizer.
KALMAN FILTER APPLIED TO A DIFFERENTIAL ROBOT

This document presents the localization problem and the trajectory that a robot must follow. It focuses on applying the Kalman filter to achieve localization and trajectory estimation in an autonomous mobile differential robot. The experimental data was obtained through tests conducted by using two incremental encoders that are part of the instrumentation of the differential robot. Data transmission is carried out from a PC, where control is performed using Matlab/Simulink software. The results are presented in graphs showing the robot's trajectory using PI control and the Kalman filter estimator in a real scenario.
State Space Formation and Fuzzy Logic Control Based Performance Analysis of DC Motor Drives

In this paper, a state space canonical form of DC motor is designed mathematically and then simulated in MATLAB Simulink. For a stable DC motor drive operation, a closed loop is required which accompany DC motor, controller, chopper and sensors. Further, in this manuscript a Fuzzy logic controller (FLC) is designed using fuzzy toolbox of MATLAB. Later, the canonical form of DC motor along with FLC and other major components of closed loop system a simulation is carried out with a load. Performance of this closed loop is observed on two cases. First, when the inner closed loop is unity and next is with complete closed loop system. Speed response for both cases is observed along with load. Time domain analysis parameters for these two cases are calculated and compared too. Comparison analysis shows the effectiveness and usefulness of the designed fuzzy logic controller with the closed loop DC motor drive.
A Non-Linear Transmission Line with Secondary Soliton Decimation

In this paper, we present the design of a nonlinear transmission line (NLTL) that decimates the secondary soliton using a geometric tapering technique while maintaining the Gaussian pulse shape of the input. The experimental results demonstrate that this NLTL sharpens an input pulse with a 1.8 GHz 20-dB bandwidth to a 4.40 GHz bandwidth with a secondary soliton that is only 10% of the peak pulse amplitude.
Dispositions as an Important Component in Computing Curriculum in IT Del

Insitut Teknologi Del (IT Del), formerly recognized as Politeknik Informatika Del (PI Del), has consistently maintained its emphasis on the computing curriculum, establishing it as the institution's primary domain of expertise since its founding. Being recognized as a campus with its high employability rate, this paper aims to describe how IT Del develops its computing curriculum in connection to its involvement in the Decart project. This paper first presents the Indonesia curriculum guideline. This is followed by the discussion of the development process of computing curriculum emphasizing three components: knowledge, skills, and dispositions. The last component is believed to be one of the important components aimed to prepare graduates to have 21st century skills. Then we relate them to the involvement of IT Del in the DECART project, funded by Erasmus Mundus. The purpose of this paper is to demonstrate that our existing curriculum development approach has been successful to build expected graduates' competence. This is expected to give an insight to define an agile curriculum framework to face the VUCA world as one of the objectives for the DECART project.
A Comparative Analysis of Multicarrier Waveforms for Future Wireless Communications

Beyond Orthogonal Frequency Division Multiplexing, there has been a lot of study done since there is a requirement for enhanced waveforms in 5G networks. For 5G, many filtered multi-carrier transmission systems, such as filter-OFDM and filter bank multicarrier modulation, have been suggested, each with its own set of advantages and disadvantages. This article seeks to give a thorough comparative study of different waveforms, allowing for a comparative evaluation of their performance on an overall basis. The study comprehensively assesses various performance metrics, including peak-to-average power ratio, out-of-band leakage power, bit error rate over wireless channels, the impact of carrier frequency offset, and spectral efficiency. These findings will be useful in determining which waveform to use for the future generation of mobile communication systems.
Sustainable Development Goals in the ICT Sector

The United Nations' Sustainable Development Goals (SDGs) set the Agenda for 2030 to foster prosperity for people and the planet. Since their introduction in 2015, the SDGs have gained political and corporate traction. Several studies have analysed how companies report on their impacts on the goals, but insights on how specific industries contribute towards the SDGs are yet to be obtained. Hence, our work focuses on how companies in the Information and Communications Technology (ICT) sector contribute to the SDGs. We have investigated what methods exist to rigorously assess the SDG performance of companies since such methods provide guidance during the assessments and mitigate the risk of greenwashing. Next, we have analysed sustainability reports of the 23 biggest ICT companies to find out how these companies report on the SDGs. Moreover, we have compiled a comprehensive overview to show which SDGs receive attention in the ICT sector and what activities the businesses undertake to realise the goals. We have also presented how many of these activities can be verified by finding evidences such as publicly available data. Interestingly, we have found that none of the 23 companies uses any of the methods specifically engineered to assess SDG contributions, instead, they apply tailor-made methods. By highlighting the state of the art on SDG assessment methods and uncovering the state of practice in SDG reporting by top companies, we hope to inspire ICT companies to improve their efforts in realising Agenda 2030 and open new research avenues on the application of the SDG framework within (and outside) the ICT sector.