Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the Accuracy of Tram Positioning System in High-Rise Building Environment Using Data from Visual Geoinformation Systems

This article describes an approach to evaluating the localization accuracy of a tram moving in an urban environment. The tram needs to be localized with a submeter level of accuracy. Since GPS information in urban environment does not provide such a level of accuracy, a solution based on the use of vision system information is proposed. By using keypoints, it is possible to estimate the motion of an object between images taken in different route passes, whereas this motion can also be calculated using the data from the on-board navigation system. The comparison of these motions will enable the assessment of the accuracy of the navigation system on board. The article provides the accuracy values obtained, as well as a description of the experiment conducted to evaluate the accuracy of the navigation algorithm.
Probabilistic model for minimizing delay in Vehicular Networks

A roadside unit (RSU) is a communication device that is statically positioned along the side of the road in a vehicular ad-hoc network (VANET). It facilitates vehicle to infrastructure (V2I) communication between vehicles and network infrastructure. The transmission delay of a VANET greatly depends upon the relative placement of RSUs along the roadside. However, an unoptimal position of RSUs along the roadside may result in failing vehicular network efficiency. Motivated by this, we investigate the optimal RSU placement problem within the network topology and propose a scheme that reduces transmission delays in VANET. In this context, a probabilistic model delay reduction RSU placement (DRP) to minimize transmission delays has been developed. SUMO is used to create the traffic mobility patterns, and then the suggested method was simulated on NS3. Results show that the transmission delays were reduced by 8% when the DRP model was compared to the other benchmark RSUs placement scheme.
Research on UAV Formation Obstacle Avoidance Based on Consistency Control

Based on the graph theory, multi-UAV form a formation through the leader-follower method. The control command is sent to the leader UAV, and the formation of UAVs is led by the leader to the target location and avoids obstacles through an improved artificial potential field method, and the formation is controlled by a consistency protocol to keep the formation stable during the flight. If the leader UAV is balanced by forces at a certain place when it does not reach the target point, a random disturbance is triggered to make it jump out of the local minimum. Through the formation consistency obstacle avoidance algorithm, the formation of UAVs is kept constant during the obstacle avoidance process to achieve formation position consistency control. Finally, the effectiveness of the algorithm is verified by MATLAB simulation.
Modeling Directional Overcurrent Relays using Modelica

Phase direction and over current elements are relied upon to provide fast and Reliable protection throughout the power system. Although directional relays have been successfully applied to many years, but the System disruptions present the most challenges. The directional relay model is implemented in the Modelica language in this study. The modules will be merged into the final relay model after being individually tested, and that model will then be put through several fault simulations. Thereafter, the results obtained from this model will be compared with other obtained from a previously prepared model using MATLAB. Finally, a sample power network was implemented in order to evaluate the relays performance in a system.
Cascaded Generalized Extended State Observer-Based Control for Servo Systems With Matched and Mismatched Disturbances

This paper proposes a cascaded generalized extended state observer-based control (CGESOBC) implementation scheme for a class of nonlinear servo systems with nonintegral-chain form and multiple matched and mismatched disturbances. In this approach, the total disturbances in each channel are reconstructed by designing a GESO. A reference model is developed with the estimated disturbances and the reference input, together with a state tracking error model containing the multiple residual disturbances. Another GESO is then devised to estimate the primary estimation errors, based on which a state feedback control law incorporating a dynamic compensator is formulated for robust stabilization of the state tracking error system. Moreover, the Lyapunov stability theory is applied to prove the bounded stability of the closed-loop system. Finally, the efficacy of the proposed control method is verified by a numerical example.
A Novel Approach for E-Commerce System for Sale Prediction with Denoised Auto Encoder and SVM based Approach

E-commerce, or online shopping, has grown in prominence over the past few years thanks to the proliferation of the Internet. Yet, there are a lot of things that can affect an online store’s success, and if the operators don’t correctly assess their supply and marketing partnerships, they could lose a lot of money. Thus, it is crucial to create a model that can reliably produce high precision sales prediction in order to guarantee the long-term success of e-commerce businesses. The suggested method comprises three stages: preprocessing, Feature selection, and model training. This proposed approach uses zero-phase component analysis and normalization in the preprocessing phase to get rid of noise and inconsistent data. Finally, the model is trained with DAE-SVM after information gain is employed for feature selection. When compared to convolutional neural network and support vector machine models, the proposed model excels.
Design and Analysis of a Graded-Index Strained Si $_{1-x}$ Ge $_x$ Optical PN Phase Shifter

In this paper, a graded-index strained Si $_{1-x}$ Ge $_x$ optical PN phase shifter with multiple strained layers has been proposed for high speed data modulation at 1550 nm with low device footprint. The quasi-vectorial finite difference method, non-local empirical pseudopotential method and the Payne-Lacey model has been used to analyze the proposed device. A phase shift of 122 $^\circ$ /mm, V $_\pi$ L $_\pi$ of 0.74 V.cm, and insertion loss of 4 dB at 5 V reverse bias with a 3-dB modulation bandwidth of 27.5 GHz has been obtained for a 500 nm × 250 nm cross-section rib waveguide with 50 nm slab. A 15 $^\circ$ /mm increase in phase shift and 0.08 V.cm decrease in V $_\pi$ L $_\pi$ has been observed when the junction is shifted by 60 nm towards the N side.
Optimization for Power Supplies Thermal Regime

The paper analyses the thermal flow on a power transistors stage inside an operational AC-DC supply source. The study is done to optimize power AC-DC operation, used as a battery charger, digital systems supply and so on. optimization is accomplished by a synergically approach between thermal measurements and thermal modeling. The experimental measurements with the IR precision image thermal camera FLIR SC640 give an appropriate view of the main heat dissipation A thermal solver, using detailed electrical and thermal information offers the designer an optimization of the power stage heat dissipation to achieve maximum system performance. The study aims to enlarge the power range of this type of versatile power AC-DC source by an optimized thermal operation design. Co-simulation also enables the designer to ensure for prototypes a short time analysis for the system. The results are interpreted for good reliability of the device.
Dense Point Cloud Reconstruction Based on a Single Image

How to recover high-resolution 3D point clouds from a single image is of great importance in the field of computer vision. However, due to the limited information contained within a single image, reconstructing a dense point cloud of objects from a single image is a highly challenging task. In this paper, we construct a multi-stage dense point cloud reconstruction network that incorporates a coordinate attention mechanism and a point cloud folding operation. The proposed network model comprises two parts: an image-based sparse point cloud generation network and a folding-based dense point cloud generation network. Firstly, we generate a sparse point cloud by extracting the features of the target object from a single image using an image-based sparse point cloud generation network. Then we use the folding-based dense point cloud generation network to density the generated sparse point cloud. Finally, the two stages are combined by deep learning fine-tuning techniques to form an end-to-end dense point cloud reconstruction network that generates a dense point cloud from a single image. By evaluating the synthetic datasets, the proposed method effectively reconstructs the dense point cloud model of the corresponding object and outperforms existing methods in terms of metrics. Meanwhile, our method also performs well on real-world datasets.
Integrating Cloud and Fog Technologies with IoT to Create Smart Agriculture

Not long ago, there was a move from traditional farming to smart farming, which depended on data and communication technologies to boost crop output and quality. One of the main technologies that have contributed to the existence of smart agriculture is the Internet of Things (IoT). The Internet of Things technology faced many challenges, so cloud computing was proposed as a complement, yet there were some shortcomings. Fog computing technology was introduced to overcome the shortcomings of integrating the Internet of Things and cloud technology. This paper defines and studies IoT, cloud and fog technologies. They are utilized to implement a smart agricultural system consisting of three layers interacting with each other to manage, monitor, and control the agricultural environment. The implemented system has been deployed and tested on a small farm, which can be controlled remotely to observe the humidity of the soil and notify the user of any hazard, such as fire.
Magnetic Adsorption Non-destructive Testing Vehicle Based on TOFD and Rainflow Counting by Neural Network Technology

We innovatively applied neural network technology to the field of non-destructive testing of steel pipelines. According to the survey, there has been a serious gap in the development of intelligence and automation in the field of non-destructive testing of steel pipelines for a long time. The defect detection of the surface of the pipelines for natural gas and oil and wind turbine main poles is conducted manually with problems of low detection accuracy, high risk, and high work intensity. Manual work is easy to miss pipeline damage, causing safety accidents and leaving a safety hazard for production. Pipeline defect detection requires several hours of high-intensity work, and defect detection workers have huge work intensity. The proposed system automatically performs non-destructive defect detection on pipelines. The cloud platform displays ultrasonic images of defect detection in real time, automatically analyzes and saves records of the recognition results during the process, and finally, a defect detection report is generated on the cloud platform.
Noise-Separated Adaptive Feature Distillation for Robust Speech Recognition

This letter makes an improvement on feature-based knowledge distillation for robust speech recognition. The use of distillation techniques in speech recognition has been demonstrated to improve the robustness of the system. In this letter, we propose a noise-separated adaptive feature distillation method, including an adaptive distillation position selection strategy and a noise separation mechanism, assuming that there is a common network structure between the student and teacher. The proposed method has two improvements. First, distillation positions can be adaptively selected in each iteration by comparing loss values computed on intermediate representations of the student and the teacher, increasing the flexibility of knowledge transfer during distillation. Second, a noise separation module is proposed to constrain noise information elimination by explicitly separating the speech information and the noise information in noisy speech, which reduces the interference of noise information during distillation. Therefore, a better recognition performance is demonstrated with the proposed method compared to the standardized feature-based knowledge distillation method.
Simulation Study of the Breakdown Performance for Vertical GaN PiN Diode with Mesa-terminals and Field-plate Structures

In this work, we have used Silvaco TCAD to study the electric-field profile of vertical GaN-based PiN diode mesa terminal structures when avalanche breakdown occurs at different etch depths, especially near the device edge. The negative angle mesa structures of vertical GaN-PiN diodes with different angles were also studied, and the surface electric-field and breakdown voltage at PN junction-depletion region interface were analysed. Finally, a vertical GaN-PiN diode structure with field-plate at an etching depth of T=0.5 was studied. The electric-field profile and breakdown voltage at the depletion region edge with a Si3N4 passivation layer and a field-plate structure were analysed, as well as the negative angle (θ=40°) mesa and the field-plate structures.
Advanced Sentiment Analysis

Sentiment analysis determines the sentiment polarity towards a given document, sentence or aspect. The output of such classification gives us the information about emotional tone of given text. In this thesis, we perform several experiments and we report results of sentencelevel and document-level sentiment analysis, aspect category detection, and aspect category sentiment analysis. We also discuss the theoretical background of such methods and review state-of-the-art models and datasets. Even though our models achieve worse results compared to stateof-the-art, the common advantage of our approaches is low time and space complexity.
Verifying Full Regular Temporal Properties of Programs via Dynamic Program Execution

Verification of programs at code level has attracted more and more attentions since the cost is high to extract models from source code. Most of approaches available for code level verification are carried out by inserting assertions into programs and then checking whether the assertions are violated. In this way, only safety properties can be verified, however, other temporal properties of programs such as liveness are hard to be verified. To tackle this problem, a novel runtime verification approach, which can verify full regular temporal properties of a program, is proposed in this paper. With this approach, a program to be verified is written in a modeling, simulation and verification language (MSVL) as a program <monospace>M</monospace> and a desired property is specified by a propositional projection temporal logic formula <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula>. The negation of the desired property is then translated to an MSVL program <monospace>M’</monospace>. Thus, whether <monospace>M</monospace> violates <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> can be checked by evaluating whether there exists an acceptable execution of the new MSVL program “<monospace>M and M’</monospace>. ” This problem can efficiently be solved with the MSVL compiler where verification cases are generated via dynamic symbolic execution. Further, we adopt parallel mechanism to handle various execution paths of a program for improving the efficiency. The proposed approach has been implemented in a tool called <italic>MSV</italic>. Experiments show that the performance of <italic>MSV</italic> outperforms existing tools such as T2, RiTHM, and LTLAutomizer in verifying temporal properties of real-world programs.
Efficient Pomegranate Segmentation with UNet: A Comparative Analysis of Backbone Architectures and Knowledge Distillation

This work examines the segmentation of on-field images of pomegranate fruit using UNet model with different backbones. Precise and effective segmentation of pomegranate fruits on the field is essential for automating yield estimation, disease detection, and quality evaluation in the agricultural industry. The models have been trained and validated using actual images captured in a pomegranate field. The study assesses the performance of many backbones, including ResNet50, Inception ResNetV2, MobileNetv2, DenseNet121, EfficientNet, VGG16, and VGG19. The VGG19 backbone achieved the highest F1 score, 90.35%, according to the data. In addition, we employed feature-based knowledge distillation to move the knowledge from the VGG19 backbone to the lighter MobileNetv2 backbone (45x smaller than VGG19 in number of parameters), which increased the F1 score of MobileNetv2 from 86.97% to 89.91%. Our findings show that the effectiveness of the UNet model for pomegranate fruit segmentation is greatly impacted by the selection of the backbone architecture, and that knowledge distillation can improve the accuracy of UNet models with lighter backbones without significantly increasing their computational complexity.
Effect of Flaxseed or Turmeric Oils on Reproductive Parameters and Serum Oxidative Stress Markers of Male Albino Rats (Rattus Rattus)

Background : Phytoestrogens are estrogen analogues of plant origin, allowing them to act as agonists at estrogen receptors. Objectives: This study was done to discover the effects of flaxseed and turmeric oils on male rats' fertility. Methodology : Eighteen male albino rats are divided into three groups (6 rats each). Group 1 ( Control group) rats received distilled water 1 mL/kg orally); group 2 (Flaxseed oil group) rats received 1 mL/kg flaxseed oil orally;group 3 (Turmeric oil) rats received 1 mL/kg turmeric oil orally. After 30 days, blood samples, epididymis, and testis were collected for biochemical analysis, sperm characteristics and histological examinations. Results: Flaxseed oil induced a significant decline (p<0.05) in % of body weight change relative to the control group. Flaxseed and turmeric oils groups showed significant declines (p<0.05) in gonadosomatic index and sperm motility, concentration, and viability, with a significant increase in sperm abnormalities. Besides, Flaxseed and turmeric oils groups showed significant decreases (p<0.05) in the serum testosterone levels with significant increases (p<0.05) in serum prolactin, FSH, and LH levels. Flaxseed and turmeric oils consumption significantly decreased (p<0.05) plasma CAT and SOD while they significantly increased (p<0.05) plasma MDA relative to the control group. The testis of rats ingested flaxseed and turmeric oils showed degenerated seminiferous tubule s , with a diminished number of mature spermatozoa in the tubular lumen, reduced diameter of seminiferous tubules and intact basement membrane. Conclusion: Feeding flaxseed and turmeric oils consumption to adult male rats induced hormonal disturbances with non-uniformities in sperm characteristics.
Design and Development of Dual-Band Integrated Network Analyzer For Water Solvent Based RF Bio Sensors

The detection of analytes using Radio Frequency (RF) based sensors has gained immense popularity in recent times. The change in the sensor’s resonant frequency in the presence of different analytes is used as the basis for detection. Although, there have been plethora of advancements in developing RF biosensors, yet most of them have been designed and fabricated considering air all around them. However, in a practical scenario, a solute is mostly used with solvent for testing. The solvent alone produces huge shift in resonant frequency whereas, the shift due to solute and solvent together is significantly narrower (in MHz) when compared to the solvent alone. This indicates that the shift due to solute is narrowband in nature. Most of these sensors utilize traditional Vector Network Analyzers (VNAs) for measuring the s-parameters. However, traditional VNAs are broadband devices that are expensive, large and cumbersome to operate. These VNAs also have much larger bandwidths than the observed frequency shifts as mentioned above and often prove to be overkill. Thus, the optimal solution is to develop narrowband measurement platforms that precisely monitor the frequency shifts. This article tackles the challenge and presents a solvent specific integrated solution for the detection of materials having different permittivities. An Integrated Biosensing Network Analyser (IBNA) is designed and fabricated using microstrip line technology to detect different analytes (Glucose and Sucrose). IBNA primarily consists of a sensing and a measuring unit specifically designed for solvent like water. While the Interdigitated Capacitor based RF Bio Liquid Cavity resonating at 2.3 GHz performs the sensing operation, the Dual Band Six-Port Reflectometer (DBSPR), with an operating frequency of 1.1 GHz and 2.3 GHz, participates in the measurement of the s-parameters. Furthermore, to demonstrate the working of the design, the detection of 1 M Glucose and Sucrose is performed by EM-simulations and experiments. The sensing is carried out efficiently, with Glucose and Sucrose yielding frequency shifts of 15 MHz and 20 MHz at room temperature respectively. The results help in establishing the utility of IBNA and its superiority over traditional network analyzers.
Multilingual Text Classification on Social Media Data for Incident Alert in Subway Transportation Network

The traditional way of gathering data to alert subway incidents, such as being stuck on a train, electrocutions due to train malfunctions . , is through dispersed sensors or cameras embedded in the infrastructure of transportation networks. However, fixed detectors as the main source of incident detection in the transportation network may not bring all the relevant information. In the past decade, Twitter data have been used as a source of information for many event detection tasks empowering organizations to acquire actionable knowledge. However, the challenge of filtering out the relevant information from large amounts of data is a hindrance to the efficient usage of Twitter data for transportation incident detection. Therefore,
Play Arcs: Structuring Player Stories for Co­Design & Content Generation in Persistent Game Worlds

Players of Massively Multiplayer Online games (MMOs) consume content much faster than game designers can produce it. However, they also generate stories through their interaction, which can contribute to adding novel types of content in the game world. We introduce and demonstrate Play Arcs, a design strategy for structuring emergent stories that players can co­design and contribute as unique game content. We develop an MMO with tools for co­ design and ‘history game mechanics’ and test as a technology probe with 49 players. We show that Play Arcs successfully structure coherent stories and support players in shaping new, unique content based on their own histories. We found that these stories can inform and guide players’ decisions, and also that, while players often share simpler stories directly, they keep more notable stories to themselves for retelling later. We conclude by discussing design challenges and directions for future work with Play Arcs.
Household Classification Using Smart Meter Data

Abstract This article describes a project conducted in conjunction with the Central Statistics Office of Ireland in response to a planned national rollout of smart electricity metering in Ireland. We investigate how this new data source might be used for the purpose of official statistics production. This study specifically looks at the question of determining household composition from electricity smart meter data using both Neural Networks (a supervised machine learning approach) and Elastic Net Logistic regression. An overview of both classification techniques is given. Results for both approaches are presented with analysis. We find that the smart meter data alone is limited in its capability to distinguish between household categories but that it does provide some useful insights.