Miracle-dz/UniFAR-FTUs huggingface dataset:
Capacity & Coverage Analysis for Planning Yogyakarta Smart City’s Network (Use Case: LoRaWAN)

Jogja Smart Service application is owned by the Yogyakarta City Government in order to create Yogyakarta Smart City. In 2021. There were 802 complaints received by the application from a total of 415,509 Yogyakarta residents regarding public facility problems in the Yogyakarta. Among many complaints, the author will focus on 5 problems; public street lighting, electricity services, drinking water services, waste disposal, and air-quality monitoring. Smart City concept is divided into 6 aspects, (1) Smart Economy, (2) Smart Mobility, (3) Smart Environment, (4) Smart People, (5) Smart Living and (6) Smart Governance. In making Smart City concept come true, it requires IoT in its implementation. The problems mentioned by the authors can be solved by smart city concept. LoRaWAN is one of LPWAN non-cellular which is the strongest candidate in the smart city concept. The authors will solve these problems using non-cellular LPWAN analysis for Yogyakarta Smart City. The results of the analysis using Atoll simulation is coverage by signal and coverage by SNR. LoRaWAN produces coverage by signal of -70.07 dBm and coverage by SNR of -101.12 dBm.
Examination of the flow near the leading edge of attached cavitation. Part 1. Detachment of two-dimensional and axisymmetric cavities

The flow near the cavity detachment region of stable attached cavitation was examined using qualitative and quantitative flow visualization. The non-cavitating and cavitating flows around a hydrophilic brass and hydrophobic Teflon sphere and cylinder were examined. The location of non-cavitating boundary layer separation and cavity detachment was related to the free-stream Reynolds and cavitation numbers. The shape of the cavity near the detachment was greatly affected by the material of the cavitating object. The cavity interface on the hydrophilic test objects curved downstream to form a forward facing step. A region of recirculating fluid existed upstream of the cavity interface. The cavity detachment on the hydrophobic test objects was much closer to the location of boundary layer separation. The forward facing step and the recirculating region were nearly absent. The measured flow field near the surface of the brass sphere, cylinder, and hydrofoils under cavitating and non-cavitating conditions was used to calculate the position of two-dimensional laminar boundary layer separation. Thwaites' and Stratford's methods were used to predict the location of boundary layer separation upstream of the cavity detachment. The predictions compared well with the observed position of separation.
Towards the Generation of Robust E2E Test Cases in Template-based Web Applications

Capture and Replay techniques provide a well-known solution for End-To-End (E2E) testing of Web applications. They allow a tester to generate test scripts without requiring advanced programming skills. For this reason, they are very popular in acceptance and regression testing activities. These techniques are affected by the issue of fragility of the produced test cases, which may break even if small changes are operated in the user interface, without modifications of the app functionality. To overcome this issue, several approaches for either generating robust test cases or automatically repairing broken test cases have been proposed. In this paper we propose an alternative solution that aims at improving the testability of Web applications for generating robust test cases. This solution applies to Web applications developed with template-based technologies. It is based on the template source code automatic injection of additional hook attributes and on the proposal of a new type of locators based on such hooks. These locators aid the unique retrieval of the user interface items involved in test cases. We validated our technique in the context of a continuous integration and delivery processes of template-based web applications that was developed from scratch. The study showed that the use of hook-based locators can improve the robustness of test cases generated by a Capture & Replay testing tool, introducing relevant savings in the regression test case repairing activity.
Analysis of Netcode, Latency, and Packet-loss in Online Multiplayer Games

With the massive growth in popularity of online multiplayer games, it's never been more important to understand the effects of packet loss and delay on user performance. In this paper, we propose an approach to understand netcode of games better. Our approach is to clear the misconceptions about the complex networking topics and help gamers identify where the issue might exist. We have introduced some tools that are easy to understand and use. With the help of these tools, entry level users or gamers do not need to learn the complex analyzing process. They can simply rely on the test result of these tools and forward the information to their ISP to resolve their connection issues. Previously, extraction of IP of any game servers was a tedious process. In our work, we have proposed the simplest way to know the IP of game servers. In the table below, we have included the total delay of current online multiplayer first-person shooter games. We believe these findings have larger implications because most first-person shooter games involve generic user behaviors similar to those we analyzed.
Open Switch Fault Detection in Electric Vehicle Drives Using Support Vector Machine

This paper presents an open switch fault (OSF) detection and faulty switch identification technique in a three-phase Electric Vehicle (EV) drive using Support Vector Machine (SVM). Three-phase instantaneous current signals obtained from the inverter output have been given to SVM for OSF detection and faulty switch identification. The performance of SVM has been assessed as a binary-class and multi-class classifier for OSF detection and classification. Various cases have been simulated under healthy and OSF conditions with different load and speed variations. The performance of the proposed technique has been satisfactory for OSF diagnosis in inverter-fed EV drives. Moreover, it reduces the computation burden on the controller.
Implementational Aspects for the Characterization of Low-Inertia in Power Systems

The replacement of conventional generating units with non-conventional renewable energy resources impacts the reliability of power systems. With high integration of renewable-based generation resources, there is a reduction in total system inertia, which could lead to systems with less time to react for frequency restoration in case of unexpected unit failures. Though several power system testbeds are available for studying and analysing different stability phenomena, none are suitable for characterizing, or analyzing low-inertia conditions. In this paper, a power system simulation environment is introduced for investigating control architectures for power grids, with special attention to the impact of high penetration of inverter-based generation resources (IBRs). The testbed is applied to the WSCC 9- bus test system, modified so that a range of IBR penetration levels can be analyzed (from zero to 80%). The testbed is suitable for studying both fast and slow electromechanical phenomena. A conclusion from numerical experiments is that high levels of IBRs do not necessarily result in low-inertia. This is due in part to the layers of control in the grid already in place to avoid frequency excursions.
Calculation of Fin Efficiency for Wet and Dry Fins

This paper presents a quantitative evaluation of methods used to calculate fin efficiency for dry and wet fins. The exact solution for circular fins uses Bessel functions, which are tedious to evaluate. The authors provide an empirical modification to the Schmidt equation (Hong and Webb equation), which provides improved accuracy for circular fins. Existing methods to evaluate the fin efficiency of rectangular, plain plate fins are evaluated. The plate-fin methods are compared against the fin efficiency calculated by the sector method using Bessel functions. Use of the sector method with the Hong and Webb equation is recommended. Cases for which the simple “equivalent circular fin” method is a very good approximation are defined. Methods to calculate the fin efficiency of enhanced fin geometries are discussed. The authors show that the wet surface fin efficiency for any fin geometry may be calculated using a simple modification of the dry surface fin efficiency equation. Example calculations are presented.
Automatic 3D Hand Pose Estimation Based on YOLOv7 and HandFoldingNet from Egocentric Videos

3D hand pose estimation problem still contains many challenges such as high degree-of-freedom (high-DOF) of 3D point cloud data, the obscured data, the loss of depth image data, especially the data obtained from the first-person viewpoint. In this paper, we propose an automated method for 3D hand pose estimation on detectable hand point cloud data collected from Egocentric vision. Our approach is the combination of YOLOv7 and HoldFoldingNet which are the current highest performing CNNs for hand detection and 3D hand pose estimation problem. We evaluate two stages in the proposed method on the FPHAB dataset, (1) the first stage is the evaluation of hand detection based on the pre-trained model of YOLOv7 and its variants, and (2) the second stage is to evaluate the result of 3D hand estimation on the HoldFoldingNet with the annotation box and the detected box and compare them with the state-of-the-art methods. The result of detecting hand action is the lowest (P=97% - thresIOU = 0.95). The 3D hand pose estimation results using HoldFoldingNet on the FPHAB dataset have the lowest error of 19.98mm, 20.4mm, respectively when testing on the annotation box and the detected box of the 5th configuration,
Problems of Intellectual Property in the Information Economy Through the Prism of Artificial Intelligence as a Dual-Purpose Technology

The state and trends of the development of artificial intelligence (AI) were analyzed in a paper as well as its influence on scientific and inventive activity and the multiplier effect of AI as a phenomenon on the administration of intellectual property protection, in particular patent and copyright, as it allows to improve the quality of consideration of applications and reduce their cost. The experience of China's leading tech giants in the development of new generation artificial intelligence with the support of state funding is also considered. Special attention is paid to the consequences of the intended use of AI, as well as the unintentional misuse of technologies. The existing problems of the digital economy regarding the recognition of authorship, the possibility of managing copyrights and the role of AI in solving these problems are identified. In particular, attention is focused on the fact that significant amounts of data are needed to improve pattern recognition algorithms, and practical approaches based on foreign experience in solving these problems are also given.
SMS: SIGNS MAY SAVE – Traffic Sign Recognition and Detection using CNN

Traffic sign classification automatically detects roadside traffic signs, such as speed limit signs, yield signs, etc. Automatically recognizing traffic signs enables the development of “smarter automobiles. ” Self-driving automobiles require traffic sign recognition to interpret and comprehend the roadway accurately. Similarly, “driver alert” systems within cars must understand the surrounding roadway to assist and protect drivers. Our automation would assist drivers in detecting and identifying traffic signs without distracting them from the road. With convolution neural networks, the signboards can be accurately classified. The precision can be improved by adding more layers. The GTSRB dataset is utilized here for training and testing; by fine-tuning the parameters, the 43 types of traffic signs are categorized accurately, and the detection speed also increases.
Prominent Prediction Model for Chronic Diabetes Disease Using Machine Learning

Diabetes is a critical health problem in the twenty- first century that needs special attention and community health care programs. Even among children, diabetes is a condition that is spreading quickly. An elevated blood sugar level is what causes diabetes. A metabolic condition called chronic diabetes mellitus is associated with blood sugar concentrations brought on by either insufficient insulin secretion, resistance to insulin action, or a combination of both. Another type of sickness, primarily affecting the heart, kidneys, blood vessels, nerves, and eyes, is also brought on by diabetes. In order to anticipate medical outcomes, machine learning techniques are applied. Utilizing both historical and current data, machine learning enables constructing models to swiftly assess data and offer outcomes. Providers of health care can make more informed choices about patient diagnoses and treatment alternatives because to machine learning, which enhances health care services as a whole. Performance is assessed using model sensitivity, selectivity, precision, recall, and correctness. Following that, the five top models are chosen to perform assembly. The quality of the dataset is being predicted to be improved by an ensemble model based on boosting. When comparing accuracy, the existing optimization model outperforms the existing single model. A 10-fold cross verification is used to increase the data's resilience.
Disturbance-resistant Camera-LiDAR Fusion for Robust Three-dimentional Object Detection

Three-dimentional object detection using monocular cameras and LiDAR fusion presupposes that the accuracy of extrinsic calibration between cameras and LiDAR is satisfactory. However, in engineering, the Camera-LiDAR calibration error is always quite large. In this paper, the fusion problem of LiDAR-camera with perturbed calibration parameters is investigated, and a soft-association method is proposed for heterogeneous sensor data association. Experiments with the KITTI datasets are carried out to verify our proposed method. Under 6 degrees of freedom bias perturbations, the intersection-over-union score obtained from our method is higher than that of the traditional method. In addition, our method allows valid correlations with translation errors of $\pm 15$ mm and rotation errors of $\pm 0.06$ rad, and our algorithm is successfully deployed on low resolution LiDAR and camera platforms.
Broadband Fiber Optic Current Transformer for Eliminating Birefringence Effect

With the increase of power system voltage level, monitoring lightning current is of great significance to the lightning protection of transmission lines. Fiber optic current transformer (FOCT) is suitable for current monitoring in ultra-high voltage (UHV) power grid. However, the external environment such as temperature is likely to produce birefringence effect in the sensor, resulting in the decline of measurement accuracy. To suppress the birefringence effect in FOCT, this paper studied a sensing structure coupled a Faraday rotator (FR). Its mathematical model is established by using Jones matrix theory, and the effect of birefringence and FR is analyzed. The temperature characteristic and lightning current response of the sensor are tested. The researches show that the output error is less than 2% in the temperature range of $\boldsymbol{-20{ }\ ^{\circ} \mathrm{C} \sim 80 \quad{ }^{\circ} \mathrm{C}}$. Meanwhile, the frequency band can cover 8/20 $\boldsymbol{\mu \mathrm{s}}$ lightning current. Therefore, FOCT studied in this paper has wide frequency band and good robustness, which can be applied to broadband current measurement in complex environment.
Carrier Phase Positioning Using 5G NR Signals Based on OFDM System

Carrier phase positioning is an attractive candidate technology for 5G high-accuracy positioning because of its centimeter-level positioning capability that has been verified in the Global Navigation Satellite System (GNSS). However, when applying it in 5G New Radio (NR) system using Orthogonal Frequency Division Multiplexing (OFDM), it will face some new challenges. In this paper, we propose several carrier phase measurement methods based on 5G NR system. Then, a multi-frequency carrier phase ranging method is proposed to mitigate the impact of the next Generation NodeB (gNB) antenna reference point (ARP) position error. Finally, it is shown that carrier phase positioning technology in 5G can achieve centimeter-level to millimeter-level positioning accuracy performance by system-level simulation experiments.
Analysis of Codes of Ethics in the Transport and Logistics Industry in the Czech Republic

The demands being placed by stakeholders on businesses with regards to ethical behaviour and conduct have been growing on a long-term basis. Within this context, a code of ethics is considered the most important tool of ethical management in companies. This applies to an even greater extent within the field of transport and logistics due to the nature of the business. This contribution therefore sets out to analyse codes of ethics in companies operating in the transport and logistics industry in the Czech Republic. Two research questions were formulated in compliance with the goal. The first sought to determine the quality of the codes of ethics of companies operating within the industry. The second sought to determine to what extent the contents of those documents met preconditions for efficacy. A qualitative comparative analysis of 21 codes of ethics was used to obtain the answers to the formulated research questions. The results of the analysis showed that significant differences exist between the examined documents in relation to their quality and contents. In terms of the provisions that form the preconditions for the efficacy of these documents, it was found that significant improvements were required in a substantial number and parts of the examined documents.
Autonomous Navigation for Mobile Robots with Weakly-Supervised Segmentation Network

This paper investigates autonomous navigation for mobile robots with a low-cost monocular camera. The main challenge lies in: i) how to accurately detect prior unseen obstacles and acquire obstacles position from monocular images without depth information, and ii) how to get the constraints for path planning to generate safe and stable path for the robot. To accurately locate surrounding obstacles using only a monocular camera, we adopt a weakly-supervised semantic segmentation network trained from LIDAR data and perform inverse perspective transformation based on ground plane constraint. Meanwhile, to reduce segmentation noise, we establish a probability occupancy map based on the distance between robot and obstacles. For path generations, we present a novel search-and-optimization based planning approach to get boundary constraints in Frenet frame and generate stable local path with consecutive image inputs. In the simulation, segmentation Intersection over Union (IoU) of the drivable area achieves more than 99% and the average mapping accuracy is less than 10cm, showing feasibility and robustness of our navigation scheme.
Distance Aware Compression for Low Latency High Bandwidth Interconnection Network

NoC(Network-on-Chip)s is an essential component of recent multi-core systems. When the number of wires available on a chip is limited, it is sometimes congested and increased la-tency can degrade the parallel application performance. The selective data compression has been proposed to mitigate such network congestion by compressing and decompressing packets based on the packet length and traffic situation. However, since the algorithm does not care the location of nodes, the compression and decompression are performed even when the packet is transferred between neighboring nodes. This paper proposes a distance aware (DA) compression mechanism to select whether the packet should be compressed by the distance to the destination. The packets to the nodes whose distance is larger than threshold level are compressed with a run-length loss-less compression at the sender's network interface and de-compressed at the receiver's network interface. Cycle level network simulation results show that the selective compression method achieves up to 45% bandwidth improve-ment with 1.26 times increase of the latency.
RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset

Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availablity of large scale real-world datasets. To motivate further advances in contactless fingerprint matching across sensors, we introduce the RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to promote research under different matching scenarios that include Single Finger Matching and Multi-Finger Matching for both contactless-to-contactless (CL2CL) and contact-to-contactless (C2CL) verification and identification. Furthermore, due to the high intra-sample variance in contactless fingerprints belonging to the same finger, we propose a set-based matching protocol inspired by the advances in facial recognition datasets. This protocol is specifically designed for pragmatic contactless fingerprint matching that can account for variances in focus, polarity and finger-angles. We report qualitative and quantitative baseline results for different protocols using a COTS fingerprint matcher (Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The dataset can be downloaded here: https://www.buffalo. edu/cubs/research/datasets/ridgebase-benchmark-dataset.html
Multi-Relational Pedestrian Trajectory Prediction in Complex Scenes

Pedestrian trajectory prediction has an important impact on the construction of smart cities and the popularization of autonomous vehicles. Pedestrian trajectory prediction in complex scenes is challenging because the trajectories are largely disturbed by the surrounding social environment. To better model the relationship between pedestrians and the social environment, we propose a novel Multi-Relation Network based on Long Short-Term Memory(LSTM). We first use Convolution Neural Network(CNN) and LSTM for feature extraction of scenes and pedestrians respectively. We realize the interaction between pedestrians and social environment by introducing attention mechanism. The experiment results on two public datasets, i.e. ETH and UCY, demonstrate that the prediction accuracy can be effectively improved by considering the social interactions.
Accurate Joint Offset Calibration for Quadrupedal Robots

Joint calibration is one of the fundamental works to ensure the locomotion performance of quadrupedal robots. Inaccurate joint offset calibration accuracy will incur foot-tip position errors and significant disturbances to locomotion performance, especially in highly dynamic scenarios. This paper proposes an accurate joint offset calibration method for quadrupedal robots. We derive the leg kinematic error model based on the product of the exponentials formula and use the iterative least squares algorithm to obtain the joint offset of the quadrupedal robot. Considering the influence of the body frame on the calibration of the abduction/adduction (Ab/Ad) joint, the offset of the Ab/Ad joint is modified by the angle between the z-axis of the body frame and that of the leg frame. We verify the effectiveness of the proposed method on an experimental quadrupedal robot, where the maximum foot-tip position error is decreased from 13.97mm to 2.25mm after calibration.