Miracle-dz/UniFAR-FTUs huggingface dataset:
RailDepth: A Self-Supervised Network for Railway Depth Completion Based on a Pooling-Guidance Mechanism

Depth completion of forward objects is critical to safe and effective autonomous driving as it can estimate dense depth from sparse light detection and ranging (LiDAR) data and RGB images. The study proposes a self-supervised depth completion method to address the deficiency in depth sensing images caused by the uneven distribution of point clouds generated by long-range LiDAR. The proposed method uses a stacked-hourglass-network structure as the backbone, which allows to perform multilevel deep fusion to extract features from uneven point clouds. In addition, a fusion module is introduced to fuse global–local features from different stages of the network output, and a pooling-guidance mechanism is designed to guide network convergence through adaptive pooling-guidance depth construction. Field experiments were conducted on the Tsuen Wan Line in Hong Kong to verify the performance of the proposed network. The results indicate that the proposed network outperforms the benchmark self-supervised depth completion methods, with a root-mean-squared error (RMSE) of 8165.673 mm and a mean absolute error (MAE) of 2596.418 mm. Specifically, the RMSE and MAE were improved by 4.7% and 2.4%, respectively, compared to the benchmark methods.
Patient Assistance System Based on Hand Gesture Recognition

We propose a two-stage hand gesture recognition architecture to support a patient assistance system. Some medical conditions limit mobility, and the patient must rely on medical staff to meet their needs. In such cases, a phone or intercom is not convenient to call for help. A vision-based system operated by changing the orientation of fingers can be used to send specific messages without making arm movements. However, vision-based hand gesture recognition is hindered by occlusion, background clutter, and variations in illumination. Therefore, we developed a two-stage architecture: the first stage produces a saliency map to simplify recognition and the second stage performs classification. A novel combined loss function optimizes the saliency detection model and makes the saliency map more precise. An adaptive kernel-based channel attention layer is used to emphasize salient features. The proposed architecture achieved precise saliency detection on four benchmark datasets and high-accuracy recognition on two. We designed an interface for patients to send specific messages to the medical staff using hand gestures. The interface help patients request assistance and connect with medical staff without leaving the bed or involving a third party.
Modified Dispersion Characteristics with Field Restricted Current Density Distribution for Dielectric Loaded Planar Rectangular Tape Helix TWTs

The current limiting function, along with the other field boundary conditions, is used to obtain the approximate modified dispersion equation for the dielectric-loaded rectangular planar tape helix (PTH). The additional boundary condition employing the current limiting function confines the current density distribution within the surface region of the tape helix, thereby nullifying the fields in the gap regions of the PTH. Despite the increase in complexity of the electromagnetic boundary value problem, the accurate dispersion equation is derived, simplified, and verified for mathematical symmetricity. The derived dispersion equation is numerically computed for $\beta_{0a}(k_{0a})$ with a significant convergence rate for each root of the $\beta_{0a}$ function. The dispersion characteristics consisting of fundamental harmonic and few additional harmonics is plotted from a finite number of simultaneous equations approximated from an infinite set.
Direct Power Control of a Surface-Mounted Permanent Magnet Synchronous Generator Wind Turbine for Offshore Applications

This paper proposes a direct power control scheme for an offshore wind turbine-driven surface-mounted permanent magnet synchronous generator with a full back-to-back converter situated onshore to decentralize the weight of the turbine. The proposed model is designed in a stationary reference frame and does not require a phase-locked loop as opposed to the traditional vector current control method. Simple feed-forward and feedback control loops are used in the proposed model to extract the maximum available wind power, maintain constant voltage at the coupling DC link, and ensure power injection into the electric network at unity power factor. Simulation results carried out in MATLAB/SIMULINK show that the proposed model has a fast transient response and steady-state performance consistent with the vector current control model. Furthermore, eigenvalue analysis results demonstrate that the proposed method’s performance is robust to variations in different system parasitic elements, even under both stiff and weak electric network conditions. These results also indicate that the back-to-back converter can operate effectively when located onshore close to the load center while the turbine is in close proximity offshore. This can be suitable for multiple specific offshore applications as it can potentially reduce the overall weight, volume, and cost of the offshore wind turbine infrastructure.
An API Framework for Flexible and Lightweight Microservice Composition

The employment of decentralized microservices requires a lightweight service composition scheme with flexibility. However, no schemes can fulfil the requirement. In this paper, a data and control interdependent problem in traditional API form is identified as the root preventing a scheme fulfilling the requirement from being devised. A new configurable API form, Standardized Control Data Separation API (SCONDAS API), is devised to resolve the data and control interdependent problem. An API framework implementing the form of SCANDAS API is further proposed. The proposed API framework enables lightweight and flexible microservice composition. Experimental evaluations validate the feasibility of the API framework.
GPU Database for Large Geospatial Datasets

Nowadays, the approaches and methods of data storage have entirely changed from the traditional SQL-based theorem. The purpose of subsequent data processing has become one of the main aspects. NoSQL databases appeared, which enabled more abstract databases such as graph and time series databases. This paper aims to inspect the benefits of a database principle that works natively on the graphics card, which can be optimal for storing and querying spatial data. This survey introduces the HDF5 file format as a binary data store method, the postGIS system as one of the most widespread GIS data storage methods and the NVIDIA GPUDirect Storage (GDS), which is the high-level concept for enable DMA between the memory of the GPU and with the PCIe devices (e.g. NVME SSD) on Linux. Another objective of the paper is to present which features of these technologies can support the native GPU database concept [7], [8].
Electromagnetic Energy Theorems and the Energy-Viewpoint-Based Modal Analysis Theories

The energy conservation law (ECL) is one of the most fundamental physical laws in the nature. The ECL has some different manifestation forms. The various manifestation forms can be mathematically expressed in terms of the corresponding energy theorems. The various energy theorems govern the energy utilization mechanisms of the corresponding physical phenomena. This paper simply reviews some important electromagnetic energy theorems, such as the entire-structure-oriented work-energy theorem (ES-WET), the partial-structure-oriented work-energy theorem (PS-WET), the energy transport theorem (ETT), the Poynting theorem, the Rayleigh-Carson reciprocity theorem (RT), the Rumsey RT, the Lorentz RT, and the Parseval theorem. The ES-WET governs the energy-scattering phenomenon; the PS-WET governs the lumped-port-driven energy-transmitting phenomenon; the ETT governs the wave-port-fed energy-transmitting phenomenon; the Poynting theorem governs the energy-absorbing phenomenon; the RTs govern the energy-coupling phenomenon; the Parseval theorem guarantees the energy invariance in the time domain, frequency domain, and spectrum domain. This paper simply summarizes some valuable energy-viewpoint-based modal analysis theories, such as the ES-WET-based characteristic mode theory (CMT) for energy-scattering phenomenon, the PS-WET-based CMT for lumped-port-driven energy-transmitting phenomenon, the ETT-based decoupling mode theory (DMT) for wave-port-fed energy-transmitting phenomenon, and the Poynting-theorem-based DMT for energy-absorbing phenomenon.
Systolic Array for Parallel Solution of the Robust Kalman Filter Used for Attitude and Position Estimations in UAVs

The efficient Kalman filter has been widely used in recent decades to obtain air navigation information in UAVs. However, for a good performance of the Kalman filter, the model that describes the system dynamics must not contain uncertainties. This paper presents the implementation of a robust Kalman filter to estimate the attitude, velocity, and position of UAVs. The robust filter considers uncertainties in the sensor models. A mathematical structure based on the solution of linear systems synthesizes the predictor-corrector robust estimation algorithm. The main contribution of this study is the proposed QR decomposition based on Givens rotation to solve the linear system. The simulated experiments used sensory data collected in Zürich-Switzerland and ground truth referencing attitude, velocity, and position. The offline simulation results express the effectiveness of the robust Kalman filter for this application, with a reduction of up to 18.9% in the estimation error, in relation to the standard Kalman filter. The proposal to use systolic arrays for numerical solutions has shown promise for implementation in parallel processing platforms, such as FPGAs.
Research on Deep Spatio-Temporal Model and Its Application in Situation Prediction

Situation prediction refers to predicting the state information of things in the future on the basis of existing information, and situational information contains complex laws of time and space. Traditional methods only consider a single factor or separate time and space. At the same time, due to the limitations of traditional algorithms, it is not possible to accurately predict air combat events with long interval dependencies. In order to solve these problems, we propose a deep spatio-temporal model based on the dynamic graph convolution and attention mechanisms. The model extracts and analyzes the features of space and time respectively. Experimental results show that the model proposed in this paper has more stable training process and higher prediction accuracy.
Investigations on Machine Learning Models for Mental Health Analysis and Prediction

Machine learning Techniques are identified as the most suitable methods for mental health analysis and prediction. Mental illness among people has increased vastly around the world and has become a serious human problem to be solved. From much research work and research articles, it is evident that machine learning algorithms can be an effective approach to finding mental illness. In this paper, different machine learning algorithms are investigated to find the best model, suitable to predict the mental health of a person more accurately and at a faster rate. In order to create a system that operates effectively and quickly, this paper investigates the performance of various machine learning models, including KNN, Support Vector Machine, Random Forest, Logistic regression, Decision tree, etc. All the models are compared based on the accuracy that each method offers after successful execution.
Real- Time Driver Drowsiness Detection System using Facial Landmarks

Driver drowsiness is one of the leading causes of accidents and has become a hot research topic. This paper gives an overview of detecting the drowsiness of drivers using behavioral metrics and machine learning approaches. The face imparts a great deal of information (eye blinks, head motions, etc.) which can be utilized to deduce sleepiness. By recognizing the driver's drowsiness and notifying the driver, Computer vision techniques and Image processing technologies can minimize most accidents. This research addresses the issue by identifying key factors such as eye closure, yawning, and head orientation. To determine this, Recurrent Neural Network (RNN) and classifiers were used to extract the facial landmarks, and a 3D locator was used for estimation. In many ways, the culminating results suggest that the real-time approach's performance outperforms the older approach.
GITM: A GINI Index-Based Trust Mechanism to Mitigate and Isolate Sybil Attack in RPL-Enabled Smart Grid Advanced Metering Infrastructures

The smart grid relies on Advanced Metering Infrastructure (AMI) to function. Because of the significant packet loss and slow transmission rate of the wireless connection between smart meters in AMI, these infrastructures are considered Low-power and Lossy Networks (LLNs). The routing protocol in an AMI network is crucial for ensuring the availability and timeliness of data transfer. IPv6 Routing Protocol for Low-power and lossy networks (RPL) is an excellent routing option for the AMI communication configuration. However, it is highly at risk against many external and internal attacks, and its effectiveness may be severely diminished by Sybil assault. Different trust-based techniques have been suggested to mitigate internal attacks. However, existing trust systems have high energy consumption issues, which cause a reduction in the performance of LLNs due to complex calculations at the node level. Therefore, this paper presents a novel fog-enabled GINI index-based trust mechanism (GITM) to mitigate Sybil attacks using the forwarding behavior of legitimate member nodes. Regarding identifying and isolating Sybil assaults, our approach outperforms the state-of-the-art methods. GITM detects and isolates a more significant number of malicious network nodes compared to other techniques within a similar time frame. By using the proposed GITM framework, the Sybil attack detection rate increases by 4.48%, energy consumption reduces by 21%, and isolation latency reduces by 26.30% (concerning time). Furthermore, the end-to-end delay is merely 0.30% more in our case, and the number of control messages decreases by 28%.
A Flash-Based Cache Optimization Strategy

Flash-based key-value caching is becoming increasingly popular in data centers. By caching most of data on SSDs, caching systems can eliminate a large number of time-consuming requests to back-end data storage and provide low-latency services. According to research, data redundancy is prevalent at all levels of storage systems with duplicate data taking up cache space and reducing the logical space for caching. Moreover, optimizing the performance of flash-based key-value caches is challenging due to the unique technical limitations of flash devices. In this paper, we propose a cache optimization strategy called PRCache, which designs a two-stage garbage collection scheme combining data deduplication techniques and replacement algorithms. We implement and evaluate PRCache, and experimental results show that PRCache improves the hit rate by 14.8% and reduces the average access latency by 13.7%.
Study on logarithmic dual gradient helix slow wave structure

In this paper, a novel logarithmic dual gradient helix slow wave structure (SWS) is proposed. Driven by a 6900 V, 50 mA pencil electron beam, the output power of the logarithmic dual gradient SWS circuit has been analyzed by the simulation tools. The maximum output power of the logarithmic dual gradient helix SWS circuit can reach 52 W. The output gain comes over 23.5 dB over 33~40 GHz. The electronic efficiency of the 50 mm long novel SWS circuit can be increased to more than 14%, which is a 50% improvement compared with the conventional helix SWS.
Effective Heart disease prediction framework using Random Forest and Logistic regression

Heart or cardiovascular disease claims approximately 17.9 million lives each year and has become a significant cause of death. Prior detection of CVDs (cardiovascular diseases) could determine dietary and lifestyle alterations needed for a high-risk patient's life, which will lessen the challenges they might face in the future. Several people experience cardiovascular conditions (CVDs) due to their current lifestyle which have even resulted in fatalities across the world. This research has attempted to narrow down potential factors for cardiovascular disease as well as reliably estimate all in all danger. Applying uniform methods in data mining such as logistic regression and random forest will help to accurately determine the factors which play a role in it. To acquire the best accuracy and to understand the parameters of the application, the LR model is first given training with five splitting conditions and then assessed with a training dataset. The algorithm generates categories of one and zero for heart disease occurrence and avoidance, respectively. Significant information is enabled by it, such as patterns and linkages. The key topic of this research work is centered on the patients, given specific health parameters, who are more likely to suffer heart problems. Using the patient's medical records, we put together a method that can identify if a cardiovascular disease assessment is required or not for the individual. To detect and label an individual with heart problem, we employed two different machine learning methods i.e., logistic regression and random forest. The precision value obtained by the Logistic Regression method is 85.25%, and the precision value obtained by the Random Forest method is 90.16%.
The Relationship between the Cattell Test and Users Social Network Subscriptions

The work is devoted to the question of identifying the relationship between the results of the R. Cattell test (16-factor test) and user subscriptions in a social network. A vector approach with space reduction is used to represent user subscales, and some machine learning algorithms are used to test the relationship. The results of this study showed that factors A, B, C, F, G, N, O, Q1, Q3, Q4 of R. Cattell test. The results of this study showed that the relationship between subscales and R. Cattell test results is possible to predict better than random.
An improved UNet model based on adaptive activation function and squeeze-and-excitation module for milling tool wear segmentation

The fine monitoring technology of the milling tool wear condition is a crucial prerequisite to ensure both the processing quality and the smooth progress of the machining process. In order to fulfil this requirement, this paper constructs an improved UNet model to achieve end-to-end high-precision segmentation of milling tool wear area. The model uses Resnet as the feature extraction framework, and introduces an adaptive activation function to prevent information loss and minimize the activation function cost. Meanwhile, the squeeze-and-excitation module is introduced in the front and back ends of the feature extraction framework to enhance the important features and suppress irrelevant features. The accuracy and adapt ability of the proposed model is confirmed through the experiment of accelerating milling cutter life and three different failure phenomena.
Equal Power Allocation based Channel Sensing in Multi-user Over Cognitive OFDM Systems

With the fast advancement of cellular communications, spectrum scarcity is a significant issue; additionally, frequency spectrum is turning into a valuable resource. Because licensed users do not always employ their spectrum and unlicensed users have set up shop in such spectrum bands, in some instances they are not productively handled. Spectrum usage is uneven as a result of this governance. Each cognitive radio user has minimal SINR requirements that determine their required quality of service (QoS), and interference from secondary unlicensed users to main users makes the power and channel allotment issue more difficult. Here, the idea outlines the equitable distribution of power and channel resources among auxiliary and main users within a network, as well as effective data transfer over users based on power-filling algorithms.
Wallet Game: Probability, Likelihood, and Extended Likelihood

ABSTRACT We propose a likelihood explanation to the two-person wallet game, a probability-related paradox, where an obviously fair game may appear favorable to both players. Yet a small variation of the game, without changing its fairness, turns it to seem unfavorable. The extended likelihood concept seems logically necessary if we want to allow the sense of uncertainty associated with a realized but still unobserved random outcome, while at the same time avoid potential probability-related paradoxes.
Construction of Secure and Energy-Efficient Connected Wireless Sensor Networks

In this paper, we study the problem of constructing secure and energy-efficient connected communication graphs of Wireless Sensor Networks (WSNs). Unlike most existing works optimizing only one of the three constraints of WSNs: security, connectivity or energy consumption, our proposed algorithm, named Cluster-based Graph Construction (CGC) Algorithm, produces a connected graph which is secure and energy-efficient. We prove the correctness of the algorithm, and then validate its performance in terms of 1) the number of keys used, 2) power levels and 3) the node degrees and the network diameter for simulated networks as it is ideal for a network to have 1) the less number of keys as a large number of keys would increase overheads of nodes, 2) the less power level consumptions to prolong the network's lifetime, and 3) the smaller node degrees and diameters for WSNs applications to produce lesslatency schedules. Finally, we conclude with the future research directions.
Birth time prediction based on uterus-activity using machine learning

The goal of this paper is to describe a predictive model that is able to estimate the expected time of the child’s birth using contraction data collected since the beginning of labor. During research both classical and neural network time series forecasting models were investigated. Among the classic time series forecasting methods, the Integrated Autoregressive Moving Average Model, i.e., ARIMA, and Holt’s exponential smoothing were examined. And among the time series forecasting methods based on neural networks, the LSTM i.e., Long short-term memory and the one-dimensional convolutional neural network were implemented. The evaluation results show that the neural networks outperformed the classical methods. The best result was achieved using the one-dimensional convolutional neural network.
THE NUMERICAL CALCULATION OF WAVE-FIELDS, REFLEXION COEFFICIENTS AND POLARIZATIONS FOR LONG RADIO WAVES

The wave-fields inside a horizontally stratified ionosphere produced by a radio wave incident at any angle from below are calculated by integrating the governing differential equations. An improved method of obtaining two independent solutions of these equations is described in some detail. For the first solution, the polarization of the wave incident on to the ionosphere from below is chosen so that no energy is propagated to great heights (i.e. no energy in the 'whistler mode'); the second solution has the incident polarization chosen to maximize this energy. In each case, a complex reflexion coefficient is derived from the relative amplitude and phases of the upgoing and downgoing waves in free space below the ionosphere, and the polarizations of the downgoing wave are also studied. The method which is usable on a small computer, is applicable for any angles of incidence, azimuth and dip, and for any height variation of the model ionosphere.