Miracle-dz/UniFAR-FTUs huggingface dataset:
A Graph-Based Visual Query Method for Massive Human Trajectory Data

With the advance of location acquisition technologies nowadays, the human trajectory data, such as vehicles, phones and bicycles, is growing vastly and extending our imagination of new users. These trajectories have shown great values in supporting situation-aware exploration and prediction of human mobility, discovering movement patterns and monitoring the traffic situations. Query is an essential task for trajectory exploration. To do this, users should input spatial, temporal and other types of query conditionals to obtain a group of filtered trajectories. However, current visual query methods for urban data suffer from two main problems. First, it is a challenge for users without background knowledge to specify query conditions, such as selecting a popular entertainment area on the map as spatial condition. Second, there still needs a way to fuse the multi-source, heterogeneous, and large-scale urban data to partition the urban area, extract their semantic information, and provide intuitive, efficient trajectory queries. In this paper, we propose CBRG(cellular-based region growing) algorithm to make a preliminary region partition which tends to be a certain collection of similar regions. Then, we generate a semantic node for graph and provide a set of feasible clues and signs to explore and mine data from the aspects of spatio-temporal characteristics, inherent coupling of data, and human mobility. It is worth mentioning that our methodology is also universal for other heterogeneous data with spatio-temporal characteristics.
Secure Data Aggregation Mechanism for Water Distribution System using Blockchain

Development of intelligent systems in particular Water Distribution Systems (WDS) increases the demand of implementing a secure scheme that can preserve user’s identification and data consumption through maintaining confidentiality, authentication and integrity. Decentralization topology has investigated a lot recently in the literature with the development of bitcoins and Ethereum networks in different IoT disciplines such as power systems and healthcare systems. In this paper, feasibility and uses cases studies on the integration WDS with Blockchain Technology are discussed. Moreover, the customer’s data and identity anonymity techniques that can be integrated with the network are discussed. Furthermore, a data aggregation mechanism of the smart meters in Water Distribution System (WDS) based on distributed ledger and Blockchain technologies is proposed. Further, the customer’s identity using bloom filter is simulated and optimal parameters of the bloom filter are suggested.
Comparison of IMF Selection Methods in Classification of Multiple Sclerosis EEG Data

Empirical mode decomposition (EMD) method is a powerful tool in the analysis of of nonlinear and nonstationary signals. It decomposes signals into a number of amplitude and frequency modulated signals namely intrinsic mode functions (IMFs). However, some of these IMFs represents the original signal better while some of them are useless. IMF selection methods are suggested to determine the IMFs which represents the original signal better than other IMFs. In this study, we analyzed the effect of IMF selection methods in classification performance. We compared power based, correlation based and power spectral density based IMF selection methods in the classification of the electroencephalography (EEG) signals, which are collected from subjects with multiple sclerosis. The EEG signals are classified as the patients are being cognitively impaired or intact. k-nearest neighbors, multilayer perceptron neural networks and random forest classifiers are used in classification. The results show that, effect of IMF selection methods on accuracy is changeable in regard to classifier preference.
Image Lag Elimination Algorithm for Pulse-Sequence-Based High-Speed Image Sensor

When the pulse-sequence-based image sensor films high-speed moving objects, the image lag problem may appears as blurring in reconstructed images. In this paper, an algorithm is proposed to suppress lags by distinguishing the lag area and calculating ratio of bright to dark part in one trigger interval. The algorithm bases on the sensor’s characteristics and image lag principle that the change of light intensity cannot be immediately reflected on the pulse generated from photoelectric accumulation. The effectiveness of the algorithm is verified both on the sensor’s behavior-level model and the real image sensor. In both experiments the curves of LSF (line spread function) and MTF (modulation transfer function) show the reconstructed edges after lag elimination algorithm are similar to the real edges both in sharpness and position. The indexes to evaluate image similarity including LMD (location mean deviation), PSNR (peak signal to noise ratio) and SSIM (structural similarity index) improve respectively. In the experiment on sensor’s model, when the simulation velocity of edge is low, the algorithm can reconstruct the perfect edges as input images. As velocity increases, the reconstructed edges are still sharp but with slight shifts. In the real experiment to film a turntable with a rotation speed of 6000r/min, the LMD, PSNR and SSIM improve from 22.2833 to 2.5167, 18.2485 to 21.8603 and 0.8345 to 0.9364 respectively. These results show that the algorithm can suppress image lag and improve reconstructed image quality.
Impact of Electric Vehicles Charging Stations Connected in a University Parking Lot

This paper presents an analysis of the impacts caused by the connection of plug-in electric vehicles charging stations in a university parking lot. A case study with realistic data from the distribution system of the campus is considered and the electric vehicles are modelled based on a probabilistic approach. Three scenarios are analyzed according to different numbers of charging stations installed in one of the buildings in the campus for a time horizon of five years. The results show that the transformer which serves the building already operates overloaded in 2021, which is not appropriate since loss-of-life may occur. System voltage remains above the minimum allowable limit for all analyzed years. The proposed method can be a helpful tool to identify under what scenarios there may be risk of operating transformer overloaded.
I-READ 4.0: Internet-of-READers for an efficient asset management in large warehouses with high stock rotation index

The I-READ 4.0 project represents an integrated and autonomous Cyber Physical System, for automatic management of very large warehouses with high-stock rotation index. Thanks to an internet of UHF-RFID readers, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in indoor scenario. The proposed solution can be employed independently on the warehouse sizes, the good types and the spatial resolution required for item localization by virtue of its low-cost, reconfigurability and scalability. The I-READ 4.0 system allows also to increase the safety levels of the human operators within the warehouse.
Ancillary services provision by aggregators and impact on distribution network operation

The growth of distributed energy resources (DER) connected at distribution network level and the current legislation concerning the participation of DER to the energy and ancillary services markets, are a huge opportunity to get flexibility services from these resources but at the same time a great challenge from the technical point of view. According to the latest indications from EU Directives, demand response and generation flexibility are encouraged in order to meet network balancing needs, but a new actor is required: the aggregator. This new participant has the duty to aggregate consumption and generation in order to offer flexibility products in the ancillary services market, while ensuring the technical feasibility and assuring revenues for its clients and itself. This work focuses on the structure and the business model of aggregators and discusses the technical implication that such role has on the distribution network.
Understanding Intelligent RAN Slicing for Future Mobile Networks Through Field Test

The future mobile networks are expected to support multiple kinds of applications with different QoS requirements. How to efficiently assign spectrum resources to different applications is still an open issue. In this paper, we first introduce our field test with customized phones for analyzing the association between application-level log and radio signal-level log. We find out that the conventional spectrum allocation scheme is inefficient use of spectrum resources. To address this issue, we design and prototype an application-specific Radio Access Network (RAN) slicing system, where we can assign each RAN slice with application-specific radio spectrum scheduling policy as well as application-specific radio resource blocks. The preliminary experimental results show the feasibility and efficiency of proposed application-specific spectrum scheduling and resource blocks allocation. Finally, we introduce how to extend our proposed application-specific RAN slicing to a product network with network intelligence.
A T-Shaped SOI Tunneling Field-Effect Transistor With Novel Operation Modes

We present a novel T-shaped tunneling field-effect transistor (TFET) on Si-on-insulator (SOI). The asymmetric source-drain structure can effectively suppress the ambipolar switching. The on-current (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm{ on}}$ </tex-math></inline-formula>)/off-current (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm{ off}}$ </tex-math></inline-formula>) ratio reaches very high value of <inline-formula> <tex-math notation="LaTeX">$\sim 10^{8}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ ds}}= - 0.5$ </tex-math></inline-formula> V with a smaller tunneling junction width at the drain. The innovative T-shape design allows integration of both TFET and metal-oxide semiconductor field-effect transistors (MOSFET) operation modes in one structure. Both TFET and MOSFET operation modes are experimentally demonstrated in this device structure, which provide the implementation of the selector function with the single device.
An RF Scanning Receiver on a Silicon Photonic Chip

We experimentally demonstrate the first on-chip broadly-tunable RF scanning receiver, using silicon photonic technology. By exploiting an innovative and simple noise-cancelling architecture, the device, fabricated using a multi-project-wafer service, achieves ultralow-noise operation in the frequency range of 0-35GHz, with an unprecedented image rejection ratio $\gt 80$ dB, in an antenna-remoting scenario.
Non-intrusive Load Identification Method Based on Integrated Intelligence Strategy

existing non-intrusive load identification methods are mostly single recognition models that based on a certain feature space. It is difficult to guarantee the global generalization performance of the model in the complex feature space. In this paper, the distribution of features of common household appliances in different feature space is analyzed and a variety of feature spaces are constructed. The Extreme Learning Machine model is selected as the base learner to train the optimal Extreme Learning Machine recognition model in the corresponding feature space. The AdaBoost algorithm is used to combine the base learner and build the ensemble extreme learning machine load recognition model. The experimental results based on the measured data showed that the accuracy of the proposed non-intrusive load identification method is obviously superior to the classical Extreme Learning Machine model and the Support Vector Machine model.
Point Cloud Generation From Multiple Angles of Voxel Grids

The advancement of deep learning technology has been concentrating on deploying end-to-end solutions using high dimensional data, such as images. Recently, a number of methods have been proposed for reconstructing 3D objects using deep learning. One such technique is the method that involves recovering 3D objects as voxel grids using one or multiple images. However, there has been very little work directed towards the generation of 3D objects represented by a set of points, i.e. point cloud, from voxel grids which are ambiguous and coarse. The development of a deep learning model that generates point clouds with details from coarse voxel grids has numerous benefits to quality of life. For example, design professionals can use this model to generate detailed 3D point clouds using a sketched and coarse voxel to enable their creativities. This paper presents an algorithm to generate point clouds from voxel grids. The algorithm explicitly loads 3D objects into voxels without projection operations and associated information loss. To obtain a comprehensive understanding of the voxel grid, the grid is analyzed through various angles, as inspired by how humans observe 3D objects. The features from various angles are passed into a GRU layer to extract patterns across views, which will then be passed to a channel-wise convolutional layer and graph convolution to generated the predicted point cloud. The experimental result of the algorithm indicates that the algorithm is capable of generating high-quality point clouds by understanding the semantic features of the voxel grid.
Frontal Synchronization Biases in Obsessive-Compulsive Disorders

Obsessive Compulsive Disorder (OCD) is one of the most common neuropsychiatric disorder in the community. Several neuroimaging systems shows that OCD causes functional disorders in the frontal lobe. In this study, the effects of the OCD on the frontal part are investigated with Inter-Channels Phase Clustering (ICPC) method. According to the Significant desynchronization was detected in slow EEG bands for 7 electrodes on the frontal lobe. These findings are consistent with the previous results that obtained by other neuroimaging devices. The results are also showed that loss of frontal synchronization cause functional disconnectivity. In addition to this, it can be concluded that OCD may cause many cognitive dysfunctions, such as loss of memory.
Optimal Operation of Battery and Hydrogen Energy Storage Systems in Electrical Distribution Networks for Peak Shaving

This paper introduces a convex model based on mixed-integer second-order cone programming (MISOCP) for the optimal operation of a battery energy storage system (BESS), and a hydrogen energy storage system (HESS) in an electrical distribution network (EDN), to provide the peak load shaving. The model minimizes the cost of the energy procurement from the substation, as well as the operational costs of the energy storage systems (ESSs). Three cases are presented according to the ESSs included in the EDN: the first one considers only the BESS operation. On the other hand, the second one considers only the HESS operation. Both the BESS and the HESS operation are carried out in the third one. The model has been tested in an 11-node radial network with solar and wind generation. Although the BESS operation reaches the greatest reductions in peak load demand and energy procurement cost, HESS is also capable to realize the peak shaving.
Investigating the Factors Influencing the Behavior of Health Care Workers for Needle Stick Injury (NSI)

Aim: Injury from needle is one of the main ways of transmitting blood diseases in the medical staff. Prevention and reduction of such diseases' side effects have been emphasized by the Health Ministry. This study aimed to investigate factors influencing the behavior of the Elam city medical staff when working with sharps instruments. Methods: In a cross-sectional study, using a census method, all healthcare staff working in the health centers of Abdanan Dehloran city (total n=66) after providing written informed consent were enrolled. Data were gathered by a questionnaire. After verifying the validity and reliability, the data were collected, and analyzed using SPSS19 and descriptive statistics, Chi-square and t-test. Findings: 59% of the participants were male; 80% were married; 62% had academic educations, and 47% worked in the nursing and injection sections. The mean scores of knowledge and attitude of the participants were 3.1±1.38 and 1.7±4.1, respectively, and just 13.4% of the subjects had not reported control tests and injury events. Conclusion: Knowledge, attitudes and practices of the medical staff of injuries from sharp objects are not desirable. it seems that empowering the staff through theoretical and practical training is proper solution in this regard.
Designing an Obstacle Detection and Alerting System for Visually Impaired People on Sidewalks

It is known that being visually impaired is one of the most challenging experiences in life and several people have been facing this situation. Since computer vision and machine learning have started to be growing fields, designing an assisting system have become simpler for both researchers and engineers. There are two fundamental elements in obstacle detection systems that are software part and hardware part. In recent years, operating systems have been reshaped around deep learning algorithms, open source libraries and programming platforms. There are several deep learning algorithms and open source libraries and in this project, we focused on programming the system with Python and using Tensorflow models to create a classifier on a tiny computer called Raspberry Pi. We used a model called ssdlite_mobilnet_v2_coco to detect 9 different objects which could present on sidewalks. Since the auditory sense is the most important sense for visually impaired people and must not be blocked, we proposed a combination of auditory and tactile senses to alert the user. A system that is called eSpeak was used to alert the user via headphones by reading the name of the detected object. At the same time, 3 different vibration sensors were located at 3 different positions as of right, middle and left. When an obstacle was detected inside one of the predefined bounding boxes, the related vibration sensor was activated with the name of the object.
Quality of Life and Related Factors in Patients with Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide and is probable to be the third cause of death in the world in 2020. COPD is a highly prevalent, irreversible, and disabling disease with no definite cure which will stay with the patient for life affecting physical, mental and socioeconomic status as well as the quality of life. Therefore, creation of a positive perception of health and preserving the quality of life in these patients are of utmost importance and should be an essential goal in treatment and care of these patients. To reach this goal, first, the quality of life in these patients should be evaluated to assess the effects of mental, social and physical aspects of the disease on the life style of these patients. Materials and Methods: A descriptive case series study was undertaken. The understudy population comprised 80 COPD patients which were selected consecutively. They all filled out the standard respiratory disease questionnaire including: 1) Demographic specifications 2) the St George's Respiratory Questionnaire (SGRQ, includes symptoms, activities, and impact). To collect the data, COPD patients who met the inclusion criteria were selected and questionnaires were filled through interviewing or selfreporting in 20 minutes. The questionnaire was scored from 0 to 100 points. The lower score indicated the better life quality and the higher score indicated deterioration. The collected data were analyzed using Microsoft Excel and SPSS 11.5 software. Results: Mean and standard deviation of the SGRQ total score in understudy patients were 58.31±16.14 (63.09±19.52 for symptoms, 76.35±17.65 for activities and 46.41±17.79 for impact parts). There were no significant differences between the SGRQ total score and sex, age, occupation and level of education. One sided variance analysis and Pearson’s correlation coefficient demonstrated a significant difference between the total score and duration of disease (p=0.01), stages of disease (p=0.02) and also the causes of COPD (p=0.02). Regarding the stages of disease, a significant difference was found between stages 2, 3 and 4 respectively according to SGRQ total score (p=0.02, P=0.04). A significant difference was also seen between smoking and a combination of factors regarding the causes of the disease
A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition

To ensure the photovoltaic (PV) arrays under partial shading condition(PSC) could still output maximum power quickly and efficiently, this work presents a modified hybrid maximum power point tracking (MPPT) method, which applies artificial neural network (ANN) to the modified perturb and observe (MP&O). Instead of using expensive illumination intensity sensors directly, the illumination intensity on each module in the PV array can be obtained indirectly by sampling the specific points of their own cheaper voltage-current sensors. ANN uses indirect illumination intensity to predict the optimal voltage areas of the global maximum power point (GMPP). Based on the areas, MP&O adopts a adaptive step size strategy to obtain GMPP. By modeling and simulation in Matlab/Simulink, it is shown that the tracking time and efficiency of the proposed method in this work can reach 0.026s and 99.87% respectively. Compared with other methods, the method has faster speed, higher efficiency, smaller fluctuation and lower complexity.
Integrating Energy Storage Systems and Transmission Expansion Planning in Renewable Energy Sources Power Systems

Energy storage systems (ESS) are more and more used in power systems where renewable energy sources (RES) are integrated. ESS can participate in frequency control and also represents a flexible solution to supply the demands in power systems. The mathematical model presented in this paper minimizes the investment costs, load shedding costs and generation costs, concomitant with integrating ESS and building new transmission lines if the capacity of the existing ones is not sufficient.
An Efficient Fault Detection and Diagnosis Methodology for Volatile and Non-Volatile Memories

Memory reliability and testability are considered as primary requirements for achieving high production yield in nowadays system on chips (SoCs). For that purpose, different testing methods and diagnosis flows were proposed in the past. The fault models and test mechanisms can be different when dealing with volatile and non-volatile memories. This paper describes an efficient test methodology for detection and diagnosis of faults in both volatile and non-volatile types of memories.