Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Street Parking Spot Detection for Smart Cities

Car parking in crowded cities is a big problem. Drivers have to do a blind search to find a free on-street parking spot. Blind searching not only causes traffic congestion but also fuel and time consumption. Indoor parking garages have sensors or light systems to point out free spots, unfortunately, an indoor approach is not applicable to the on-street parking problem due to its expensive nature. In our proposed solution, parking spots are monitored with roadside cameras. First, street images taken by roadside cameras are collected to form a dataset. Second, a Convolutional Neural Network (CNN) based on this dataset is built. Then the trained CNN analyzes a new street image to check if there is a free spot or not. In this paper, a mobile application is also developed and presented. Our mobile application takes the user’s request and triggers the corresponding roadside cameras, and then notifies the driver about the available parking spots around the region and offers navigation to the spot upon the driver’s confirmation.
Sedentary behaviour and physical activity patterns in adults with traumatic limb fracture

Objective: To describe patterns of sedentary behaviour and physical activity in adults two weeks post-hospital discharge following an upper or lower limb fracture, and identify associated predictive factors. Design: Observational study. Setting: Level 1 Trauma Centre. Participants: Adults aged 18–69 years with an isolated upper (UL) or lower (LL) limb fracture. Main Outcome Measures: Sitting time and steps measured via a triaxial accelerometer and inclinometer-based device (activPAL) (anterior thigh); and moderate-intensity physical activity (MPA) measured via triaxial accelerometer (ActiGraph) (hip) for ten days. Results: Of 83 participants, 63% were men and 55% had sustained LL fractures; mean (SD) age was 41 (14) years. Participants sat for a mean (SD) of 11.07 (1.89) h/day, took a median (IQR) of 1575 (618–3445) steps/day and had only 5.22 (1.50–20.78) mins/day of MPA. Multivariable regression analyses showed participants with LL fracture, had increased adjusted mean sitting time of 2.5 h/day relative to UL fracture (β = 2.5 hours, p < 0.001). For each day since surgery/injury there was reduced adjusted mean sitting time of 4 mins/day (β = −0.06 hours, p = 0.048). LL fracture was associated with 80% fewer steps/day (Ratio of Geometric Means (RGM) = 0.20, p < 0.001) and 89% less MPA (RGM = 0.11, p < 0.001) relative to UL fracture. Older age was associated with 59–62% less MPA relative to the youngest participants (RGM = 0.38–0.41, p = 0.01). There was no association between the predictive variables sex, BMI and pre-injury physical activity and any outcome. Conclusions: At two weeks post-hospital discharge, participants were engaged in high amounts of sitting and were physically inactive. Injury location was the strongest predictor of outcome, indicating that patients with LL fracture are most in need of encouragement to reduce sitting time and gradually increase activity, within the bounds of clinical safety.
Dual-Band High-Gain Planar Corrugated Antennas With Integrated Feeding Structure

In this paper, a dual-band high-gain planar corrugated antenna is presented for applications in space and satellite communications. An analytic method based on the unit-cell analysis of a corrugated structure is presented to estimate the resonant frequency of the corrugated antenna where the maximum broadside gain happens. Therefore, the design of periodic corrugated antenna structures, which is very time-consuming due to the large size of the structure, is reduced to the design of a unit-cell of the structure which is very fast. A 2D corrugated (bull’s-eye) antenna structure is then parametrically studied to find the parameters which affect the resonant frequency of the corrugated structures. A dual-band bull’s-eye antenna with an integrated feeder structure at center is designed for the center frequencies of 9.7 GHz and 13.85 GHz. The dual-band operation is enabled using two different corrugations realized on two laminate boards. A prototype is fabricated and measured maximum broadside realized gains of 15. 8 and 17.5 dBi are achieved for the first and second bands, respectively.
Circularly Polarized Slot Antennas With Dual-Mode Elliptic Cavity

Circularly polarized slot antennas based on dual-mode elliptic substrate integrated waveguide (SIW) cavities are proposed in this letter. Current distribution on the dual-mode cavity is analyzed first to facilitate the layout design. The antennas are divided into three categories. For the first one, three unbalanced slots are etched on the upper surface of the cavity with a central rectangular slot placed perpendicularly to the arc-shaped slots on the two sides of the long axis. For the other two antennas, two pairs of symmetric arc-shaped slots are etched on the upper surface of the cavity. One of them has an additional shorting-pin in the center. Circular polarization is achieved by simultaneously exciting two quasi-TM110 cavity modes and combining them in the 5–6 GHz frequency range. The working principle of the dual-mode antennas is theoretically illustrated in detail and experimentally verified. The proposed antennas offer attractive features, including novel working principle, simple structure, low loss, low profile, low cost, and good gain, appearing as good candidates for indoor communication.
BAIWL: Blacklisting Approach to Improve Wireless Sensor Network Lifetime

Due to their ease of deployment and autoconfiguration capabilities, the use of Wireless Sensor Networks (WSNs) to monitor environment has become popular. Sensor nodes are known for their limited energy budget. They are sometimes deployed in unfriendly area. So, changing or recharging depleted battery is a hard task. Thus, energy management becomes a real challenge for the lifetime of the network. So, each node needs to optimize its energy consumption.In most routing protocols used in WSNs, the hop count metric is very often used as a route selection criterion. For transmitting data to a given sink, this approach favors the selection of nodes in the medium of the network. These nodes are very solicited to forward data to the sink. Thus, their energies are exhausted faster than other nodes. For a better distribution of the energy consumption due to data transmissions, we proposed an approach that forbids low residual energy node to be forwarder node. When an intermediate node finds that its residual energy is lower than a predefined threshold, it bans communicating control information to its neighborhood. Its low residual energy will be only used to transmit its own data. This approach has been compared to standard routing protocols used in WSNs, namely Hop count and RPL routing protocols. The obtained results show that with the blacklisting approach, we can significantly improve the network lifetime and packet delivery ratio.
Online Structural Break Detection for Pairs Trading Using Wavelet Transform and Hybrid Deep Learning Model

Pairs trading is a statistical arbitrage strategy which first monitors two stocks whose prices are cointegrated, and then makes arbitrage when the prices of these two stocks get non-conintegrated. The phenomenon that the cointegration relationship between two stocks does not exist any longer is called structural break, and detecting structural breaks is important for pairs trading. To detect structural breaks as soon as possible, we propose in this paper a hybrid deep learning model using both frequency-domain and time-domain features to detect structural breaks. To evaluate the performance of traditional methods and our model, we collect the historical tick data of the top 150 companies from Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) for experiments. Experimental results show that our proposed method is able to detect structural breaks more accurately than tradition methods.
A Control Strategy Based on Power Forecasting for Micro-Grid Systems

Balancing production and demand of energy is the main challenge to integrate renewable energy sources (RES) in micro-grid (MG) systems. However, the variability and the uncertainty nature of the production and the consumption make the system more difficult to control. In fact, weather conditions influence on the production of renewable energy sources while occupancy influences on the power consumption. Therefore, the development of accurate short term forecasts are needed for a seamless integration of RES (e.g. photovoltaic system, wind turbine) together with the traditional electrical grid in MG systems. This paper presents a forecasting model for predicting the power production and consumption in MG systems together with the battery state of charge (SoC). A control strategy is then implemented to balance the Demand/Response by taking into account the forecasted and real-time values. Based on the data collected from a real MG system, simulation results are presented to show the effectiveness of power forecasting for MG control.
Image Encryption Based on Hash Table Scrambling and DNA Substitution

This paper proposes an image encryption algorithm based on hash table structure scrambling and DNA substitution. The algorithm uses the classical ‘scrambling-diffusion’ process, and the pseudo-random sequence used in each process is generated by the hyper-chaotic Chen system. Firstly, in the process of scrambling, two sequences with no repeated values are constructed by using the closed hash method in the hash table structure for chaotic sequences, and the plain image is scrambled twice according to the two sequences. Then, in the process of diffusion, DNA coding and decoding rules and DNA substitution rules are made according to the chaotic sequences. The image is dynamically encoded, substituted and decoded with DNA. In this way, while modifying the gray value of image pixels, it also destroys the strong correlation between adjacent pixels. Through a series of verifications, it is concluded that the algorithm not only has good encryption effect, high security and large key space, but also has the advantages of high key sensitivity and strong anti-attack capability.
Design, Fabrication, and Evaluation of the “Maya” Social Robot

In the past few years, social robots have attracted researchers' attention from leading countries in this field. These robots can play a potential role in human life, such as entertainment, therapy, and education. This paper covers the design process and fabrication of a robot called “Maya”, whose purpose is to act as an intermediary in children's medical treatment progress. Maya is an intelligent toy embedded with multimedia devices with the capability for self-presentation and other behaviors. Two of the most important factors of this type of social robots are their acceptance into everyday life and the capability to be used with children, in particular during their medical treatment process. In this study, a scenario was designed for the robot, including a poem, story, and music, to evaluate these two aspects. The results indicated that the robot has both the potential to be therapeutic and the ability to be used in the entertainment and education of children.
P1 as an objective auditory rehabilitation assessing indicator for cochlear implant children

Cochlear implants (CIs) provide a tool for hearing reconstruction. How to effectively and reasonably evaluate the auditory rehabilitation level of CI children has always been a difficult problem, especially for those with prelingual deafness. Many studies have used electroencephalograph (EEG) technology for CI users, which have shown that EEG is suitable for clinical postoperative evaluation. This study aims to explore changes of latencies and amplitudes of P1 wave in CI children under auditory stimulation. A pure tone at 1000 Hz was used as the stimuli to induce cortical auditory evoked potentials (CAEP). The characteristics of P1 wave were compared between normal hearing children and CI children over different implantation period. Results show that CI children started to have improved auditory perception after implantation, moreover, their P1 wave amplitude becomes similar to that of normal hearing children after six months. One year after implantation, the characteristics of P1 wave of CI children become similar to those of normal hearing children. Therefore, it seems that P1 could be used as an objective auditory rehabilitation assessing indicator for CI children.
Geotagging Urban Elements Using Deep-Learning and Street-Level Video

There are several works using computer vision to create databases with geotagged elements from urban areas, however, most of them are based on existing images provided by a third-party. In order to maintain up-to-date information that helps governmental, educational, economical, and private sectors decide how to invest resources, it is essential to be capable of acquiring the data on-demand. This research proposes building a prototype to capture street-level videos, using deep learning to detect the relevant objects, employing computer vision algorithms to track them, and applying least-squares to calculate their geotags. Experimental results on the object detection task using a Faster-RCNN model are carried out on a dataset of real urban imagery taken by the prototype in the city of Culiacan, showing positive results despite the high variety of appearances within objects of the same class and the challenging visual conditions of the urban scenery.
ARAS-IREF: An Open-Source Low-Cost Framework for Pose Estimation

Despite the amount of research reported on state estimation and sensor fusion in the field of robotics, there are no well known low-cost solutions for a referencing system to determine the accuracy of developed methods by providing a suitable ground truth for them. In this paper an efficient and accurate 6-DoF pose measurement system is proposed and implemented on a spherical parallel robot using IR LEDs. This approach uses the perspective-n-point algorithm to derive the transformation matrix representing the accurate relative pose of the end-effector with respect to an inertial frame. Exploiting a visible light filter in front of the camera has rendered this approach robust against illumination changes. Furthermore, it allows for mitigating the rolling shutter effects by reducing the exposure time. Finally, a custom made testing module is proposed to verify the accuracy of the proposed device, and the calibration process proves the accuracy and efficiency of the system.
Hybrid Cognitive Computing for Enabling Automated Driving

Automated driving systems are highly integrated AI systems, and also a hotspot of research in recent years. In general, driving decisions are time-sensitive, and in certain circumstances, latencies could prove fatal affecting the transition between different levels of driving automation. In this respect, the present study aims to tackle exactly this challenge on how hybrid cognitive intelligence enables automated driving systems to respond quickly to changing situations. As such, a cognitive functionality is proposed by operating on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience and, finally, selecting the optimal level of autonomy
Improved Interconnect Layout of DC Link Capacitor Bank to Minimize Parasitic Inductance and its Effect on Performance of SiC MOSFET

SiC power devices has the advantage of faster switching capability resulting in low switching losses. However parasitic inductance due to converter layout and load parasitic capacitance, affects the switching performance of the device. In presence of such parasitics and high di/dt and dv/dt due to faster switching, the device experiences huge voltage and current overshoot during switching transient which leads to failure of the device. This paper presents an interconnect layout of the DC link capacitor to minimize parasitic inductance. Appropriate capacitors of desired voltage rating are selected to minimize equivalent series inductance (ESL). A DC link capacitor bank is created by series-parallel combination to effectively reduce ESL. A double pulse test is carried out with 1200 V, 35 A SiC MOSFET to experimentally determine parasitic inductance.
Bi-Directional Clamping Based H5, HERIC and H6-Type Transformerless Inverter Topologies with Improved Modulation Technique

Transformerless inverters (TLIs) are well commercialized for Photovoltaic applications due to its small in size, less cost and high power conversion efficiency. However, the major challenge is to develop an inverter with reactive power capability and minimum leakage current. Therefore, in this paper, an improved H5, Heric, and H6-type topologies with bi-directional clamping branch are proposed to maintain constant common-mode voltage (CMV) during the freewheeling period. Thus, high-frequency variations due to switch junction capacitances in CMV are diminished and it further reduces the leakage current. Besides, an improved pulse width modulation scheme (PWM) provides a bidirectional current path during zero state to enhance the output waveform quality of the grid-connected inverter at different power factor conditions. Finally, the proposed concept is examined through the simulation results using Matlab software.
Modeling Strategies and Spatial Filters for Improving the Performance of P300-speller within and across Individuals

In recent years, improving the performance of cross-individual brain-computer interfaces (BCI) has become a research hotspot. This paper proposes a within-individual model and two cross-individual models for P300 speller character recognition and uses canonical correlation analysis (CCA) spatial filter and task-related component analysis (TRCA) spatial filter to optimize the model. Those methods are compared in their performance to allow for an accurate classification of P300 speller. As a result, the best classification accuracy rate of the within-individual recognition model is 98.83%, and the best classification accuracy rate in cross-individual model is 85.09%.
Enhanced Model for Cloud Data Security based on Searchable Encryption and Hybrid Fragmentation

Cloud computing is a new technology that transfer the computing process from personal computers into cloud servers over the internet. Nevertheless, as the client information data is stored in the cloud provider servers, the confidentiality of the information become a new concern. Different algorithms based on Encryption is presented previously to provide cloud clients with confidentiality. The main idea of encryption algorithms for cloud data is to permit cloud clients queries to be handled using encrypted data without decryption. This paper presents a new security mechanism using hybrid method of encryption algorithms and a distribution system to enhance cloud database confidentiality. A vertical fragmentation technique is adopted from alsirhani’s model for distributing data over clouds. However, to overcome a weakness in alsirhani’s model where compromises to a fragment can still make data meaningful. Instead, the proposed model uses a hybrid fragmentation technique to make data on fragments meaningless if compromised. The proposed model distributes the cloud database among the clouds using the provider views and level of confidentiality that is delivered by the employed encryption algorithms. To evaluate the proposed searchable encryption and hybrid fragmentation model, the study developed a Java application for simulating the hybrid cloud. The simulation combines public and private clouds; as essential processes is conducted inside the private cloud. The evaluation of the work was conducted by comparing the proposed model with existing solutions in query response and security characteristics. Preliminary results showed that the proposed searchable encryption and hybrid fragmentation model provides a secure mechanism that enhances data confidentiality in terms of faster response and additional security.
Prediction of Specific Language Impairment in Children Using Speech Linear Predictive Coding Coefficients

Specific language impairment (SLI) is a kind of neurodevelopmental disorders which could disturb the speech production and language comprehension ability of a child. The detection/prediction of SLI in children is done through behavioral analysis and age specific language test scores by speech therapists and child psychologists. Modern computational approaches may chop off the long prediction time to diagnose the disorder and support the child as well as parents to start ministrations early. The present work gives an idea for detection of SLI from children’s voice with the help of linear predictive coding coefficients (LPCs) as they have the ability to model human vocal system. Two classifiers: naïveBayes (NB) and support vector machine (SVM) were resorted to categorize children in both SLI and healthy groups respectively. The best classification accuracies of 97.9% (for top-20 LPC features) and 97.8% (for top-10 LPC features) were secured by NB classifier for 5-fold cross-validation protocol. The study concluded that LPC parameters play a crucial role in detection of SLI and could be put to uncover other discern neurodevelopmental disorders via children’s speech signals.
A Survey on Signal Processing Based Pathological Voice Detection Techniques

Voice disability is a barrier to effective communication. Around 1.2% of the World’s population is facing some form of voice disability. Surgical procedures namely laryngoscopy, laryngeal electromyography, and stroboscopy are used for voice disability diagnosis. Researchers and practitioners have been working to find alternatives to these surgical procedures. Voice sample based diagnosis is one of them. The major steps followed by these works are (a) to extract voice features from voice samples and (b) to discriminate pathological voices from normal voices by using a classifier algorithm. However, there is no consensus about the voice feature and the classifier algorithm that can provide the best accuracy in screening voice disability. Moreover, some of the works use multiple voice features and multiple classifiers to ensure high reliability. In this paper, we address these issues. The motivation of the work is to address the need for non-invasive signal processing techniques to detect voice disability in the general population. This paper conducts a survey related to voice disability detection methods. The paper contains two main parts. In the first part, we present background information including causes of voice disability, current procedures and practices, voice features, and classifiers. In the second part, we present a comprehensive survey work on voice disability detection algorithms. The issues and challenges related to the selection of voice feature and classifier algorithms have been addressed at the end of this paper.