Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Phase Dual Active Bridge Power Converter Implementation in Power Supply System of Telescope Rotary Support Device

The article considers the method of uninterruptible power supply realization for the case of longterm grid voltage dips to provide work of the telescope rotating support device without reduction of its dynamics. That allows to provide full operation range for the telescope system in condition of partial power supply from grid. Mathematical model of three phase dual active bridge-based power converter is presented which consist of rectifier, power filter, three phases dual active bridge and inverter with permanent magnet synchronous machine drive and takes into account parameters of the electric drive of the azimuth axis trajectory measurements telescope.
Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis

Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multi-instance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.
The Design of Cable Arrangement System for Towing Winch and Its Control Algorithm

In order to improve the adaptability of the cable arrangement mechanism of towing winch for cable with different diameter and ensure the arrangement of the cable on the drum. One type of towing electric winch is studied in this paper, the control system of cable arrangement mechanism is designed, and the mathematical model of the speed control system is built based on balance equation, the intelligent PID algorithm is used in the cable arrangement control system for towing winch. The intelligent PID algorithm is compared with the conventional PID algorithm through simulation experiments. The results show the accuracy of the algorithm in this paper, and the control algorithm can be applied to the cable arrangement control system of towing winch.
Wideband Microstrip Antenna Design for Power Harvesting

A 1×2 array microstrip patch antenna with defected groundplane structure (DGS) has been designed and simulated. This antenna design is intended for power harvesting application. Several simulation steps have been conducted such as simulating single microstrip antenna without DGS, then simulating single microstrip antenna with DGS, and simulating 1×2 array microstrip antenna without DGS. The last simulation yields wider bandwidth but the gain is still low. Antenna design still needs improvement to fulfill power harvesting requirements.
Tightly Coupled Semantic RGB-D Inertial Odometry for Accurate Long-Term Localization and Mapping

In this paper, we utilize semantically enhanced feature matching and visual inertial bundle adjustment to improve the robustness of odometry especially in feature-sparse environments. A novel semantically enhanced feature matching algorithm is developed for robust: 1) medium and long-term tracking, and 2) loop-closing. Additionally, a semantic visual inertial bundle adjustment algorithm is introduced to robustly estimate pose in presence of ambiguous correspondences or in feature sparse environment. Our tightly coupled semantic RGB-D odometry approach is demonstrated on a real world indoor dataset collected using our unmanned ground vehicle (UGV). Our approach improves traditional visual odometry relying on low-level geometric features like corners, points, and planes for localization and mapping. Additionally, prior approaches are limited due to their sensitivity to scene geometry and changes in light intensity. The semantic inertial odometry is especially important to significantly reduce drifts in longer intervals.
HIDROID: Prototyping a Behavioral Host-Based Intrusion Detection and Prevention System for Android

Previous research efforts on developing an Intrusion Detection and Prevention Systems (IDPS) for Android mobile devices rely mostly on centralized data collection and processing on a cloud server. However, this trend is characterized by two major limitations. First, it requires a continuous connection between monitored devices and the server, which might be infeasible, due to mobile network’s outage or partial coverage. Second, it increases the risk of sensitive information leakage and the violation of user’s privacy. To help alleviate these problems, in this paper, we develop a novel Host-based IDPS for Android (HIDROID), which runs completely on a mobile device, with a minimal computation burden. It collects data in run-time, by periodically sampling features reflecting the utilization of scarce resources on a mobile device (e.g. CPU, memory, battery, bandwidth, etc.). The detection engine exploits statistical and machine learning algorithms to build a data-driven model for the benign behavior. Any observation failing to match this model triggers an alert, and the preventive agent takes proper countermeasure(s) to minimize the risk. HIDROID requires no malicious data for training or tuning, which makes it handy for day-to-day usage. Experimental test results, on a real-life device, show that HIDROID is well able to learn and discriminate normal from malicious behavior, with very promising accuracy of up to 0.9, while maintaining false positive rate by 0.03.
Compact Bandpass Filter using QMSIW cavity with slot resonator

In this paper, a compact quarter-mode substrate integrated waveguide bandpass filter is designed. Here, The slot resonator is etched on top layer of QMSIW cavity and decreases QMSIW cavity resonant frequency. Therefore, a new compact bandpass filter is designed using the proposed QMSIW cavity with slot resonator. To validate the proposed method, a second-order bandpass filter is designed on RT/Duroid 5880 substrate, thickness 0.79 mm using HFSS full-wave EM simulator. The simulation results are very much similar to Advanced design system (ADS) circuit simulation results.
High Open-Circuit Voltage in Wide-Bandgap Perovskite Photovoltaics with Passivation Layers Based on Large Cations

In this work, passivation interlayers are investigated that enable high open-circuit voltage (VOC) in wide-bandgap perovskite (WBP) solar cells. The interlayers are fabricated by processing a thin film of a large cation precursor on top of a 3D WBP thin-film (EG ~ 1.72 eV). The large cations n-butylammonium bromide (BABr) and ethylammonium bromide (EABr) show promising performance in WBP solar cells with stable power output conversion efficiencies of up to 19.4% and 17.9%, respectively. The corresponding VOC of these devices reaches values up to 1.31 V and 1.27 V for BABr and EABr, respectively. Compared to the reference devices without passivation interlayer (VOC ~ 1.23 V), this corresponds to an improvement in VOC of up to around 80 meV (BABr) and 40 meV (EABr), respectively. This enhancement in VOC originates from a reduction in non-radiative recombination losses. For the case of BABr, we reported already in a previous publication that a hybrid 2D/3D perovskite heterostructures is formed. Building on these results, in this proceeding paper we compare the performance of WBP solar cells using the large cation BABr and EABr for the processing of a passivation interlayers.
3D Point Cloud Enhancement using Unsupervised Anomaly Detection

3D point cloud is increasingly getting attention for perceiving 3D environment which is needed in many emerging applications. This data structure is challenging due to its characteristics and the limitation of the acquisition step which adds a considerable amount of noise. Therefore, enhancing 3D point clouds is a very crucial and critical step. In this paper, we investigate two promising unsupervised techniques which are One-Class SVM (OCSVM) and Isolation Forest (IF). These two techniques optimize the separation between relevant/normal points and irrelevant/noisy points. For evaluation, three metrics are computed, which are the processing time, the number of detected noisy points, and Peak Signal-to-Noise (PSNR) in order to compare the both proposed techniques with one of the recommended filters in the literature which is Moving Least Square (MLS) filter. The obtained results reveal promising capability in terms of effectiveness. However, OCSVM technique suffers from high computational time; therefore, its efficiency is enhanced using modern Graphics Processing Unit (GPU) with an average rate improvement of 1.8.
CNN Based Road User Detection Using the 3D Radar Cube

This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar target- and object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets’ positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target- and object-wise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.
Multi-task Enhanced Dam Crack Image Detection Based on Faster R-CNN

To improve the detection accuracy for multiple small targets with Raster R-CNN model, we propose a Multitask Enhanced dam crack image detection method based on Faster R-CNN (ME-Faster R-CNN) to adapt the detection of dam cracks in different lighting environments and lengths. To solve the problem of insufficient samples of dam cracks, transfer learning methods are utilized to assist network training and data enhancement. In the ME-Faster R-CNN, ResNet-50 network is firstly adopted to extract features of original images and obtain the feature map. Then, the features map is input into multi-task enhanced RPN module to generate the candidate regions through adopting the appropriate size and dimension of anchor box. At last, the features map and candidate regions are processed to detect the dam cracks. Experimental results demonstrate that ME Faster R-CNN with transfer learning can obtain 82.52% average IoU and 80.08% average precision mAP, respectively. Compared with Faster R-CNN detection method with the same parameters, the average IoU and mAP can increase 1.06% and 1.56%, respectively.
Multiplier Using NAND Based Compressors

In this paper, NAND based 5:3 compressor is proposed and is used to implement a high order 15:4 compressor. The 15:4 compressor's performance that uses proposed 5:3 compressor is compared with the implemented 15:4 compressor using existing low order compressors such as 6:3, 7:3 & using full/half adders. Compressors are used to add the partial product terms in the multiplier design at various stages. All the low order compressors use stacking approach to minimize the number of XOR gates along the critical path that uses basic logic gates for implementation. As per the proposed idea all the low order compressors are designed using only NAND gates for the comparison purpose and are in turn used to implement high order 15:4 compressor. Usage of NAND gates only in the design improves the design uniformity and gives better comparison in terms of the delay through the critical path. The optimum result for area, power and delay is observed for the 15:4 compressor implemented using 5:3 compressor proposed in this paper. This optimized 15:4 compressor as a major block of high order compressor, along with required number of other low order compressors, is used to implement a 16×16 multiplier and compared with existing 16×16 Wallace tree multiplier explained in literature survey. The functional simulation is carried out using Xilinx and the performance comparison is done using Cadence RTL compiler at 90 nm technology. The result shows that there is an improvement of 6.01%, 4.243%, & 9.97% with respect to area, power & delay respectively, in 16×16 multiplier using proposed idea when compared with existing 16×16 Wallace tree multiplier.
An SoC-Based System for Real-time Contactless Measurement of Human Vital Signs and Soft Biometrics

Computer vision (CV) plays big role in our current society's life style. The advancement of CV technology brings the capability to sense human vital sign and soft biometric parameters in contactless way. In this work, we design and implement the contactless human vital sign parameters measurement including pulse rate (PR) and respiration rate (RR) and also for assessment of human soft biometric parameters i.e. age, gender, skin color type, and body height. Our designed system is based on system on chip (SoC) device which run both FPGA and hard processor while provides real-time operation and small form factor. Experimental results shows our device performance has mean absolute error (MAE) 2.85 and 1.46 bpm for PR and RR respectively compared to clinical apparatus. While, for soft biometric parameters measurement we got unsatisfied results on age and gender estimation with accuracy of 58% and 74% respectively. However, for skin color type and body height measurement we reach high accuracy with 98 % and 2.28 cm respectively on both parameters.
Contacting n+ Poly-Si Junctions with Fired AZO Layers: A Promising Approach for High Temperature Passivated Contact Solar Cells

Polysilicon (poly-Si) based passivating contacts are promising to improve silicon solar cells conversion efficiency. However, the use of Transparent Conductive Oxides (TCO) has to be considered to improve lateral conductivity while maintaining good optical and surface passivation properties especially for bifacial devices. In this work different Aluminum-doped Zinc Oxide (AZO) based layers have been investigated after high temperature firing steps to contact Phosphorus-doped poly-Si layers. Contact resistivity below 100 mΩ. cm2 at the AZO/n+ poly- Si interface and stable implied open circuit voltage values (> 715 mV) have been obtained for firing temperatures from 550°C to 900°C. Moreover, the use of capping layers allows to maintain highly conductive AZO layers upon annealing. This novel high temperature contacting method via indium-free TCOs, is particularly interesting for the industrial integration of poly-Si based passivated contacts and provides new perspectives for advanced homojunction solar cells.
Indonesian Music Genre Classification on Indonesian Regional Songs Using Deep Recurrent Neural Network Method

Indonesia has a diverse genre of music (music genre) and is spread throughout the provinces in Indonesia. Indonesian music genres include angklung, gamelan, and kulintang. The Indonesian music genre has some similarities in terms of sound such as angklung and kulintang because it is made of wood and gamelan which has various types such as Javanese gamelan and Balinese gamelan which have a similar sound so that listeners are difficult to use to stream the right music. Because of this, the Indonesian music genre is more difficult to recognize because the instruments that build are diverse and have similarities with each other. One of the tools that can be used to facilitate the introduction of the Indonesian music genre is classification. The classification carried out needs to find the appropriate parameters to determine an accurate Indonesian music genre. In this study using the mel-spectrogramand the Deep Recurrent Neural Network (DRNN)method for music classification problems. The parameters and DRNN architecture tested are dropout value, the number of Gated Recurrent Unit(GRU) hidden layer, and the output activation. In this study using 0.25; 0.5; 0.75 as a dropout value, 4, 5, and 6 as the number of GRU hidden layer, and sigmoid, softmax as the output activation. The data used are 1000 music clips with 30 seconds duration of 192kbps quality obtained from youtube as well as compact disk (CD). The highest accuracy value of 83.28% is obtained by using a dropout value of 0.25, the number of GRU hidden layers of 6, and the output activation of softmax.
A Compact Dual-Wideband Bandpass Filter Based on Open-/Short-Circuited Stubs

A dual-wideband bandpass filter is proposed and analyzed, which is based on loaded open-/short-circuited stubs. The dual-band filter consists of a second-order bandpass filter and a third-order bandpass filter, which can be designed separately. The bandwidth of two passbands can be adjusted by tuning the impedance and the length of the stubs. A dual-band bandpass filter prototype operating at 2.4/5.2 GHz is designed for demonstration. It shows a low insertion loss of 0.3 dB and wide bandwidth of 51.9% at 2.4 GHz and 23.3% at 5.2 GHz. The filter structure is simple without narrow coupling gap between resonators, which is easy for fabrication.
Estimation of Actual Evapotranspiration in Fragmented Mediterranean Areas by the Spatio-Temporal Fusion of NDVI Data

Actual evapotranspiration <inline-formula><tex-math notation="LaTeX">$(\text{ET}_{{\rm{A}}})$</tex-math></inline-formula> is a fundamental component of the land water cycle that can be predicted by the combination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient application of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the <inline-formula><tex-math notation="LaTeX">$\text{ET}_{{\rm{A}}}$</tex-math></inline-formula> estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground <inline-formula><tex-math notation="LaTeX">$\text{ET}_{{\rm{A}}}$</tex-math></inline-formula> observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal vegetation evolutions of the three study sites, which negatively affects the performance of <inline-formula><tex-math notation="LaTeX">$\text{ET}_{{\rm{A}}}$</tex-math></inline-formula> simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the <inline-formula><tex-math notation="LaTeX">$\text{ET}_{{\rm{A}}}$</tex-math></inline-formula> estimates both in terms of correlation and errors. The improvements are particularly evident during the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.
Performance Evaluation of Active and Passive Haptic Feedback in Shape Perception

Haptic technology allows producing computergenerated signals that a perceiver experiences through touch. In general, haptic devices can be classified as active and passive. In this paper, we present a performance evaluation of a group of 10 voluntary subjects in a task of shape perception when both active and passive kinesthetic haptic feedback are provided. The results of a series of psychophysical experiments show that active haptics is superior for shape recognition: active haptics enhances the perception of surfaces while passive haptics conveys the sensation of being guided along pathways. In addition, the guidance nature of passive haptics makes the pathway direction easy to recognize. This last observation can be effectively exploited in more challenging tasks such as navigation/mobility assistance.
A novel design of W-band dual reflector antenna with circular polarization

A high-performance dual reflector antenna with good circular polarization is proposed in this paper. The antenna consists of a square-caliber horn (7 mm*7 mm) with septum circular polarizer, a shaped sub-reflector with width as 100 mm and height as 100 mm and a parabolic cylinder antenna with width as 1150 mm and height as 220 mm. The designed antenna has a pencil-like beam in azimuth plane and shaped beam in elevation plane. Simulation results show that the feed has rotationally symmetric beam with port reflection coefficients below -25 dB, axial ratios below 1.2 dB from 92.5 GHz to 97.5 GHz, the gain 17.3 dB at the center frequency in the working band. The sub-reflector converts the beam from the feed horn to desired shape in elevation plane and wide beam that efficiently illuminating the main reflector. The main reflector focuses the beam from sub-reflector as pencil-like beam. Simulation results show that the gain of the whole antenna system is 44.9 dBi and 3-dB beam width is about 0.16° in azimuth plane at 95 GHz.
Hydrogen Gas Production Using Water Electrolyzer as Hydrogen Power

The commonly consumed energy currently comes from fossil fuels that cannot be renewed that decrease over time. The depletion of fossil energy reserves encourages people to find renewable energy sources, and one of them comes from hydrogen fuel. By utilizing water, which is very abundant on earth, hydrogen can be a potential alternative fuel. The process of producing hydrogen from water is called the electrolysis process of water. In this study, a water electrolyzer prototype was designed. The amount of water used in this study is 6 liters varied with variable concentration changes of 10, 20, 30, 40, and 50% NaOH solutions. The optimal production of hydrogen gas achieved at the concentration of 50% NaOH solution to produce a gas of 4.5902 liters and the moll percentage of hydrogen in the product is 78.34%. The results of gas chromatography analysis show that the hydrogen content in the product is 77.85%; 10.86%, nitrogen 11.24%; and carbon dioxide 0.07%. The electrical efficiency obtained reaches 82% with the heat loss of 17.92%.