Miracle-dz/UniFAR-FTUs huggingface dataset:
Color Transfer to Anonymized Gait Images While Maintaining Anonymization

Gait anonymization helps prevent the identification of people by gait recognition systems using videos uploaded to social media. Our current gait anonymization approach is to first modify the silhouette of the gait sequence and then transfer the colors of the skin, hair, clothing, etc. in the original gait images to the modified gait images to produce a final RGB anonymized gait image sequence. Since users typically care about the quality of the generated videos as they want to share them with family and friends in addition to caring about privacy, the generated videos should contain color images that are sharp and finely textured. Existing anonymization models are unable to produce such images. In this paper, we focus on color transfer while maintaining anonymization. This is challenging because the original gait images may consist of multiple colors, the modified gait differs from the original one, there is no real ground truth anonymized gait, and it may be difficult to exactly separate the foreground colors from the background colors in the original gait images. To overcome this problem, we propose transferring the colors without using ground truth and without extracting the colors in the original gait images. In this model, the overlapping region between the two gaits is first located, and the colors in that region in the original images are transferred to the modified images. The colors in the remaining region are interpolated from the color in the overlapping region, so the colors in the overlapping and non-overlapping regions are coherent. Quantitative and qualitative experiments demonstrated that the proposed model is more effective than our previous models with no reduction in anonymization.
Scheduling Algorithm Considering Interference Interval for LPWA

LoRaWAN is one of the radio communication standards of low power wide area network (LPWA) using the chirp spread spectrum. In uplink communication of LoRaWAN, packet retransmission is used to compensate for packet loss due to packet collisions caused by interference coming from other systems on the same frequency band. However, simple packet re-transmission increases packet collisions on the massive system condition since collision avoidance is not considered in LoRaWAN. Therefore, in this paper, we propose an adaptive transmission timing control algorithm for avoiding periodic interference from other systems by estimating their intervals. In our method, an environment with multiple LoRa gateways and LoRa nodes are considered and the received power of interference signals at each LoRa gateway is shared among all LoRa gateways. LoRa gateways can distinguish the system of interfering signals by comparing with the Euclid distances between the data sets of the received power of the interfering signals and the data sets of the received power of the signals come from other interfering systems. In addition, whether the interfering system communicates periodically or not is figured out from the information of the interference interval. LoRa gateways predict the timings that the LoRaWAN system is less interfered considering the periodically interfering systems and control the packet transmission timing of LoRa nodes. The simulation results show the proposed method reduces the packet loss rate compared to the existing system without a significant decrease in throughput.
A proactive caching approach in 5G networks

One of the key challenges of emerging networking technologies e.g. 5G, is handling a large number of user requests. Network caching is a well-experienced approach to addressing this challenge, through which frequently requested files are cached at the network edge to reduce the latency and traffic load of the network. Here, a Multilevel Proactive Caching (MPC) approach is suggested, which creatively exploits the effect of time on the file fame, denoted as a parameter called "boost". Boost as well as some other parameters like fame and conent volume are utilized to determine an individual parameter called precedence. The results indicate that the suggested approach, significantly enhances the cache hit ratio, backhaul load and file access latency of the network.
Implementation of Feedforward Active Noise Control Techniques for Headphones

This paper develops active noise control (ANC) system with headphones to reduce recording environmental noise, such as from bus, train and airplane. By applying an additional MEMS microphone to get reference signal at each side of headphones, the proposed ANC headphones apply TMS320C6713 as the computing core to generate the anti-noise signal to destructively interfere the undesired noise. The antinoise signal, which can be integrated with the audio signal, drives the speaker of the commercial headphones to reduce the environmental noise. Experimental results show the presented ANC headphones achieves larger bandwidth of noise reduction than the commercially available headphones.
Analysis of the Internals of MySQL/InnoDB B+ Tree Index Navigation from a Forensic Perspective

In this paper we analyze the structure and generation of InnoDB-indices, as well as navigation in this internal structures with respect to its application in digital forensics. We thus provide an overview on the internal workings of the index that can be used to detect manipulations on the underlying table space. We analyze the physical as well as the logical structure of the index pages and its relation to the very theoretical field of B+-tree forensics. There we provide a first usable forensic technique targeting a real life open source database system. Furthermore, we discuss several use cases in the course of forensic investigations, as well as applications of the outlined methods, including possible extensions to the field of file system forensics.
A low power low ripple Schmitt trigger based PWM Boost Converter for Energy Harvesting Applications

The design of monolithic power management circuits is critical in energy harvesting applications. A Pulse width modulation (PWM) boost converter with feedforward and feedback control mitigates the changes in input-output parameters and avoids the use of compensation circuit. However, the conventional design of a clock generator in feedback path employs power hungry comparators and latches. This paper proposes an improved design of clock generator replacing comparators and latches with a schmitt trigger circuit. As a result, a low power and a low ripple PWM was acheived. The design was implemented in UMC 180 nm CMOS technology generating a stable output of 3.3 V for an input range of 1.2 V - 1.6 V. Due to the application of the proposed technique, a reduced power consumption of 60.19 mW and low ripple voltage of 18.29 mV was achieved, for a maximum load current of 100 mA at an efficiency of 98%. Thus making the design suitable for energy harvesting applications.
Multi-Terrain Quadruped

Robots are deployed to perform various tasks and assist mankind. This paper describes the operation of a four-legged robot, i.e., a quadruped in real-world scenarios, with multiple terrains and obstacles. This work focuses on the design and locomotion of a sprawling-type autonomous quadruped on flat surfaces, slopes, and stairs while simultaneously implementing obstacle avoidance.
Arithmatopia: Using MOBA Game to Increase Basic Calculation Proficiency

This study is trying to determine if playing Arithmatopia, an arithmatic MOBA game with number bond gamification, can increase basic calculation proficiency, especially in terms of the fluency of single-digit addition. The game implemented Team Games Tournament as the foundation of the competitive and cooperative learning component. We use a quasi-experimental design with pre- and post-test to measure the ability to fluently solve single-digit addition exercise of two groups of first-grade students. One of the groups played our Arithmatopia game when the other did not. While both groups taught by the same math teacher, the test showed a significant difference between the pre- and post-test. The group who plays Arithmatopia has a higher increase in their average score compared to the other group.
Subband Channel Selection using TEO for Replay Spoof Detection in Voice Assistants

Recently, there is an increase in the demand for Voice Assistants (VAs) due to their convenience in accessing and controlling the household devices. To make VAs user-friendly, less strict speaker verification constraints are imposed onto them which makes VAs highly vulnerable to spoofing attacks. In this paper, authors propose the design of front-end countermeasure system against replay spoofing attack for VAs that make use of microphone array to capture spatial diversity. We exploit this microphone array information by proposing a novel approach of the subband channel selection using mathematical structure of Teager Energy Operator (TEO). These selected subband channels are used to compute proposed Teager Energy Cepstral Coefficients (TECCmax) feature set. With this approach, we gain significant improvement in the performance of replay attack detection task on VAs against the baseline feature set, i.e., Constant-Q Cepstral Coefficient (CQCC). Results indicate an absolute reduction in Equal Error Rate (EER) of 4.11% and 8.66% on development and evaluation set, respectively, of ReMASC dataset. Authors also performed classffier-level fusion of GMM, and LCNN-based back end classifiers using proposed TECCmax feature set and obtained absolute reduction of 5.98% and 10.67% on development and evaluation sets, respectively.
Variational Mode Decomposition based Image Segmentation using Sine Cosine Algorithm

Multilevel thresholding utilizing the histogrambased method is the well-known and preferred technique of image segmentation. This method generally suffers from irregularities and sharp details in the histogram, which leads to stagnation. Along with this, the computational time of this method grows exponentially as the number of thresholds increments. In this study, a freshly developed Sine Cosine algorithm is coupled with variational mode decomposition (VSCA) to overcome these problems. VSCA employed Masi entropy as a cost function for image segmentation. The outcome of the proposed algorithm examined with Non-VMD based method using quantitative parameter such as structural similarity index (SSIM), feature similarity index (FSIM), quality index based on local variance (QILV), computational time, and mean square error(MSE). The outcomes confirm that the proposed algorithm performs more reliable results than Non-VMD based techniques.
Speed Control of BLDC Motor fed from Solar PV Array using Particle Swarm Optimization

In this paper, Brushless DC motor speed has been controlled using the Particle Swarm Optimization (PSO) technique. Appropriate parameters i.e. Kp and Ki for the PI controller can be found using the PSO technique. The system is supplied from a Solar PV Array along with the MPPT technique to fetch its maximum efficiency from the solar array. The end result shows that the Solar fed controller based on PSO can control the BLDC motor speed.
PSpice modelling of the Pulsed Electro Acoustic device: A new approach to account attenuation, dispersion and reflection phenomena

The Pulsed Electro Acoustic (PEA) method is very common to study the transport of space charges inside dielectric. In most cases, the calibration of the transfer matrix does not take into account some phenomena as reflections, attenuation and dispersion of the pressure wave going through the sample. Analytical models of the PEA can help to bring this lack of information to the matrix transfer. In this study, we first introduce an electrical PSpice model, based on analytical ones, to consider the acoustic phenomena mentioned. Second, we present a new signal processing employing this PSpice model to build a PEA transfer matrix, which is used to deconvoluate a PEA signal.
Efficient FPGA Implementation of softmax Layer in Deep Neural Network

Deep neural network (DNN) is an important technology in the field of artificial intelligence. The softmax layer is one of the key component layers for completing multi-classifcation tasks. The softmax layer contains a large number of exponents and division operations, which causes it to consume a lot of hardware resources in FPGA implementation. This paper presents an efficient FPGA implementation of the softmax layer. For the exponential operation of the softmax layer, the mathematical transformation better than the Coordinate Rotation Digital Computer (CORDIC) algorithm is used to convert to subtraction and 2 exponential operation. The division is converted into a subtraction and shift operation, and added the highest non-zero bit detection processing before the operation to greatly reduce the cycle of the division operation. Experimenting with our softmax architecture on Xilinx ZCU102, the results show that the FPGA implementation of softmax layer can attain the precision of magnitude 10-6 and greatly accelerate the calculation of the softmax layer.
Research on Functional Test of Mobile APP Based on Robot

With the rapid popularization of smartphones, various applications (Application: APP) have come into being. Therefore, automated testing of mobile applications is particularly important. At present, the automated testing on the market is often completed through test frameworks. However, when using the test framework we need to consider the compatibility of the mobile platform system. What's worse, accessing multiple apps at the same time has a bad impact on accuracy. In view of these problems, a robot-based automated test system for mobile applications is proposed. Firstly, a wireless projection technology is utilized to project the screen of a mobile device onto a computer screen. Then, the optical character recognition (OCR) algorithm and the normalized correlation matching algorithm are used to position the coordinates of the elements. Finally, the robot performs a click operation under control. The experimental results show that the system can achieve cross-platform testing, including automatically testing Chinese software or English software on Android and iOS platforms, and also has high accuracy and robustness.
Familial Classification of Android Malware using Hybrid Analysis

With the developments in mobile and wireless technology, mobile devices have become important part of our lives. While Android is the leading operating system in the market share, it is also the most targeted platform by attackers. While there have been many solutions proposed for detection of Android malware in the literature, the family classification of detected malicious applications becomes important, especially where the number of mobile malware variants increases every day in the market. In this study, a solution based on machine learning and hybrid analysis is proposed for the Android malware familial classification problem. An extensive feature set including network-related features and activity bigrams is proposed. The effective static and dynamic analysis features are studied thoroughly and evaluated on Malgenome [1], Drebin [2], and UpDroid [3] datasets.
A Novel Decentralized Virtual Impedance Droop Integrating Phase Locked Loop (PLL) Control Scheme for Parallel DG Inverter in an Islanded Microgrid

The integration of distributed generation (DG) units and their control is one of the major challenges in the islanded microgrid environment. This paper introduced a novel decentralized control strategy for parallel operation of DG inverter in an islanded microgrid. The controller presented here wipes out the requirement of a wire based communication line. The controller assimilates virtual impedance drooping with the phase locked loop (PLL). The synchronous reference frame (SRF) provides active and reactive power based on frequency and voltage reference. The proposed controller requires basic loop like power control, voltage and current control. Mainly, the resistive nature of the line has poor performance with conventional droop that results in improper synchronization of the controller. Simulation is performed on MATLAB simulink platform and results are compared with the conventional droop technique. The presented controller improves synchronization, decoupling of reactive (Q) and active power (P), and the response of the system.
A Variational Autoencoder for Joint Chord and Key Estimation from Audio Chromagrams

This paper describes a deep generative approach to jointly estimating chords and keys from music signals. Although deep neural networks have widely been used for estimating various kinds of musical elements, joint estimation of multiple kinds of musical elements has scarcely been investigated so far. Given the mutual dependency between keys and chords, which both describe the harmonic content of music, we propose to use a unified deep classification model for jointly estimating chords and keys. At the heart of our study is the integration of supervised multi-task learning with unsupervised variational autoencoding for achieving improved performance and semi-supervised learning. Specifically, we formulate a deep latent-variable model that represents the generative process of chroma vectors from discrete key classes, chord classes, and continuous latent features. The deep classification model and another deep recognition model are then introduced for inferring keys, chords, and latent features from chroma vectors. These three models are trained jointly in a (semi-)supervised manner, where the generative model acts as a regularizer for the classification model. The experimental results show that the multi-task learning improves the consistency between estimated keys and chords and that the autoencoding-based regularization significantly improves the estimation performance.
Doppler Centroid Estimation with Quality Assessment for Real-Time SAR Imaging

The range Doppler algorithm (RDA) is widely used for synthetic aperture radar (SAR) imaging. Correct estimation for Doppler centroid is essential for range cell migration correction, azimuth compression, secondary range compression in RDA. This paper presents two methods to assess and improve the Doppler parameter estimation results. The symmetry of the Doppler spectrum is examined to evaluate the quality of coarse estimation of baseband Doppler centroid and the weighted least squares algorithm is used to derive the refined results. Selective window and weighted combining based on the quality index of the average power are adopted for detecting Doppler ambiguity. From the simulation results, we show that the proposed techniques with the appropriate quality index can improve the performance of estimation compared to the conventional schemes for real-time SAR signal processing.
Step-down Fibonacci switched capacitor converters- a topological design method

Fibonacci step down converters are an attractive proposition in the realm of integrable switching power management as they give a very high step-down ratio per capacitor as compared to other switched capacitor topologies such as series parallel, ladder. This paper puts forth a generalized method of synthesizing and describing step-down Fibonacci converters. Based on charge conservation a simplistic theoretical proof of the proposed synthesis procedure is also presented. Useful closed form expressions for parameters such as flying capacitor voltage ripple and equivalent output resistance have also been derived.
Deep Neural Network for Melanoma Classification in Dermoscopic Images

Melanoma classification in dermoscopic images is a very challenging task on account of the low contrast of skin lesions, the various forms of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions and artifacts of dermoscopic images such as dark lighting. In this paper, we investigate pathological course of outlier lesions developing to be melanoma and try to meet the above challenges by proposing a novel neural network based on Efficient-B5. Compared with existing approaches, our deeper, wider and higher resolution network can capture far more complex and more fine-grained feature representations for melanoma classification. In order to evaluate model performance, we conduct a variety of experiments. The experimental results on a large publicly available dataset ISIC 2020 Challenge Dataset, which is generated by the International Skin Imaging Collaboration and images of it are from several primary medical sources, have demonstrated the significant performance gains of our proposed network compared with prior popular melanoma classifiers, ranking the first in melanoma classification.
Near-Infrared Off-Axis Integrated Cavity Output Spectroscopic Gas Sensor for Real-Time, In Situ Atmospheric Methane Monitoring

A near-infrared methane (CH<sub>4</sub>) sensor system was demonstrated by using off-axis integrated cavity output spectroscopy (OA-ICOS), which was an effective gas-phase analytical technique and was adopted for fast, real-time and <italic>in-situ</italic> ambient atmospheric CH<sub>4</sub> monitoring. A 60cm-long cavity with an effective absorption path length of 2150 m was fabricated with a fast response time of ~0.8 s. An Allan deviation analysis yielded a minimum detection limit of 13 parts-per-billion in volume (ppbv) using wavelength modulation spectroscopy (WMS) technique, which corresponds to a noise equivalent absorption sensitivity (NEAS) of <inline-formula> <tex-math notation="LaTeX">$9.7\times 10^{-10}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-1}\cdot $ </tex-math></inline-formula>Hz<sup>−1/2</sup>. By applying multiple denoising scheme of wavelet transform (WT), Kalman filtering (KF) and empirical mode decomposition (EMD) method, the precision levels of CH<sub>4</sub> measurements were improved by a factor of 4, 5 and 6, respectively. For field application, dual-gas sensing was implemented for simultaneous measurement of CH<sub>4</sub> and water vapor (H<sub>2</sub>O) in a single sensor system. Also, a three-day continuous monitoring of atmospheric CH<sub>4</sub> concentration levels was carried out to verify the validity and reliability of the demonstrated ppbv-level CH<sub>4</sub> sensor system.
Discovery of Event-Related Potentials during a Cognitive Process of Comparison Operation

Cognitive processes and mental states of software developers have been investigated using electroencephalogram (EEG) in software engineering. However, the feasibility of eventrelated potential (ERP) analyses for program comprehension were rarely examined. In this study, we aimed to discover the ERPs related to program comprehension. We conducted an experiment to capture ERPs with ten software developers during a cognitive process of comparison operation as one of the simple program comprehension processes. As results, ERP waveforms were observed in the EEG data of four subjects. In addition, we confirmed the inter-stimulus difference in peak amplitudes and peak latency.
Slow-Wave Microstrip Line Model for PCB and Metallic-Nanowire-Filled-Membrane Technologies

An electrical model composed of lumped elements is proposed for slow-wave microstrip lines that use grounded blind vias/nanowires to achieve the slow-wave effect. The main difference of this model, from the traditional RLGC model for transmission lines, is the modeling of the blind vias/nanowires that consider a mutual inductive coupling considered between sections of the model. Two transmission lines on two technologies are analyzed: printed circuit board (PCB) and metallic nanowire-filled membrane (MnM) substrate. The calculi for each component of the model are detailed. Electrical simulations are done, and a comparison between the measured and simulated data is shown. It is shown that the electrical model can predict the transmission lines’ behavior, especially the dispersion on the results that happen in frequency.