Miracle-dz/UniFAR-FTUs huggingface dataset:
A refinement on detection in cell counting

Cell counting is an important task and it’s difficult for the naked eyes to keep counting cells one by one for a long time. In this paper, we use multiple random forests (RFs) to detect cells and propose a post-processing to refine the detection result. Each RF generates a different detection result and averaging all detection results can decrease the count error. For some false detected cells approved by a few RFs, they have low confidence in the averaged detection result. To improve the count accuracy further, we propose a post-processing to remove the low-confident detections and enhance the high-confident detections. We validate our proposed method on two cell datasets and prove that our proposed method can refine the count result and improve the performance.
RFID Reader Antenna Optimal Deployment Based on Improved Elephant Herding Algorithm

With the rapid development of radio frequency identification (RFID) technology, in a variety of special environments (such as factories, warehouses, prisons), the demand for optimal deployment of RFID reader antennas has attracted extensive attention. At present, the deployment of RFID reader antenna is very difficult, there are many constraints and it is difficult to find the optimal solution, this paper proposes an improved element herding algorithm(IEHO) In the RFID network planning, the weighted coefficient method is used to optimize the four objectives of coverage, load balance, economic benefit and reader conflict at the same time. The experimental results show that compared with the bacterial foraging optimization algorithm (MC-BFO), particle swarm optimization (PSO) and constrained bat algorithm (BA-OM), the coverage increases by 9%, 17% and 22% respectively. The load balance is increased by 10.91% and 56.60% respectively, and the reader interference problem is well improved.
Optical Reflection Interference Equalization for Single-Wavelength Bidirectional WDM-PON Transmission System

This paper proposes a novel return light cancellation technique using an adaptive interference equalizer in the wavelength division multiplexed passive optical network (WDM–PON) transmission system employing the single-wavelength bidirectional transmission scheme. In this scheme, the same wavelength is assigned to both the uplink and the downlink. In other words, the number of the wavelengths allocated to optical network units can be half compared with a typical PON system. However, this presents us with a problem in that the reflected component of the transmitted signal light interferes with the received signal light. We investigated the performance of the proposed interference canceling equalizer using back-to-back transmission simulation and a dual-polarization-quadrature-phase-shift-keying signal transmission experiment. The results indicated that the proposed adaptive equalizer enabled reception with no error when the OSNR was sufficiently large, even when the power of the interfering light was large (i.e., SIR $\geq$ 2 dB).
A Fully Integrated Gas Detection System With Programmable Heating Voltage and Digital Output Rate for Gas Sensor Array

In this paper, a gas detection system for <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> MEMS gas sensor array is proposed. It has multiple measurement channels, programmable heating voltage, programmable measurement time, 24-bit digital output with 8-bit accuracy, wide measurement range and high linearity. The chip is realized in SMIC 180 nm complementary metal oxide semiconductor technology with an area of 1.58 mm <inline-formula> <tex-math notation="LaTeX">$\times1.66$ </tex-math></inline-formula> mm. The result of the fixed resistance measurement shows that the linearity of the resistance ranges (<inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-200 <inline-formula> <tex-math notation="LaTeX">$\text{K}\Omega $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-500 <inline-formula> <tex-math notation="LaTeX">$\text{K}\Omega $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-1 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-2 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-5M<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-10M<inline-formula> <tex-math notation="LaTeX">$\Omega$ </tex-math></inline-formula>) are all less than 1% at different measurement time intervals. At 1 s measurement time, the circuit has a wide detectable resistance range (<inline-formula> <tex-math notation="LaTeX">$200~\Omega $ </tex-math></inline-formula>-10 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega$ </tex-math></inline-formula>), and high linearity of 0.3047%. The experimental results combined with the resistive MEMS gas sensor array show that the circuit has a good detection effect in the gas concentration range (0 ppm-340 ppm), and can even detect the change of gas concentration at a low concentration of 0.1 ppm. Moreover, the system can detect different responses of the same sensor at different heating voltages.
A New Gysel Out-of-Phase Power Divider With Arbitrary Power Dividing Ratio Based on Analysis Method of Equivalence of N-Port Networks

An equivalent method has been presented to analyze an <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-port network composed of transmission lines or coupled lines. Based on this method, a three-port coupled line with one-port short-circuited can be seemed equal to another three-port network without port coupling at the central frequency. A new Gysel out-of-phase power divider with output port spacing approaching 0 wavelength at the central frequency, arbitrary power dividing ratio, and arbitrary terminal real impedance has been proposed to reduce the size and output port spacing. A power divider with power dividing ratio <inline-formula> <tex-math notation="LaTeX">$k^{2} = 3^{2}$ </tex-math></inline-formula> and a power divider with power dividing ratio <inline-formula> <tex-math notation="LaTeX">$k^{2} = 1000$ </tex-math></inline-formula> have been implemented on printed circuit board (PCB). It can be observed that there is good agreement between the simulated and measured results.
Improved Moisture Barrier Properties of High Temperature Vulcanized Silicone Rubber by Direct Fluorination

Our previous results have shown that surface hydrophobicity of a high temperature vulcanized silicone rubber can be significantly enhanced by direct fluorination. In this report, the surface fluorinated sample together with the virgin sample as a reference have been investigated on their water vapor barrier properties at $30^{\circ}C$ or $60^{\circ}C$ in a humidity environment of 75% or 97% RH. The experimental results show that the fluorinated surface layer has a high barrier to water vapor ingress into the interior of the SIR, so that the surface fluorinated sample has a much lower absorption of moisture in comparison with the virgin sample under the same conditions. SEM images show that the fluorination leads to the formation of a fluorinated surface layer with a thickness of 0.56 $\mu m$ and a nanostructured surface. The improvement of the barrier properties is attributed to a decrease in free volume of the surface layer as well as the increase in surface hydrophobicity by the fluorination, as indicated by contact angle measurements and ATR-IR and XPS analyses.
Anisotropic Data-Specific Wavelets for Structure-aware Image Processing

This paper proposes a novel anisotropic data-specific wavelets (ADW) for structure-preserving image analysis and processing. Our ADW is founded upon the rigorous mathematical derivation of hierarchical heat diffusion coupled with graph Laplacian, which not only inherits powerful properties from Mexican hat wavelets in spirit but also intrinsically encodes multi-level image properties. First, we construct anisotropic heat kernel by embedding the intrinsic image properties into graph Laplacian, and on such basis, ADW is derived from the heat kernel difference of adjacent layers in image pyramid, which is equivalent to the negative first-order derivative of the fundamental solution of heat diffusion equation with respect to time. At the application fronts of ADW, we perform various image processing experiments and conduct quantitative comparisons with other state-of-the-art methods to demonstrate the versatility and superiority of our method.
Significance of CMVN for Replay Spoof Detection

In this paper, significance of the Cepstral Mean and Variance Normalization (CMVN) is investigated for replay Spoofed Speech Detection (SSD) task. Literature shows that application of the CMVN produces significantly better performance on many feature sets, which is counter-intuitive for replay SSD task. This behaviour is analyzed by performing experiments for environment-independent and dependent cases with % Equal Error Rate (EER) as evaluation metric. Furthermore, analysis is also performed with the help of estimated probability density function (pdf) of the genuine vs. spoof speech feature representations. The experiments are performed on the publicly available and statistically meaningful ASVspoof 2017 version-2 dataset using well-known CQCC-GMM and LFCC-GMM SSD systems. This dataset comprised of seven acoustic environments for replay speech. This study reveals that performance of the SSD system is better with application of the CMVN on environment-independent case. Whereas performance degrades drastically on environment-dependent scenario with application of the CMVN. For this scenario, the CMVN suppresses the transmission channel distortion, which is in fact the discriminative cues for the genuine vs. replay speech signal. This results in degradation of the performance. However, for environment-independent scenario, CMVN scale down the variability in feature space across the different environment, which improves the performance.
A PCA-Based Method to Select the Number and the Body Location of Piezoresistive Sensors in a Wearable System for Respiratory Monitoring

This work aims at selecting the number and the body location of piezoresistive sensors to instrument wearable systems for respiratory rate (RR) estimation. We tested a novel method based on Principal Component Analysis on 10 healthy male subjects, who were asked to perform six trials on a treadmill (at rest, during walking and low speed running). We monitored RR using both a smart garment composed of six piezoresistive sensors and a flowmeter, used as reference signal. On the basis of the results, we propose the following guidelines: i) breathing assessment at rest requires one sensor, placed on the lower thorax; ii) low speed walking assessment requires three sensors, placed on upper thorax, lower thorax and abdomen; iii) high speed walking and running assessment requires a four-sensor configuration (one sensor placed on the upper thorax, one on the lower thorax and two on the abdomen); iv) an effective assessment can be performed at all speeds using the above-mentioned four-sensor configuration, denoted as General Purpose Configuration.
Orbital-Angular-Momentum-Based Radar Imaging By Dice Regularized Orthogonal Matching Pursuit

Without relative motion, the orbital angular momentum (OAM) based radar can obtain the 2-D image of targets. However, the existing OAM based imaging algorithms perform weakly in robustness or computational efficiency. In this paper, we make use of the sparse property of the targets in the image region and propose a novel imaging algorithm. As a canonical sparse recovery technique, the matching pursuit method is firstly applied in radar imaging realm. A new measurement function called dice coefficient is introduced to improve the algorithm performance. Simulation results demonstrate that the imaging radar based on the proposed algorithm can achieve robust 2-D high-resolution imaging at a low computational cost. Furthermore, the potential for extremely high-resolution imaging in a small image region has been also indicated in this work.
An Adaptive Wavelet Approach for Detection of Breathiness in Speech

Speech production is related to the breathing process, which gets affected if a person performs any heavy physical exercise. It causes perceptual, temporal, and spectral characteristic changes in the speech utterance. In this work, the subband characteristics of the Out-of-breath speech have been analysed. The subband decomposition is achieved by Empirical Wavelet Transform (EWT), which produces a set of intrinsic mode signals. Features are extracted from each mode, and they are combined using the Temporal pyramid matching (TPM) technique. TPM ensures the feature vector sizes across utterances remain fixed. The separability of the features is evaluated using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers. The performances are measured in a speaker-independent fivefold cross-validation approach. SVM showed a 75.41% accuracy of separating normal and out-of-breath speech compared to ANN with an accuracy of 73.53%.
Determination of dihedral Ψ angles in large proteins by combining NHN/CαHα dipole/dipole cross‐correlation and chemical shifts

We propose a strategy based on the combination of experimental NHN/CαHα dipole/dipole cross‐correlated relaxation rates and chemical shift analysis for the determination of Ψ torsion angles in proteins. The method allows the determination of a dihedral angle that is not easily accessible by nuclear magnetic resonance (NMR). The measurement of dihedral angle restraints can be used for structure calculation, which is known to improve the quality of NMR structures. The method is of particular interest in the case of large proteins, for which spectral assignment of the nuclear Overhauser effect spectra, and therefore straightforward structural determination, is out of reach. One advantage of the method is that it is reasonably simple to implement, and could be used in association with other methods aiming at obtaining structural information on complex systems, such as residual dipolar coupling measurements. An illustrative example is analyzed in the case of the 30‐kDa protein 6‐phosphogluconolactonase. Proteins 2006. © 2006 Wiley‐Liss, Inc.
Centroid-Tracking-Aided Robust Object Detection for Hospital Objects

COVID-19 outbreak has a big impact to people’s daily life in 2020, especially in healthcare sector. As COVID-19 viruses are highly contagious, it is important to take strict measures to ensure all patients got the needed care while taking healthcare workers safety into consideration. Robot-based care is being hurriedly developed recently, and one of the important abilities for such robot is to be able to distinguish object commonly found in hospital thus the robot can make the correct action towards the correct object. For this publication, an object detector is trained to detect the hospital bed, thus it can be an input to the care robot navigation system when it is going to approach patients. As hospital beds vary from one brand to another, and this research has limited time constraint and readily available hardware, the object detector confidence is still low. Thus, a centroid tracking method is implemented to aid the hospital object detection, ensuring the robot can detect the correct bed more robustly with considerable speed for embedded implementation.
56 GBaud O-Band Transmission using a Photonic BiCMOS Coherent Receiver

We present a photonic BiCMOS coherent receiver dedicated to the O-band and achieve detection at up to 56 GBd using quadrature-phase shift-keying (QPSK) at various optical signal-to-noise ratios (OSNRs). Transmission experiments are performed and potential applications within that power budget are discussed.
Speaker-invariant Psychological Stress Detection Using Attention-based Network

When people get stressed in nervous or unfamiliar situations, their speaking styles or acoustic characteristics change. These changes are particularly emphasized in certain regions of speech, so a model that automatically computes temporal weights for components of the speech signals that reflect stress-related information can effectively capture the psychological state of the speaker. In this paper, we propose an algorithm for psychological stress detection from speech signals using a deep spectral-temporal encoder and multi-head attention with domain adversarial training. To detect long-term variations and spectral relations in the speech under different stress conditions, we build a network by concatenating a convolutional neural network (CNN) and a recurrent neural network (RNN). Then, multi-head attention is utilized to further emphasize stress-concentrated regions. For speaker-invariant stress detection, the network is trained with adversarial multi-task learning by adding a gradient reversal layer. We show the robustness of our proposed algorithm in stress classification tasks on the Multimodal Korean stress database acquired in [1] and the authorized stress database Speech Under Simulated and Actual Stress (SUSAS) [2]. In addition, we demonstrate the effectiveness of multi-head attention and domain adversarial training with visualized analysis using the t-SNE method.
Robust Speech Dereverberation Based on WPE and Deep Learning

Reverberation has a considerable impacts on speech quality and intelligibility. Weighted prediction error (WPE) employs a linear regression model to estimate late reverberation and then cancel it. The key point of the WPE is to estimate the power spectrum of the early speech. However, its estimation relies on an iterative procedure with high computational complexity. Another problem is that the WPE has a noise-free assumption. So, the performance degrades in noisy conditions. To address these problems, we propose an algorithm for speech dereverberation in the presence of background noise, in which deep learning is integrated into the WPE method. Specifically, we employ a neural network to predict the power spectral density (PSD) of early speech and a binary mask which distinguishes target speech from background noise. To alleviate the noise impact on estimation of echo path, a dual-filter strategy is adopted to model the echo paths of target speech and background noise individually. Experimental results show that the proposed method significantly improves speech quality in noisy environments.
Research on Prediction Algorithm of Satellite Nephogram Based on GAN

The traditional work in the field of nephogram prediction is largely based on the premise that the cloud system remains stable. These methods have poor performance in predicting cloud trends, and cannot meet the effectiveness of prediction, which greatly limits the availability of cloud prediction. There are two main challenges in nephogram prediction. Firstly, most of the observed cloud changes are non-stationary and non-linear irregular changes. Secondly, different cloud systems change in the same region are also different, so it is difficult to establish an efficient model for description. In view of the above problems, this paper uses the improved generative adversarial network to predict the nephogram, and predicts the future frames of the nephogram according to the input frame sequence. This method abandons the complex mathematical operation and the premise of stability and linearity. Using enough data and fewer constraints to the model. The experimental results on FY-4 nephogram data show that the experimental results of using this method to predict satellite nephogram are better than those predicted by traditional methods and general GAN model.
Symmetry In The Structure Of Musical Nodes

We investigate, if any symmetry that lies ubiquitously in most of the structures of nature is also present in music and how it presents itself within music. We try to quantify symmetry and patterns by constructing the statistical and visual directed graphs of pitch frequencies and their temporal alignment (time duration) in a composition. We draw these graphs for 1,409 tracks. This paper realizes the structure inbetween the pitch and time duration sequences via the underlying probability distributions and graph theory. It reduces the analysis of pitch frequencies in a given composition to 20 % out of 128 and the time durations of in the composition to only 53 out of the range of 0 to 2400 (approximately). Furthermore, we observe symmetric patterns for temporal graphs and their degree distribution indicates a self-organizing behaviour. To model pitch prediction in our brain while listening to a musical composition via Upper Confidence Bound Reinforcement Learning (UCB-RL) algorithm and analyze the learning curves per the composition of the Beethoven, Mozart, and Bach. We observe the highest smallest learning curve for Beethoven and the largest for Bach. This work may find its potential applications in music therapy, music synthesis, and cognitive science.
Application of Convexified Convolutional Neural Network in Text Classification

This paper introduces a type of convexified convolutional neural network (CCNN), introduced by Zhang, Liang and Wainwright. We applied this model on the classification of text-based online shopping reviews. This work makes an estimate on the error term brought by the low rank approximation. We also build our codes on the work done by Schaik. We make adjustments on the choices of kernel functions and further extend the application of the algorithm to multilayer CCNNs. The results show that Zhang’s model is practical on learning shallow CCNNs. However, there is no big improvement in multilayer CCNN. Analysis of likely causes are mentioned by the end of this section. Strengths, weaknesses as well as future work are discussed in the end.
Observer-Based Disturbance Rejection Voltage Control of PV-Battery Based Microgrid

An observer-based disturbance rejection control is presented for photovoltaic (PV)-battery based microgrid. The presented microgrid furnishes an uninterrupted supply to residential loads even at unavailability of the PV generation. The PV-battery microgrid comprises of a PV generator, battery storage integrated with a bidirectional converter, a voltage source converter (VSC), L-C filter and nonlinear loads. The maximum power point (MPP) tracking algorithm is utilized to enhance the utilization of the PV generator. The battery management control uses the PV energy and state of charge (SOC) to protect the battery from overcharging and deep discharging through the bidirectional converter. To control three phase VSC, an observer-based disturbance rejection algorithm is used to regulate the load voltages and frequency. The total harmonics distortion (THD) of load voltages is less than the 5% at nonlinear load as per IEEE 519 standard. A laboratory prototype is developed, and experimental results are presented to ascertain the viability of the microgrid.