Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and simulation of first-order Li-Ion battery cell with experimental validation

In this paper the first-order RC equivalent circuit model (ECM) of the Lithium Ion cell is presented. This circuit can be used in real-time applications, for example, the battery management system (BMS) because is not computational complex. The model parameters are identified with experimental measurements on a real Li-Ion cell. First the discharge capacity was measured and after the cell was completely charged and discharged applying constant current pulses to determine the open circuit voltage (VOC) and the parameters for the equivalent circuit components. For one ECM the parameters are calculated using one discharge pulse which was applied at approximatively 60% state of charge and for the second model the circuit parameters are calculated for each discharge pulse and saved in a look-up-table depending on the state of charge. For model validation was applied a charge/discharge scenario and the results show a good agreement between the battery voltage and the ECM voltage.
Line-start permanent magnet motor-single-phase steady-state performance analysis

The paper describes an efficient calculating procedure for the steady-state operation of a single-phase line-start capacitor-run permanent-magnet motor. This class of motor is beginning to be applied in hermetic refrigerator compressors as a high-efficiency alternative to either a plain induction motor or a full inverter-fed drive. The calculation relies on a combination of reference-frame transformations including symmetrical components to cope with imbalance, and dq axes to cope with saliency. Computed results are compared with test data. The agreement is generally good, especially in describing the general properties of the motor. But it is shown that certain important effects are beyond the limit of simple circuit analysis and require a more complex numerical analysis method.
Stock Volatility Prediction Based on Self-attention Networks with Social Information

Stock volatility prediction is a challenging task in time-series prediction according to the Efficient Market Hypothesis which supposes all the investors are rational. However, many theories have showed that stock markets are not efficient due to the effects of psychological and social factors. In this paper, we constructed self-attention networks (SAN) to quantify the impact on the volatility of Chinese stock market of social information, such as social opinion and social concern. Our SAN model can explore the relationships among features at different time steps more flexibly, and thus, explore stock historical information more effectively. Empirical results show the superiority of our model compared to other existing models on given stock data.
Fuzzy modeling for complex processes

A complex process which is difficult to be mathematically expressed can be described by a set of fuzzy inference rules, and fuzzy modeling has been regarded as one of the key problems in fuzzy systems research. A quick and accurate fuzzy modeling method is presented in accordance with the characteristics of SISO systems. That is, the domain of discourse of the input variable is divided firstly according to the changing degree of the process output while the input variable changes, and based on the above, dividing the total number and the premise parameters of the fuzzy rules can be determined, then because the presented fuzzy model can be expressed as a fuzzy neural network which is a feedforward neural network, so the BP algorithm is applied to obtain the consequent parameters of the fuzzy rules. The effectiveness of the presented fuzzy modeling method and the generalization ability of the fuzzy rules model are demonstrated by a simulation example.
Threshold Voltage Instability Mechanisms in p-GaN Gate AlGaN/GaN HEMTs

In this study, we propose a technique to evaluate the transient threshold voltage behavior of p-GaN capped AlGaN/GaN high-electron-mobility transistors (HEMTs). The threshold voltage is monitored from $10 \ \boldsymbol{\mu}\mathbf{s}$ to 100 s during positive gate bias stress. Technology computer-aided design (TCAD) simulations offer in-depth analysis of the different threshold voltage instability mechanisms: (i) electron trapping at the AlGaN/GaN interface, (ii) hole accumulation and trapping at the p-GaN/AlGaN interface and in the AlGaN barrier, respectively, and (iii) hole depletion of the p-GaN layer.
Application of source localization algorithms in magnetoencephalography: test on a new generation of magnetometers

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique for measuring the activity in the brain. In the vicinity of the head it measures the magnetic field produced by the electric currents in neurons. From the measured magnetic field, using various computational techniques, we determine the location of the source. In this paper, we present the main methods for localizing activated areas in the brain with the data obtained from a 128-channel superconducting quantum interference (SQUID) system. Great emphasis is on solving the inverse problem, i.e. finding the source from the measured magnetic field around the head, with the minimization of the forward model for calculating the magnetic field inside a conducting sphere. We also demonstrate each step in the image processing of the magnetic resonance imaging (MRI), which we use for a more precise source localization. We address all the major drawbacks when using the SQUID gradiometers. As an alternative, we present the new generation of magnetometers, optically pumped magnetometers (OPM), which operate at room temperature. Compared to SQUIDs they can be placed closer to the head with the use of a custom 3D printed sensor holder. We also present the first test results with a custom system of 15 OPMs.
Threshold Physical Unclonable Functions

Physical Unclonable Functions (PUFs) have been proposed as a tamper-resistant alternative to the traditional methods for secret key generation and challenge-response authentication. Although many different types of PUFs have been presented, the search for more efficient, reliable and secure PUF designs continues. In this paper, we introduce a new class of PUFs, called threshold PUFs. We show that, in principle, any n- input threshold logic gate can be used as a base for building an n-input PUF. This opens up the possibility of using a rich body of knowledge on threshold logic implementations for designing PUFs. As a proof of concept, we implement and evaluate binary and ternary PUFs based on recently proposed threshold logic flip-flops.
Autoencoder-Based Fabric Defect Detection with Cross- Patch Similarity

Fabric quality inspection plays an important role in the textile industry. As an effective approach to learn data representations, autoencoder has been adopted for defect detection. With the basic idea that the defect area cannot be recovered by the model trained on non-defective image patches, the residual is often used as an indication for defect judgement. However, usually the texture (non-defect) area in a defective patch also cannot be well reconstructed, which makes the pixel-wise detection inaccurate. In this paper, by exploring similarities between different patches in the whole test image, a novel autoencoder-based fabric defect detection method is proposed. In order to maintain the texture area in the reconstructed patch, the original encoded latent variable is modified, and the cross-patch similarity is introduced for determining the modification function. The whole algorithm is conducted in an iterative way, and the detection results will become better and better. Experimental results on the benchmark datasets demonstrate the effectiveness of our proposal.
Dual U-Net for the Segmentation of Overlapping Glioma Nuclei

The morphology and surroundings of cells have been routinely used by pathologists to diagnose the pathological subtypes of gliomas and to assess the malignancy of tumors. Thanks to the advent and development of digital pathology technology, it is possible to automatically analyze whole slides of tissue and focus on the nucleus in order to derive a quantitative assessment that can be used for grading, classification, and diagnosis. During the process of computer-assisted diagnosis, the accurate location and segmentation of nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an important step. In this paper, we proposed a U-Net-based multi-task learning network in which the boundary and region information is utilized to improve the segmentation accuracy of glioma nuclei, especially overlapping ones. To refine the segmentation, a classification model is used to predict the boundary, a regression model is used to predict the distance map, and the final segmentation is obtained by using the fusion layers. The proposed approach was compared with other specially designed boundary-aware methods by using a pathological section dataset that consists of 320 glioma cases from the Huashan Hospital at Fudan University. Both the pixel-level and object-level evaluations showed that the structural modification is effective in segmentation with an F1-score of 0.82, a Hausdorff distance (HD) of 3.95, and an aggregated Jaccard index (AJI) of 0.66 (+0.46%, −3.75%, and +4.09% compared with the unimproved methods, respectively). In addition, comparative experiments on multi-organ nuclei segmentation (MoNuSeg) open dataset proved the advanced nature of the proposed method in the field of nuclei segmentation, especially separating touching objects. The proposed method obtains an AJI of 0.59 and an F1-score of 0.79.
A Low-cost Neuroadaptive Control Approach for Unmanned Aerial Vehicle under Time-Varying Asymmetric Motion Constraints

This paper presents a neuroadaptive tracking control scheme for uncertain Unmanned Aerial Vehicle (UAV) subject to asymmetric yet time-varying (ATV) full-state constraints without involving feasibility conditions. By blending a nonlinear state-dependent transformation into each step of backstepping design, a neural network-based adaptive control scheme is developed, which, as compared with most existing methods, exhibits several attractive features: 1) it is robust and adaptive to parametric/non-parametric uncertainties; 2) it not only directly accommodates ATV motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers; and 3) it only involves one lumped-parameter adaptation, thus is structurally simpler, computationally less expensive, and easier in implementation. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness of the proposed control strategy for UAV is confirmed by systematic stability analysis and numerical simulation.
On-line event detection by recursive Dynamic Principal Component Analysis and gas sensor arrays under drift conditions

Leakage detection is an important issue in many chemical sensing applications. Leakage detection by thresholds suffers from important drawbacks when sensors have serious drifts or they are affected by cross-sensitivities. Here we present an adaptive method based in a Dynamic Principal Component Analysis that models the relationships between the sensors in the array. In normal conditions a certain variance distribution characterizes sensor signals. However, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drifts the model is adaptive and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic signals and with real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method.
A Multi-User-Hybrid Beamforming Scheme for Dual-Band Wireless Networks

To highly increase the system capacity to cater to the ever-increasing mobile service requirements, millimeter wave (mmWave) bands with massive available bandwidth have been envisioned as promising candidates in spectrum extension by the fifth generation (5G) wireless communication networks. Nevertheless, compared with conventional microwave cellular bands, mmWave bands suffer from severer propagation loss and are hard to be directly applied into practical wireless communication systems. With an ability not only to overcome the unfavorable path loss of millimeter wave (mmWave) bands but also to provide spatial multiplexing at economical hardware costs, the hybrid analog and digital beamforming (HBF) technology attracts growing attention recently. Based on the feature of HBF, we propose a multi-user HBF (MU-HBF) precoding scheme for user closely co-located scenarios where users are too close to each other that they cannot be distinguished only by physically-separated directional beams. Considering that the limited coverage scope of mmWave directional beams cannot guarantee the network robustness, the control/user (C/U) plane decoupled network architecture is applied to integrate microwave bands with mmWave bands and form a dual-band wireless network, in which microwave bands provide omnidirectional coverage for mobility-related control plane signaling and mmWave bands provide high-speed transmission rate for user plane data. Analysis and simulations are provided to demonstrate the superior performance of the proposed MU-HBF precoding scheme under user closely co-located scenarios.
Optimization of BLE Beacon Density for RSSI-Based Indoor Localization

Location Based Services (LBS) and Proximity Based Services (PBS) can play an important role in our daily life by simplifying tasks. Functions such as turning on and off lights can occur automatically or locking and unlocking doors can be done using LBS. By knowing the location of a user, appliances can be automated to function once the user is near them. Through the use of indoor localization, a user's position can be calculated. When designing an indoor localization system the density of transmitters plays an important role in maximizing the accuracy obtained. Increasing the number of references can improve the accuracy by providing additional information that the system can use in calculating a location. However, placing too many transmitters in the area can create interference in signals and negatively impact the localization results, while not having enough transmitters will hinder localization as not enough information is available. In this paper, we examine the optimal number of Bluetooth Low Energy (BLE) beacons to be used for indoor localization to optimize localization accuracy. Two algorithms were compared: trilateration and nonlinear least squares applying two types of filtering: moving average, and Kalman. Nine different types of systems were developed and compared in terms of accuracy and precision. According to experimental results placing six beacons in an environment will produce the optimal results. Using a nonlinear least squares algorithm with the three closest references with a moving average filter produced the lowest error of 1.149 meters with a standard deviation of 0.698 meters.
Temperature-Dependent Gate Degradation of $p$-GaN Gate HEMTs under Static and Dynamic Positive Gate Stress

This paper experimentally investigates the time-dependent gate degradation of Schottky-type $\boldsymbol{p}$-GaN gate transistors subjected to positive gate voltage stress. By means of combined static/dynamic gate stress and temperature-dependent analysis, the dependence of time-to-breakdown ($\boldsymbol{t}_{\mathbf{BD}}$) on stress mode and temperature are unveiled. It is demonstrated that $\boldsymbol{t}_{\mathbf{BD}}$ is Weibull distributed and the mean-time-to-failure (MTTF) is comparable under static and dynamic stress conditions. Both the gate breakdown voltage and MTTF exhibit positive temperature dependence. The maximum applicable gate voltage for a 10-year lifetime is extrapolated at different stress conditions. Moreover, the mechanism of the gate degradation is discussed by comparing the devices' performance before and after the progressive breakdown. It is revealed that electrons accelerated in the depletion region of the $\boldsymbol{p}$-GaN layer under large forward gate bias would gain enough energy and induce defects near the metal/ $\boldsymbol{p}$-GaN interface, resulting in the time-dependent gate degradation.
A Multilayer Preventive Control to Regulate Voltage and Frequency in Autonomous Microgrids

This paper proposes a method to prevent the frequency and voltage excursion in autonomous microgrids beyond their predefined desired ranges, following any change in load and/or renewable energy output. The proposed technique utilizes the short-term prediction data of load and renewable generations to determine any prospective occurrence of events that violate the microgrid’s predefined range of frequency or voltage. It then determines the optimal generation for dispatchable sources and the MG’s most appropriate topology as first options to prevent the violation. If it is not enough, the proposed technique goes on to engage in supporting actions, such as exchanging power with neighboring microgrid, utilizing energy storages, demand response and renewable energy curtailment. It considers the technical, reliability and environmental aspects of the MG along with the operational cost, as well as the cost of supporting actions. The optimized control variables are sent to the local controller before the occurrence of the event to maintain the frequency and voltage within the desired limits.
OLLDA: Dynamic and Scalable Topic Modelling for Twitter : AN ONLINE SUPERVISED LATENT DIRICHLET ALLOCATION ALGORITHM

Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new documents arriving in a stream, and, at the same time, achieving high quality of topics’ detection in special case environments, such as Twitter.The proposed algorithm is a combination of an online algorithm for LDA and a supervised variant of LDA - labeled LDA. The performance and quality of the proposed algorithm is compared with these two algorithms. The results demonstrate that the proposed algorithm has shown better performance and quality when compared to the supervised variant of LDA, and it achieved better results in terms of quality in comparison to the online algorithm. These improvements make our algorithm an attractive option when applied to dynamic environments, like Twitter. An environment for analyzing and labelling data is designed to prepare the dataset before executing the experiments. Possible application areas for the proposed algorithm are tweets recommendation and trends detection.
Software business models and contexts for software innovation: key areas software business research

This paper examines business, design, and product development aspects of software business models. Contexts of small and large companies for creating software innovations are also analysed. Finally, software business research is called for and an agenda for software business research is presented to better understand the dynamics of the software industry and help create and manage successful software-intensive ventures.
Fault Diagnosis of Rolling Bearings Based on Undirected Weighted Graph

One of the main functions of rolling bearing condition monitoring is to diagnosis the type of fault that is occurred during its continuous operations. This paper presents a new method for rolling bearing fault diagnosis based on the graph model. Concretely, through Fourier transform, the periodogram is computed from the condition monitoring (CM) signal and then modeled into an undirected weighted graph. This graph is subsequently fed to K-Nearest Neighbor (KNN) Classifier for fault type diagnosis. In particular, to perform KNN upon graph model, a robust graph distance metric so-called sum of the difference in edge-weight values (SDEWV) is adopted via investigating four candidate metrics existed in the literature. Based on experimental results in the publicly-available database, we demonstrate exciting results of the proposed method in bearing fault diagnosis, indicating its great potentials in real engineering applications.
Efficient low-power designs using MOSFETs in the weak inversion region

The availability today of process technologies such as submicron CMOS and BiCMOS allow circuit designers to push the limits of circuit performance. However, this is only possible when designers are fully equipped with good understanding of such device operation. In very low power applications, the use of MOSFETs operating in the weak inversion region is very attractive. Nevertheless, it is not easy to manipulate the devices since models such as the BSIM empirical model are complicated and involve too many parameters. As a result, hand calculation for an initial design is very difficult and can be a major obstacle for circuit designers. This paper presents an intuitive way of manipulating the weakly inverted MOSFET. An effective procedure for initial hand calculation is given, based upon simulation models and parameters. Comparisons between the calculation results and simulation results are performed to confirm the concept.
Towards an EmoCog Model for Multimodal Empathy Prediction

This paper describes a newly proposed empathy prediction model, the EmoCog model, as our solution for the One-Minute Gradual (OMG) Empathy Challenge. The objective for the challenge was to estimate the valence (positivity/negativity) of a listener in a story-telling conversation. We implemented the EmoCog model with two approaches - one with support vector machines (SVMs) and one with neural networks (NNs). We extracted a total of six features corresponding to three categories: 1) cognitive empathy, 2) emotional empathy, 3) synchrony and used them as input to our models. On the validation set, we achieved 0.19 Concordance Correlation Coefficient (CCC) for the SVM approach and 0.25 for the NN approach. On the test set, we achieved results better than baseline, with CCC scores of 0.08 and 0.07, respectively.