Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Invariant Representations From EEG via Adversarial Inference

Discovering and exploiting shared, invariant neural activity in electroencephalogram (EEG) based classification tasks is of significant interest for generalizability of decoding models across subjects or EEG recording sessions. While deep neural networks are recently emerging as generic EEG feature extractors, this transfer learning aspect usually relies on the prior assumption that deep networks naturally behave as subject- (or session-) invariant EEG feature extractors. We propose a further step towards invariance of EEG deep learning frameworks in a systemic way during model training. We introduce an adversarial inference approach to learn representations that are invariant to inter-subject variabilities within a discriminative setting. We perform experimental studies using a publicly available motor imagery EEG dataset, and state-of-the-art convolutional neural network based EEG decoding models within the proposed adversarial learning framework. We present our results in cross-subject model transfer scenarios, demonstrate neurophysiological interpretations of the learned networks, and discuss potential insights offered by adversarial inference to the growing field of deep learning for EEG.
An Islanded Microgrid with Novel Fault Tolerant Inverter

A novel fault-tolerant inverter applied in this islanded microgrid is proposed in this paper. Firstly, the topology structure and the working principle of the microgrid with the fault-tolerant inverter are provided. The fault-tolerant inverter allows the switching devices of different bridge arms to be reconstructed, which improves the fault-tolerant space. A fault-tolerant pulse signal algorithm is proposed, combined with the droop three-loop control to form a control method for the fault-tolerant inverter. It is no need to switch the control algorithms between the normal operating mode and the fault-tolerant mode. The reliability of the system is improved. The circulating currents eliminating method between the two inverters when the fault-tolerant inverter is in fault state are analyzed. Finally, the validity of the topology and the control strategy of the system is verified by simulation.
iDocChip - A Configurable Hardware Architecture for Historical Document Image Processing: Text Line Extraction

Digitizing historical archives poses a great challenge due to the quality degradation existing in these documents. Hence, even well-established Optical Character Recognition (OCR) systems, such as Abby, OCRopus, Tesseract, etc., fail to give sufficient recognition accuracy for historical archives, since they are optimized for transcribing contemporary documents. In contrast, the open-source anyOCR system is designed specifically for digitizing historical documents with state-of-the-art image processing techniques, to achieve high accuracy. Nowadays, the retrieval of historical document images for further OCR requires special scanning devices that are bulky and stationary. As a result, a portable device that combines scanning and OCR capabilities is beneficial to transcribe documents without the need to remove them from where they are archived. For example, smart goggles equipped with embedded OCR device can be used for instant word spotting. However, the available anyOCR software implementation has long runtime and high power consumption. As a solution, we propose a low power, energy-efficient accelerator with real-time capabilities called iDocChip, which is a hybrid hardware-software programmable System-on-Chip (SoC) for digitizing historical documents. This chip can be easily integrated in a portable device. This paper focuses on one of the most crucial processing steps in anyOCR: Text line extraction. We propose, to the best of our knowledge, the first hybrid hardware-software architecture of the text line extraction technique implemented on an FPGA based programmable SoC. The resulting custom hardware accelerator outperforms the existing anyOCR software implementation by 120x, while achieving 1700x higher energy efficiency without affecting the high accuracy of the system.
One-Dimensional MUSIC-Type Algorithm for Spherical Microphone Arrays

Spherical microphone arrays have been widely used in direction-of-arrival (DOA) estimation in recent years. The conventional multiple signal classification method formulated in spherical harmonic domain (SHMUSIC) is computationally expensive, particularly in the application of joint estimation of the azimuth and elevation. In this paper, one-dimensional MUSIC-type approach (ODMUSIC) is proposed to estimate the elevation and azimuth. We construct a mapping matrix between the spherical harmonic function and Fourier series, then utilize the Vandermonde structure of Fourier series to develop the polynomial rooting method. The mapping matrix only depends on the array configuration and can be calculated prior to DOA estimation. The two-dimensional angle search can be transformed into two one-dimensional angle findings based on the mapping matrix. By using the characteristic of Fourier series, we can construct two root polynomials to estimate the azimuth and elevation, respectively. The proposed method can avoid the two-dimensional spectral search and the angles are automatically paired. Computer simulations validate the effectiveness of the proposed method.
Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

Aiming at improving the tracking performance of the delta-generalized labeled multi-Bernoulli (<inline-formula> <tex-math notation="LaTeX">$\delta {-}\textrm {GLMB}$ </tex-math></inline-formula>) filter, we present a one time step lagged <inline-formula> <tex-math notation="LaTeX">$\delta {-}\textrm {GLMB}$ </tex-math></inline-formula> smoother in this work, which also inherently outputs targets trajectories and differs from the Probability hypothesis density (PHD), Multi-Bernoulli (MB), and Cardinalized probability hypothesis density (CPHD) smoothers that are incapable of generating target trajectories directly. Under the standard multitarget measurement likelihood and state transition kernel, we show that a <inline-formula> <tex-math notation="LaTeX">$\delta {-}\textrm {GLMB}$ </tex-math></inline-formula> distributed multitarget filtering density would result in a same distributed one time step lagged multitarget smoothing density. An efficient implementation of the proposed smoothing algorithm using the standard ranked assignment technique is also given. Numerical results show that the proposed smoother is capable of tracking a time-varying number of targets, in the presence of measurement origin uncertainty, target detection uncertainty, and clutter, and show that the proposed smoother outperforms the <inline-formula> <tex-math notation="LaTeX">$\delta {-}\textrm {GLMB}$ </tex-math></inline-formula> filter, and the PHD, MB, and CPHD smoothers of the same time lag on both the estimates of target number and state and it also outperforms the LMB and the approximated <inline-formula> <tex-math notation="LaTeX">$\delta {-}\textrm {GLMB}$ </tex-math></inline-formula> smoothers of the same time lag on target number estimate.
iSEC: An Optimized Deep Learning Model for Image Classification on Edge Computing

Optimization strategies in deep learning models require different techniques for different use cases. Besides, various phases of the model deployment life-cycle specify possible and particular optimization strategies. In this paper, an optimized deep learning model on the edge computing environment is proposed for image classification cases. For preparing the dataset, the image preprocessing and data augmentation methods are utilized to prepare the data for the training process. To accelerate the deep learning training process, this system implemented CPU optimization and hyperparameter tuning. Tensorflow is applied as a framework for the training model. InceptionV3, VGG16, and MobileNet are applied as topology implemented in the deep learning training comparison. In this case, InceptionV3 was used for modeling the deep learning applications on edge. To optimize the trained model, a Model Optimizer is used on the edge device. It can be seen in the experiments, MobileNet was the least accurate model (85%) and the longest time to load the model (71s). VGG16 was the most reliable (91%) and the shortest time to load the model (50s). InceptionV3 has median accuracy (87%) and the average time to load the model (52s).
A Compact Miniaturized UWB-MIMO Antenna With WLAN Band-Notched Function

This paper presents a compact miniaturized ultra-wideband(UWB) antenna with band-notched Function, which is experimentally researched for multiple-input-multiple-output (MIMO). Two selfsame monopoles are arranged symmetrically, which constitute the UWB-MIMO antenna. The monopole of the antenna is modified from a micro-strip off-center-fed patch. The modified ground plane which is excavated three open rectangular slots and the antenna monopole become the antenna radiator which achieves the desired ultra-wideband. The center part of the ground plane can achieve high isolation phenomena by propagating through the restraining surface current. Moreover, the counter monopoles enhance isolation and enable more compact antenna which has dimensions of $28\times 31\text{mm}^{2}$. Two G-shaped slots are etched on the monopoles, which achieves the band-notched function in WLAN (5.15-5.85GHz) band. Both simulated and measured return loss $(\mathrm{S}11 < -10\text{dB})$ of the antenna have better isolation than −15 dB from 3-12GHz. The performance of the antenna is investigated on isolation of the two ports, envelope correlation Coefficient (ECC), realized gain, radiation pattern.
Routing Protocols in MANET: An Overview

Mobile Ad-hoc Networks also known as MANETs belong to a collaborative collection of different mobile devices from a heterogeneous network, the prolific feature is the capability to join and quit the network any time. This leads to a creation of a decentralized network of mobile nodes which can connect, communicate and pass messages to each other without the requirement of an intermediate router. MANETs have the provision to offer much quicker deployment as compared to a normal networks, the deployment being horizontal type is only possible in this scenario. MANETs are generally used in diverse set of applications like Military operations, emergency or disaster relief operations, business meetings, political meetings, Mine cite operations. In these kinds of networks the most important issue is that of finding the best route available between the two nodes according to the user requirements. Undoubtedly routing protocols and their evaluation is the most discussed topics in the MANETs since the evolution of this network. A number of enhancements have been done to these basic protocols being discussed here by different researchers from time to time according to their requirements. But this paper tends to throw some light on the basic protocols which will provide the readers the basic information about the characteristics, advantages and disadvantages of the particular protocol and can help them to use any protocol according to their use. An intention is to discuss regarding the proactive, reactive, hybrid, location-based, power aware, uni-cast, multicast; covering apparently major number og routing protocols been proposed so far. This paper tends to cover the routing protocol covering the timeline from last two decades. In the conclusion we will give the table showing the comparison of various discussed routing protocols. We hope that this paper will help the researchers in the area of Adhoc networks.
Analysis of vibration characteristics for cam absolute gravimeter via improved MEEMD-HHT method

An accurate determination of gravitational acceleration has been playing an important role in the areas of metrology, geophysics, and geodetics. Vibration interference is the main factor affecting the accuracy of absolute gravity measurement based on optical interferometry. A novel research on vibration characteristics for cam absolute gravimeter was proposed with the improved MEEMD-HHT method. After the adaptive decomposition of the measured signal, the vibration data was analyzed and studied with the time-frequency spectrum and energy spectrum. Experimental results show that this method can restrain mode mixing effectively, and accurately obtain the energy distribution of each frequency band of vibration. This research can improve the accuracy of absolute gravity measurement and lay the foundation for the design of high-precision vibration absorber.
Inquiry Based Learning with Kanban Game

In this paper, we present the design and the effectiveness of a computer game that supports inquiry-based learning of Agile development with Kanban. Seventy four undergraduate students who were enrolled in classes related to software engineering participated in the study. By engaging students in multiple rounds of questioning and peer discussion, the students were actively involved in the conceptualization, investigation and generation of new knowledge. From survey results, observation of focus groups as well as the game log, we found that students were highly engaged in the learning process. There was also an improvement of students' knowledge in agile software development.
Shared-Memory Parallel Maximal Biclique Enumeration

We present shared memory parallel algorithms for maximal biclique enumeration (MBE), the task of enumerating all complete dense subgraphs (maximal bicliques) from a bipartite graph, which is widely used in the analysis of social, biological, and transactional networks. Since MBE is computationally expensive, it is necessary to use parallel computing to scale to large graphs. Our parallel algorithm ParMBE efficiently uses the power of multiple cores that share memory. From a theoretical view, ParMBE is work-efficient with respect to a state-of-the-art sequential algorithm. Our experimental evaluation shows that ParMBE scales well up to 64 cores, and is significantly faster than current parallel algorithms. Since ParMBE was yielding a super-linear speedup compared to the sequential algorithm on which it was based (MineLMBC), we develop an improved sequential algorithm FMBE, through "sequentializing" ParMBE
A Reduced Order Observer Design for Sensorless DFIG

This study presents a Reduced-order Extended Kalman Filter (EKF) implementation for Doubly Fed Induction Generator (DFIG) that is controlled by Matrix Converter (MC). MC has many advantages such as no need for high value filters, capability of bi-directional energy conversion and no need for DC bus. However, due to the complexity of control algorithms of this topology, various control methods such as Model Predictive Control (MPC) have been developed. Although it is easy to implement, weakness of this control method is that it uses a mathematical model and affected by parameter changes due to the disturbances. In this study, Reduced-order EKF has been applied instead of full-order EKF to reduce the execution time of the algorithm and to ensure that the resistance values of DFIG are more robust to variations due to external disturbances. Moreover, the sensorless control approach has been applied with the Reduced-order EKF to robust speed estimation.
Trajectory Optimization for Reusable Rocket Landing

Rocket landing process starts from the time of stage separation to the end of the vertical landing to the target area. This paper presents a homotopy-based trajectory optimization algorithm for reusable rocket landing problem. By analyzing the rocket characteristics of different phases in the landing flight process, the rocket landing optimization problem is established with the fuel-optimal index, and the dynamics and relevant constraints at various phases. Then, a unique homotopy optimization framework based on multiple flight phases is proposed for solving this problem. Simulation results showed that the proposed algorithm in this paper can effectively handle complex landing constraints, and also provides a feasible method to analyze the whole process of reusable rocket landing.
Reliability Evaluation of Distribution Network Containing PV-assisted Charging Station for EVs

In this paper, the optimal operation of PV-assisted charging station and the fault probability of distribution network are considered, and Sequential Monte Carlo algorithm is used to evaluate system’s reliability. After considering the charging cost, discharge times of energy storage system and other objective conditions, PV-assisted charging station’s optimal operation model is proposed. Then, on the basis of PV-assisted charging station’s optimal operation model, reliability assessment of distribution network containing vehicle-photovoltaic charging station is realized using Monte Carlo, and fault response of electric vehicle charging station, optimization strategies and the situation of charging station as a power source to supply power to the island of fault area are also considered. The influence of PV-assisted charging station on distribution network’s reliability is also analyzed.
Asynchrony Estimation of Solar Irradiance by Quantification of Joint Recurrence Plot

Photovoltaic (PV) power generation is regarded as the one of the essential resources for the achievement of the world de-carbonization; The world installation of the PV power generation is rapidly growing. However, the mass installation of PV power plants causes the imbalance of the electricity power supply and demand on the power transmission system. This is due to the uncontrollableness of the solar irradiance fluctuation. Besides its timewise fluctuation, the spatial variance of the solar irradiance affects the electric power quality on the power system with a lot of decentralized PV energy resources. To realize the more PV installation by the energy management system (EMS), the evaluation on the spatiotemporal change of the solar irradiance plays the important roles: The asynchrony assessment for the solar irradiance is expected in the evaluation on the smoothing effect of the PV power generation. To evaluate the relation between two different time series, some of the measurement from cross recurrence plot has been used. In particular, cross-sample entropy is measured as the indicator of the degree of asynchronous. The authors introduce the cross sample entropy approach to the quantification of the joint recurrence plot for measuring the degree of the spatial asynchronous for the solar irradiance, and visualize them.
Detection of UXO-like Targets with Portable Transient Electromagnetic System

With the advantages of strong anti-interference ability and high spatial resolution, electromagnetic induction (EMI) has proven to be successful in unexploded ordnance (UXO detection. Portable transient electromagnetic system can work in a variety of complex conditions where the large and heavy vehicular systems cannot be readily used. The detection of UXO-like targets with portable transient electromagnetic (TEM) system is discussed detailed in this paper. Firstly, with description of the single dipole model for UXO response approximation, the location and characteristic response extraction for UXO-like targets is talked with the DE algorithm. Then, the improved sensor of the portable TEM system with a single-layered transmitting coil and five 3-component receiving coils is introduced. The transmitting coil is constructed with a diameter, number of turns, and transmitting current of 0.5 m, 30, and 5 A, respectively. The receiving coil is constructed with a length, number of turns, and resonant frequency of 6 cm, 600, and 180 kHz, respectively. Finally, 12 UXOs and 6 harmless targets buried in the area of 10 m × 6 m at different depths in different orientations have been detected with the improved portable TEM system. Target location and characteristic response have been estimated by DE algorithm with the measured response. The experimental results show that the high-quality data provided by the improved portable TEM system combined with the DE algorithm based on the dipole model can locate and characterize the separated targets accurately. Most of the position errors are within 10cm in x position and 5cm in depths for separated buried targets. For the multiple-targets buried together, the system can still locate and characterize the targets even if the response of two targets overlaps.
Mobile Data Gathering Approaches in Wireless Sensor Networks: A Survey

Data gathering is one of the most significant fundamental operations in a wireless sensor network. But it depletes large amount of energy of sensor nodes that reduce their network lifetime. Recently, various methods are introduced for energy optimization and to improve the operation time of entire network using mobile elements or mobile collector. This paper explores those data gathering techniques that not only enhance the network lifetime but also improve the performance of sensors. Major issues and challenges in mobile data gathering are discussed as well as various mobile data gathering approaches are compared on the basis of their algorithms, features merit and demerits. This paper will be useful for new researchers to get overall exposure to mobile data gathering techniques and research challenges on efficient data gathering in WSN
Manifold Regularized Stochastic Block Model

Stochastic block models (SBMs) play essential roles in network analysis, especially in those related to unsupervised learning (clustering). Many SBM-based approaches have been proposed to uncover network clusters, by means of maximizing the block-wise posterior probability that generates edges bridging vertices. However, none of them is capable of inferring the cluster preference for each vertex through simultaneously modeling block-wise edge structure, vertex features, and similarities between pairwise vertices. To fill this void, we propose a novel SBM dubbed manifold regularized stochastic model (MrSBM) to perform the task of unsupervised learning in network data in this paper. Besides modeling edges that are within or connecting blocks, MrSBM also considers modeling vertex features utilizing the probabilities of vertex-cluster preference and feature-cluster contribution. In addition, MrSBM attempts to generate manifold similarity of pairwise vertices utilizing the inferred vertex-cluster preference. As a result, the inference of cluster preference may well capture the comparability in the manifold. We design a novel process for network data generation, based on which, we specify the model structure and formulate the network clustering problem using a novel likelihood function. To guarantee MrSBM learns the optimal cluster preference for each vertex, we derive an effective Expectation-Maximization based algorithm for model fitting. MrSBM has been tested on five sets of real-world network data and has been compared with both classical and state-of-the-art approaches to network clustering. The competitive experimental results validate the effectiveness of MrSBM.
Thick Clouds Removal From Multitemporal ZY-3 Satellite Images Using Deep Learning

The presence of clouds greatly reduces the ground information of high-resolution satellite data. In order to improve the utilization of high-resolution satellite data, this article presents a cloud removal method based on deep learning. This is the first end-to-end architecture that has great potential to detect and remove clouds from high-resolution satellite data. For cloud detection, a convolution neural network (CNN) architecture is used to detect them. For cloud removal, the content generation network, the texture generation network, and the spectrum generation network based on traditional CNN are proposed. The proposed CNN architecture can use multisource data (content, texture, and spectral) as an input of the unified framework. The results of both the simulated and real image experiments demonstrate that the proposed method is robust and can effectively remove thick clouds, thin clouds, and cloud shadows. In addition, compared with some existing methods, the proposed method can recover land cover information accurately.