Miracle-dz/UniFAR-FTUs huggingface dataset:
Signal to Symbol Transformation Techniques for Robust Diagnosis in TRANSCEND

TRANSCEND uses qualitative transient analysis methods for monitoring and diagnosis of dynamic systems to overcome complexity and convergence issues that arise in numerical processing, especially for nonlinear systems. Generating qualitative features from real signals, the signal to symbol transformation problem, is a challenging task. This paper discusses model-driven methods for generating symbolic feature descriptions of magnitude and slope changes in noisy continuous data. Experiments have been successfully conducted on faults introduced into the cooling system of an automobile engine.
On the Efficiency of Miniaturized 360° Beam-Scanning Antenna

The efficiency and beam-scanning performance of a compact, beam reconfigurable antenna are presented. The proposed design provides a 360° reconfigurable bidirectional radiation pattern in the horizontal plane by using phase variations between its 3 ports. Antenna miniaturization is achieved by dielectric loading. The miniaturized designs are presented for 4 different permittivity values so that the resonant frequency is shifted from 2.46 GHz to 2.16 GHz, 1.95 GHz and 1.8 GHz. The simulated results show good linear beam-scanning despite a decrease in antenna efficiency.
Plant Disease Diagnosis with Color Normalization

A plant disease diagnosis method based on color histogram invariant features, is evaluated on pear diseases. The employed fuzzy-like classification method is tested with five different normalization methods applied on Red-Green-Blue (RGB), Hue-Saturation-Lightness (HSL), Hue-Saturation-Value (HSV) and $\mathbf{L}^{\ast}\mathbf{a}^{\ast}\mathbf{b}$ format. Three metrics (sensitivity, specificity, accuracy) are used to assess the success of each normalization method. The experimental results show that the success in the disease recognition can be improved by up to 4% if appropriate color normalization is employed.
Integrating Behavior Modeling with Data Mining to Improve Human Error Prediction in Numerical Data Entry

Human errors in numerical data entry can lead to serious consequence but it is difficult to predict those errors because mechanisms of human errors vary and no contextual clues are available. This study suggests integrating human behavior modeling and data mining as an advanced method to predict human errors. Human behavior modeling utilized top-down inference to transform interactions between task characteristics and conditions into general inclination of an average operator to make errors, while data mining parsed psychophysiological measurements into individual’s likeliness of making errors on a trial-by-trial basis through bottom-up analysis. Specifically, an enhanced Queuing Network-Model Human Processor (QN-MHP) generated modeling features to be combined with real-time EEG features that were collected in a realistic numerical typing experiment, and potential errors were predicted by detecting error-associated features by linear discriminant analysis (LDA) classifiers before responses. The detection could be made as early as 300 milliseconds beforehand, and the results showed that integration improved the LDA classifiers’ performance by 31.7% in keenness (d') and by 12.5 % in area under ROC curve (AUC) from that of using EEG only. The integration may help implement future adaptive augmented system to prevent cognitive breakdown by determining appropriate automation/augmentation levels.
The MIPI C-PHY Standard: A Generalized Multiconductor Signaling Scheme

Synchronous digital communication buses have traditionally relied on two fundamental principles related to signal modulation and maximum likelihood sampling. In simple terms, transmitting information digitally starts with representing the information using data bits and then encoding these bits into some physical quantity, such as a voltage potential or an electronic current flow. Such a physical quantity is transported over a physical medium, such as a metallic conductor or even free space [1]. Similarly, when it comes to receiving data bits, a receiver is expected to quantize the sensed physical quantity and minimize the probability of quantization error in the presence of noise, fading, and interference [2].
Design of Human-Skin Equivalent Phantom for Evaluating the Surface Temperature Elevation due to Millimeter Wave Exposure

Use of Millimeter wave (MMW) frequencies in 5G and WiGig wireless devices will increase the public concerns on human safety for electromagnetic fields exposure. Major health effects due to MMW exposure are the local temperature elevation on the skin surface. To evaluate the heating effects caused by wireless devices employing MMW frequencies, it is essential to develop a human-skin equivalent phantom to represent the required surface temperature elevation on a multi-layer skin model.
Interconnect estimation for segmented FPGA architectures

Most modern FPGAs have a segmented architecture, with routing tracks of varying lengths. No interconnect estimation method is available for such architectures. We propose a new method that produces reliable estimates for segmented FPGA architectures on both global and local levels. We compare our estimates with the detailed routing results produced by standard routing tools in the VPR design suite.
Quadrilateral-Shaped Wideband Circularly Polarized CPW-Fed Monopole Antenna

This paper presents a new type coplanar waveguide (CPW)-fed quadrilateral-shaped compact planar monopole antenna with wideband circular polarization (CP). The antenna operates in the C-band (5.96–8.0GHz), as well as in the X-band (8.0–11.72GHz) and provides circular polarization (CP) in both bands. The fractional axial ratio (AR) bandwidth for CP 55.86% (5.96–10.58GHz) and fractional bandwidth of 118.2% (4.5–17.5GHz) for the return loss (S11) is realized simply by designing a quadrilateral-shape monopole with an asymmetric ground plane. The proposed CPW monopole antenna structure is simple and compact, its design is efficient, and it has a wide impedance matching (S11) band as well as 3-dB axial ratio (AR) operating band. Simulated and experimental results are presented to validate the antenna design and are shown to be in good agreement.
Machine Learning to Predict Annual Stock Market Index - a Genetic Programming Approach

The objective of this research was to generate an indicator of global political stability, by predicting the annual S&P 500 stock market index. This was done through machine learning, using a genetic programming approach, creating an algorithm with a template that takes into account the previous years' data of S&P 500 stock index, gold prices, the number of casualties in U.S. wars, crude oil prices, Dow Jones Industrial Average and rates of inflation in U.S. The prediction of this algorithm was highly accurate, within 14%.
TechTIDE: Warning and Mitigation Technologies for Travelling Ionospheric Disturbances Effects

Travelling Ionospheric Disturbances (TIDs) are ionospheric manifestations of internal atmospheric gravity waves (AGW) in the neutral atmosphere driven by near-Earth space dynamics and by lower atmosphere phenomena. They constitute a threat for operational systems such as precise navigation (e.g., EGNOS and NRTK) and high frequency geolocation as they can impose disturbances with amplitudes of up to ∼20% of the ambient electron density, and Doppler frequency shifts of the order of 0.5 Hz on HF signals. The Horizon 2020 Project TechTIDE (http://techtide.space.noa.gr/) funded by the European Commission aims at designing and testing new viable TID impact mitigation strategies for the technologies affected by developing a system able to calculate in real-time the main TID characteristics (velocity, amplitude, propagation drection), to realistically specify background ionospheric conditions and to specify those ionospheric characteristics whose perturbation, because of TIDs, cause the impact in each specific technology. The TechTIDE system will contribute new understanding of the physical processes resulting in the formation of TIDs, and will consequently help to identify the drivers in the interplanetary medium, the magnetosphere and the atmosphere. This paper will provide a description of the instrumentation involved and outline the project methodologies for the identification and tracking of TIDs based on the exploitation of real-time observations from networks of Digisonde, GNSS receivers and Continuous Doppler Sounding Systems.
Remote Sensing Image Target Recognition Based on Pruned Deep Neural Network Models

In this work, pruned deep neural network models have been used for remote sensing image target recognition to reduce the storage resources. This model compression procedure including two parts: first, pre-train the model for remote sensing image target recognition. Second, compression models by network pruning and weight clustering. The pruning procedure was implemented by learning the important connections in the network, and the weight clustering procedure was implemented by k-means algorithm. Experiments were performed with the AlexNet and VGG-16 networks on the NWPU-RESISC-10 datasets. Results show that the proposed method reduced the storage of AlexNet by 11.4×, from 240MB to 20MB and reduced the VGG-16 by 11× from 552MB to 50MB, with no significant loss of accuracy.
Classification of EEG Signals Using Alpha and Beta Frequency Power During Voluntary Hand Movement

Pattern recognition using non-invasive techniques like electroencephalography (EEG) is valuable to infer and evaluate the neural interaction. In this study, EEG have been compared during the presence and absence of voluntary hand movement. Components of the alpha and beta frequency bands like the sensorimotor rhythm originated from the primary motor cortex and related brain areas reflect human movement. The power of 8–13 Hz alpha and 14–30 Hz beta frequency bands were used for the classification. To classify the data, k-NN algorithms (kNN), support vector machines (SVM), logistic regression (LR), decision tree classifiers (DT), linear discriminant analysis (LDA) and Gaussian naive bayes (NB) machine learning algorithms have been used. The best classification accuracy was achieved using decision tree algorithms which had an accuracy average f-score of 0.88 among four participants. In conclusion, decision tree classifiers ought to make alpha/beta frequency band based feature extraction for recognition of human movement.
A Parameter Optimization Design Method Based on LCC Resonant Converter

For a fixed frequency LCC resonant converter have some problems of the full switching state of the lag arm is easily lost and the resonant current is large at heavy loads, resulting in only a narrow ZVS range and large conduction losses. Firstly, perform time domain analysis on the converter. Secondly, parameter design of LCC resonant converter based on fundamental analysis method using power analysis and time domain analysis. The parameter design method broadens the ZVS conduction range of the converter, and effectively reduces the resonant current and reduces the circulating current loss. Finally, a simulation platform is built in the Matlab/Simulink simulation environment. The simulation results verify the correctness of the theory.
A Digital Twin for Cyber-Physical Energy Systems

The pervasiveness of digitalization brings new opportunities, especially for monitoring and control in the energy domain. Access to sensor and context data allows a modern energy system to manifest as a Cyber-Physical Energy System (CPES). Specifically, in this paper we demonstrate the applicability of a Digital Twin for a CPES. We apply our vision of the Digital Twin paradigm to detect and analyze anomalies in a flexible energy deployment. Our results show that, with enough expert knowledge and insight, the Digital Twin can explore system behavior and is a candidate for system calibration and control parameter estimation.
Spin-Orbit Torque MRAM for ultrafast embedded memories: from fundamentals to large scale technology integration

Spin-orbit torque (SOT) magnetic random access memory (MRAM) is an emerging non-volatile memory that offers efficient and reliable sub-ns switching. It is considered as a promising candidate to tackle the replacement of SRAM memories in low level caches (L1-3). In this paper, we review the fundamentals of spin-orbit torque and we describe full-scale integration of perpendicular magnetized SOT-MRAM devices on 300mm wafers, using manufacturable methods and CMOS compatible processes. We demonstrate state-of-the-art properties of W-based top-pinned magnetic tunnel junction (MTJ) with perpendicularly magnetic anisotropy (PMA) that possess large endurance (>1011), and that can be switched at subnanosecond regimes (210ps) with power as low as 130pJ on 60nm devices. We conclude by opening current research focus and recent progress made by our team.
Markov Models for Pattern Recognition: From Theory to Applications

This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.
Harmonic Cancellation Technique for Ultra-Wideband Filter-Less 5G Transmitter

To support ultra-wideband transmitter, bulky filter banks are required for suppressing the harmonics at the power amplifier output. Therefore this increases the transmitter size as well as cost. This paper proposes a digitally supported harmonic injection scheme to cancel out the modulated harmonics with the provision of frequency reconfigurability and small size. The proposed filter less architecture is realized in a low-cost field-programmable gate array (FPGA) suitable for the fifth generation (5G) remote radio unit/remote radio head. For proof-of-concept, three different frequencies are chosen. The experimental verification reveals a reduction of approximately 29 dBc of second harmonic signal. Moreover with the proposed cancellation algorithm, the harmonic reduced to approximately 55 dBc below the main channel.
Large-Scale Parameter Estimation in Channel Sounding with Limited Dynamic Range

In this paper, we examine large-scale parameter (LSP) estimates for spatio-temporal mobile radio channels in the case when the measurement dynamic range is too poor to derive the LSPs for all the measurement locations. Conventionally the LSPs are derived with, e.g., 20 dB dynamic range from the strongest detected multipath component (MPC), but such a measurement dynamic range cannot be guaranteed in all measurements as some of them suffer from large path loss (PL) and/or variation due to shadow fading (SF). We will show that the LSPs, specifically PL, SF, delay spread (DS), and angular spread (AS), can be considered as incomplete data. Incomplete data can be described either by a maximum or a minimum value (or both) when the measurements are with poor dynamic range. We show that the incomplete data should be taken into account in deriving the LSP statistics using the maximum likelihood estimation (MLE) rather than omitted as "outage". Outdoor-to-indoor (O2I) measurements at the 14-14.5 GHz range are used as an example.
Multi-satellite Global Satellite Mapping of Precipitation (GSMaP) - Design and Products -

Estimation of the global distribution of precipitation with high accuracy and resolution has long been one of the major scientific goals. Precipitation map on a global basis is important for modeling of the water cycle, maintaining the ecosystem environment, agricultural production, improvements of weather forecast precision, flood warning, and so on. GSMaP (Global Satellite Mapping of Precipitation) is a project aiming (1) to produce high-precision and high-resolution global precipitation map using satellite-borne microwave radiometer data, (2) to develop reliable microwave radiometer algorithms, and (3) to establish precipitation map techniques using multi-satellite data for GPM.
Practical Graph Mining with R

Discover Novel and Insightful Knowledge from Data Represented as a Graph Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs. Hands-On Application of Graph Data Mining Each chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks. Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical Foundations Every algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique. Makes Graph Mining Accessible to Various Levels of Expertise Assuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners.
User Body Interaction of 5G Switchable Antenna System for Mobile Terminals at 28 GHz

In this paper, a 12-element switchable antenna system for handsets is proposed at 28 GHz. The effect of the head and hand in the impedance matching and radiation pattern is assessed. The electromagnetic field (EMF) exposure in the body is analyzed. The evaluation method of exposure for the previous mobile generations is the specific absorption rate (SAR). Since the penetration depth of the EMFs is lower at the millimeter-wave (mm-wave) band, power density (PD) in free space is employed as evaluation metric instead. The maximum permissible transmitted power to satisfy the proposed limits is calculated, showing a severe discontinuity compared with the maximum power in 4G UE. The exposure of the switchable monopole is compared with a patch phased array, showing a faster decrease in the PD peak than the array at 1.3 cm from the terminal.
Object Detection Based on Saliency and Sea-Sky Line for USV Vision

This paper proposes a fast and effective object detection approach for a monocular vision system aboard an unmanned surface vehicle (USV) under the ocean environment. By utilizing the improved saliency detection and the sea-sky line (SSL) as well as the way of centroid-judging, the object detection method is constructed for an USV on the ocean environment. The improved frequency tune (FT) saliency detection algorithm is proposed to capture the salient objects. Moreover, based on analysing the prior information for the attitudes of USV, Hough transform and Mahalanobis distance are adopted to identify the SSL, which is implemented in a parallel programming. The SSL is used to filter out the clouds, meanwhile the irregular background noises from sea surface are excluded by judging the number of centroids of potential objects below the SSL among the randomly chosen history frames. The proposed approach is applied on our ’Jiuhang490’ USV’s video, and results indicate its real-time performance and accuracy.
An Accelerated OpenVX Overlay for Pure Software Programmers

This paper presents an FPGA-based overlay for accelerating computer vision applications written in OpenVX. A software programmer simply writes an application using the standard OpenVX API. The OpenVX overlay consists of an architecture and a runtime system that runs any OpenVX application, unmodified, in an accelerated manner on an FPGA. The architecture uses a Soft Vector Processor (SVP) for general acceleration, and a library of Vector Custom Instructions (VCIs) to further accelerate specific OpenVX kernels in the FPGA fabric. The VCIs are predesigned in advance by a skilled FPGA designer. The runtime system analyzes the OpenVX computational graph and selects some kernel nodes to be executed by VCIs, with the remaining kernel nodes to be executed by the SVP. In making the selection, the runtime system uses an optimization algorithm and relies upon bitstream relocation and bitstream merging to fit multiple VCIs into a single, fixedsize Partially Reconfigurable Region (PRR). The optimization algorithm must select the VCIs that satisfy the area constraint of the PRR and give the best overall application acceleration. For example, on a Canny-blur OpenVX application, an 8-lane SVP achieves speedup of 5.3 over the hard ARM Cortex-A9. Selecting some nodes as VCIs provides another 3.5 times speedup, for an overall speedup of 18.5. The overlay enables OpenVX programmers with no FPGA design knowledge to accelerate their application.
High Gain Ultra Boost Converter With Voltage Regulation

Nowadays high gain boost converters are efficient for many applications. This work introduces a high gain ultra boost converter with voltage regulation by increasing voltage gain and reducing voltage stress across the switch. This converter uses an autotransformer with coupled inductor integrating on the same core to achieve high output voltage about ten times of input voltage without using endmost duty cycle. And also a Switched/Diode voltage multiplier cell is using in coupled magnetic circuit to recycle the leakage energy. Methods used here for voltage regulations are Line regulation and Reference regulation. The simulation of proposed converter is verified using MATLAB/SIMULINK software. Modes of operation and analysis of the converter are also discussed.
A low-voltage CMOS multiplier for RF applications

A low-voltage analog multiplier operating at 1.2 V is presented. The multiplier core consists of four MOS transistors operating in the saturation region. The circuit exploits the quadratic relation between current and voltage of the MOS transistor in saturation. The circuit was designed using standard 0.6 /spl mu/m CMOS technology. Simulation results indicate an IP3 of 4.9 dBm and a spur free dynamic range of 45 dB.
Segmentation of Crowd into Multiple Constituents Using Modified Mask R-CNN Based on Mutual Positioning of Human

Automatically segmenting groups and individuals from crowd images can be important for surveillance purpose. In this paper, we present a simplified crowd detection and segmentation system. Crowd scene images can be divided into different regions: group, individual, crowd and row. Mask R-CNN with modified backbone architecture is employed for crowd image segmentation. The proposed system is tested on BIWI dataset and self-generated dataset to achieve accuracy of 83%. Furthermore, a crowd dataset* (CVML** crowd dataset) having nearly four thousand images are annotated for training the system.