Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel triple-band microwave chip-less sensor tag for structural health monitoring applications

ABSTRACT In this paper, a novel triple-band microwave chip-less sensor tag is presented for Structural Health Monitoring (SHM) systems. Remote sensing capability of microwave devices is employed here to design a sensor for crack detection in metallic structures. The cracks occurred by natural fatigue or human-made mistakes can be distinguished by desired sensor tags. Triple-band operation of the proposed sensor tag at C and X frequency bands is considered here to precisely detect the narrow-width cracks and their directions. In RCS calculations, the resonant frequencies of the designed sensor tag are displaced at the presence of cracks on its ground plane. The proposed sensor tag can detect cracks with a resolution of 0.2 mm which shows a RCS resonant frequency shift of at least 21.1 MHz. This is a big improvement of detectable range in SHM applications compared to the current sensors.
Electrokinetic Behavior of Solder Powders in Non-aqueous Media

Solder paste dispensing is a key process in the electronics assembly. The increased global demand for smaller, lighter, faster and cheaper assemblies drives the electronics industry to use dispensers with finer apertures. However, reduction in the aperture size of dispensers faces the industry with increased clogging of dispensers and incomplete transfer of paste to the printed circuit board (PCB) pads. One factor contributing to the clogging of dispensers is the agglomeration of the solder powder suspensions which depends on the electrokinetic behavior of the particles. When suspensions are close to the point of no charge, wherein zeta potential is equal to zero, the particles tend to agglomerate. This work focuses on the determination of the zeta potential of Sn, SAC305 and PbSn5Ag2.5 suspensions in isopropyl alcohol (IPA) through microelectrophoresis. The effect of acid, base and flux additives were studied. Results showed that the zeta potential of the solder suspensions are negative in pure IPA and charge reversal occurs in both acidic and basic region. In systems containing flux additives, the zeta potential of the suspensions is highly dependent on the type efflux. FTIR spectroscopy, conductivity measurement, and surface analysis was done in order to explain the observed electrokinetic behavior.
Effect of Different Spatial Resolutions of Multi-temporal Satellite Images Change Detection Application

Remote sensing (RS) satellites may provide much information about the land cover at different resolutions that have been utilized in many military and civil purposes. Multi-temporal images Change detection (CD) is one of the RS applications. Images Co-registration is an important step before the change detection process, and although they are geo-referenced to each other they may not have the same spatial resolution. So, prior to the multi-temporal analysis, images should be similar in pixel size. Using optical sensors with different characteristics and resolutions to obtain the same geographical area may cause effects in change detection results. In this paper; Image difference pixel-based change detection technique is proposed, and the effect of the image pixel size on change detection is studied. Two Images of the same area are taken from two different sensors; Worldview2 and Quickbird2 images with “2”m and “2.4”m pixel size are used respectively. The change detection results show that; for “2” m pixel size, the change detection is 0.15 % With regard to “1” m, the change detection is 0.06 % With regard to “2.4” m, and 0.05 % with regard to “4” m pixel size. On the other hand, the change detection results show that, for “2.4” m pixel size, the change detection is 0.09 % With regard to “1” m and 0.015 % With regard to “4” m pixel size.
Weighted-to-Spherically-Uniform SSIM Objective Quality Evaluation for Panoramic Video

As an important part of immersive media, panoramic video has entered the stage of commercialization in recent years. The panoramic video offers users an immersive viewing experience with its 360°horizontal and 180°vertical viewing angles. The spherical panoramic video has different projection relations in different regions of the sphere when projected to 2-D plane for encoding. In order to more accurately evaluate the objective quality of planar panoramic video and images, this paper proposes a method based on Weighted-to-Spherically-Uniform SSIM (WS-SSIM) to evaluate the objective quality of panoramic video and images. The structural similarity index (SSIM) is multiplied by different weights in different regions so that the spherical distortion observed by the user corresponds linearly to the plane distortion. The performance of the proposed WS-SSIM evaluation method is analyzed through experiments. The results show that the WS-SSIM evaluation method can effectively evaluate the objective quality of panoramic video and images.
Parallel Large-Scale Image Processing for Orthorectification

Recently, as the amount of data that needs to be processed is getting larger, various techniques of parallel processing have been developed to deal with large-scale data efficiently and quickly. In this paper, we shall present fast parallel orthorectification algorithms for large-scale image data on various environments, and compare their performance in terms of speed-up and execution time. Our research consists of two parts: First, we implement parallel orthorectification algorithm on CPU multicore, Xeon-phi multicore and GPU. Second, we analyze these experiment results by comparing the performance of each algorithm. More specifically, we compare the performance of CPU multicore with Xeon-phi and GPU parallelization to find which one is more efficient as well as the performance of Xeon-phi multicore and GPU parallelization. We shall show that for the former, GPU parallelization is more efficient technique than CPU multicore parallelization, while for the latter, Xeon-phi multicore parallelization is more efficient technique than GPU parallelization. This is due to the data upload/download time on GPU. Even if we extend the experiment to infinite-scale, data upload/download time on GPU is still needed. Therefore, Xeon-phi multicore parallelization is better than GPU parallelization not only on the extended environment but also the infinite-scale environment.
MicrothingsChain: Edge Computing and Decentralized IoT Architecture Based on Blockchain for Cross-Domain Data Shareing

With the continuous development of the Internet of Things (IoT), the surge in data generated by sensors and smart devices has caused data overload in designated centralized data centers. However, centralized data centers, such as cloud storage, cannot afford to manage these gigantic data in the suitable way. Due to the lots of devices and senors continuously creating data, traditional IoT architectures must address many challenges that are not designed to provide trust, security, resiliency, and low latency. To solve theses problems, we propose a decentralized architecture named MicrothingsChain based on blockchain with smart contract, which enables edge computing nodes storing corresponding domain IoT data and interacting with each other safely. It brings computing and storage capabilities to the edge of the IoT network and realizes distributed storage of large amounts of data. Specially, MicrothingsChain adopts publish-subscribe mode, in which users can access the data across domain. Furthermore, we present one cryptographic currency for our ecology named MicroCoin. The security analysis and experimental results demonstrate that MicrothingsChain is suitable in practice.
PUBLISH: A Distributed Service Advertising Scheme for Vehicular Cloud Networks

Vehicular Cloud (VC) has gained popularity today allowing mobile users to access a variety of on demand resources while on the move using low cost Vehicular Network. VC enables vehicles with sufficient resources to act as mobile cloud servers and provide their computing, communication and caching resources to nearby vehicles. However, due to high mobility and intermittent connectivity, it is challenging for mobile users to efficiently discover providers’ services before request targeted services from them. Therefore, service advertising is of great interest with which offered services by Provider Vehicles (PVs) in the vehicular cloud can be fast propagated into the network. Given PVs’ limited budget for renting advertiser vehicles, how to achieve the maximum service advertising coverage within a given period of time for a given budget requirements is NP-hard. This work aims to propose a new Centrality-based approach, PUBLISH, for PVs’ services advertising in the vehicular cloud. We exploit the centrality score of both services and vehicles to find the best set of ’appropriate’ vehicles as services advertisers. Results from scalable simulations show that PUBLISH efficiently identify the best services advertisers in comparison to other schemes in the literature.
An Image-Level Classification Framework for Hyperspectral Image with CNNs

Hyperspectral images include richer spectral and spatial information than common images, which are widely used in military, agricultural fields, etc. With the development of sensor technology, the spatial resolution and spectral resolution of hyperspectral images have been improved significantly. However, the disadvantage that there may contain only one part of one object which has different spectral information in hyperspectral images. This will lead to unsatisfactory performance in traditional pixel-level hyperspectral image classification. Thus, a new hyperspectral image classification framework based on convolutional neural network is proposed. First, band selection is adopted to obtain multiple sets of false color images for small sample hyperspectral data. Then, parallel CNNs are introduced to get the classification results of different band combinations. Finally, statistical analysis strategy is performed to obtain the final output result. Experiments show that the classification accuracy of this method is better than that of the previous algorithm on the same dataset.
Response characteristics of the resistivity and water content during the imbibition process in remolded coal without gas

The water content distribution in a coal body is closely related to the gas distribution. It is difficult to directly determine the water content distribution in the process water imbibition. To retrieve the water content by the resistivity value and establish a relationship between the resistivity and water content, the response characteristics of the resistivity and water content of remolded coal formed under different loads are studied by a self‐designed resistivity experimental system. The results show that the resistivity of the remolded coal decreases over time during the imbibition process, including a rapidly decreasing stage and a slowly decreasing stage. The resistivity of the remolded coal is related to the water in the macropore in the rapidly decreasing stage. In the slowly decreasing stage, the resistivity is related to the contact between coal particles. The pressing load, imbibition range, and imbibition speed are negatively correlated with porosity. The resistivity of the steady state is also negatively correlated with the water content and pressing load. The relationship between the resistivity of the steady state and the water content fit a power function. This study reveals the law of resistivity and the water content during the imbibition process and provides an effective methodology to bridge the gap for measuring water content distribution during the imbibition process.
Employing CNN to Identify Noisy Documents Thereafter Accomplishing Text Line Segmentation

Due to the presence of high volume of noise in text documents, it becomes very difficult to achieve high accuracy while performing text line segmentation. Hence, approaches which are dependent on the performance of the line segmentation stage, also suffers. Also due to the variability of noise patterns, the text contents get distorted and sometimes it results in broken strokes of a character. Thus, character level recognition becomes a challenging task for noisy documents. Our proposed method is aimed to address the aforesaid challenges. Since, we are considering the input image to be of noisy kind, a convolutional neural network (CNN) architecture is introduced to initially identify whether the input image contains noise or not. We consider this as a two-class problem and try to identify the respective classes, i.e. noisy or clean. Then, we apply the proposed piecewise projection profile feature and an adaptive region growing based two-stage algorithmic approach which initially identifies the text line upper and lower boundaries in a noisy text document image and then try to regroup the broken strokes of a character to enhance the character recognition accuracy. The proposed method has been tested on a large size dataset containing noisy documents and also different quality measures are computed to establish the effectiveness of the proposed method. From the measures, it can be noted that the text line segmentation accuracy is comparable to other similar state-of-the-art results.
Time-frequency Performance Study on Urban Sound Classification with Convolutional Neural Network

Convolutional neural network (ConvNet) is a class of deep feed-forward neural network which exploits the strong spatially local correlation in natural images. It achieves successful performance in visual analyzing area. Recently, ConvNet has been employed in acoustic processing area and been proved to be able to learn the spectro-temporal pattern of sound and differential them for the classification purpose. In this manuscript, the time-frequency resolution of the input sound is studied for their efficiency in the classification accuracy when ConvNet is adopted. Simulation results shows that the data augment solution, which is called multi-width frequency-delta, presents little contribution for the performance improvement when the network is carefully designed. In addition, a suitable temporal resolution in acoustic sound segmentation can achieve good classification effect.
Ahead of the Curve: A Deeper Understanding of Network Threats Through Machine Learning

The role of big data and machine intelligence in the field of information security is gaining importance as malicious attackers use evasion techniques (polymorphism, encryption, obfuscation) to bypass signature-based detection. As most threats propagate through the network, it is important to have proactive techniques to discover an infection before it damages a computer.This paper will examine how header-based information as well as other characteristics in the HTTP network traffic can be used to train a machine learning model to capture malicious behavior.Network streams tagged as malicious are preprocessed and clustered. It has been found that features in the raw byte stream augmented with handcrafted features are useful in learning the characteristics of network threats.In specific clusters formed, it is possible to identify certain threats targeting a specific server, or if there are characteristics that can be observed in the injected code for exploit detection.Clustering malicious network traffic leads to a better understanding of protection against these types of threats, identification of connected malware campaigns, and insight on future trends.
Weighted Single-Pass Fuzzy c-Means Algorithm Based on Density Peaks

This paper presents an improved single-pass fuzzy c-means algorithm, which is referred to as Weighted Single-Pass Fuzzy c-Means Algorithm Based on Density Peaks (dpwSPFCM). The classical clustering methods can deal with the small-scale data problems rather than the large-scale data problems. In addition, the traditional single-pass fuzzy c-means algorithm is sensitive to the order of input data. In the proposed algorithm, the samples are weighted and reordered according to the density peaks of the data. The dpwSPFCM combines data segmentation and sample weighting technique based on density characteristics, which contributes to getting better clustering results. Experimental results on several UCI datasets have shown that dpwSPFCM has better performance than several well-known algorithms, i.e., FCM, online FCM(OFCM) and single-pass FCM(SPFCM).
A Long Short-Term Memory Network for Vessel Reconstruction Based on Laser Doppler Flowmetry via a Steerable Needle

Hemorrhage is one risk of percutaneous intervention in the brain that can be life-threatening. Steerable needles can avoid blood vessels thanks to their ability to follow curvilinear paths, although knowledge of vessel pose is required. To achieve this, we present the deployment of laser Doppler flowmetry (LDF) sensors as an in-situ vessel detection method for steerable needles. Since the perfusion value from an LDF system does not provide positional information directly, we propose the use of a machine learning technique based on a Long Short-term Memory (LSTM) network to perform vessel reconstruction online. Firstly, the LSTM is used to predict the diameter and position of an approaching vessel based on successive measurements of a single LDF probe. Secondly, a “no-go” area is predicted based on the measurement from four LDF probes embedded within a steerable needle, which accounts for the full vessel pose. The network was trained using simulation data and tested on experimental data, with 75% diameter prediction accuracy and 0.27 mm positional Root Mean Square (RMS) Error for the single probe network, and 77% vessel volume overlap for the 4-probe setup.
Detection of Anomalous Vital Sign of Elderly Using Hybrid K-Means Clustering and Isolation Forest

Age-related changes to vital signs indicate the possibility of a health condition which requires attention. A deviation from normal in vital signs might be an anomaly and indicate an important warning sign of changing health and indicators of the severity of illness that needs an immediate and reflexive response. Once anomalies are detected at the right time, the system will result in reflexive stimulus and will inform it to the care giver. It leads to the good and fast response of outcomes to save the patient’s life. In this study, we proposed hybrid technique of K-Means clustering and isolation forest to detect anomalies. To evaluate the reliability of proposed hybrid technique, we compare existing isolation forest algorithm and hybrid technique of K-Means clustering and isolation forest on labeled datasets obtained from public. The results show that our hybrid technique is more sensitive in detecting anomalies. Applied on some labelled data, hybrid technique has lower error rate. For some labelled data, the hybrid technique has high error rate.
A Novel NLFM Waveform With Low Sidelobes Based on Modified Chebyshev Window

It is well known that the nonlinear frequency modulation (NLFM) chirp waveform can shape advantageously the power spectrum density (PSD) such that the autocorrelation function exhibits reduced sidelobes as the window function. However, differences between the PSD and window function due to the Gibbs effects caused by the Fresnel integral would lead to deteriorative performance of the NLFM chirp waveform. In particular, the correlation function of NLFM waveform with Chebyshev PSD is seriously inconsistent with the lowest sidelobe level of the Chebyshev window function possesses. To overcome the inconsistency, therefore, in this letter, a novel NLFM waveform with modified Chebyshev window PSD is proposed, which combines the Chebyshev with edge distortion compensation, allowing, in theory, low sidelobe level as Chebyshev window. Using theoretical analysis also confirmed by simulation, this letter shows that the novel NLFM waveform possesses low sidelobes without high computational complexity in design.
A CPW-SIW Filter With Wide Stopband Performance Using Grounded Metallic Vias

In this paper, a novel planar bandpass filter with wide stopband suppression is proposed based on the combination of coplanar waveguide (CPW) and substrate integrated waveguide (SIW) technologies. The wide stopband suppression is achieved by eliminating the second harmonic resonance of the CPW cavity with two centrally positioned metallic vias, which play a key role in the design. The proposed filter can obtain good out-of-band rejection (>20 dB) from 16.9 GHz to 35.7 GHz, which spans from 1.3f0 to 2.75f0 where f0 is the center operating frequency of the filter. A demonstration filter is simulated by the full-wave software HFSS and simulation results are presented.
Applying Deep Bidirectional Long Short-Term Memory to Mandarin Tone Recognition

Automatic tone recognition is an important module of Computer-Aided Pronunciation Training (CAPT) system and it can provide many instructive information to guide the learner to practice their tone pronunciation in the process of Mandarin Chinese learning. However, many factors including the tone sandhi, the tonal articulation and other effects have many effects on tone recognition in continuous speech. To deal with these factors, we explored the Deep Bidirectional Long Short-Term Memory (BLSTM), which can utilize the context information from bi-direction in the time series, to Mandarin tone recognition. In addition, convolutional layers were adopted to reduce the spectral variation. The experimental results showed that the performance of proposed CNN-DBLSTM was the best and it achieved the tone error rate (TER) of 7.03% with a 15.1% relative error reduction from the DNN baseline system with TER of 8.27%. It demonstrated that our proposed model was more effective to handle the F0 variations than other models.
Greedy Sparse Channel Estimation for Millimeter Wave Communications

In this paper, we propose a new greedy channel estimation technique which can reconstruct dynamic sparse signals for millimeter wave (mmWave) communication system. In presence of the mobile user under high mobility, the angle of arrival (AoA) and angle of departure (AoD) is changing with time. We assume that the AoA and AoD are varying by discrete-state Markov random process. We formulate the estimation problem for mmWave channel as joint estimation problem of two variables; 1)AoA and AoD indices, and 2)the amplitude vector in the angular domain. The proposed greedy algorithm effectively estimate the AoD and AoA indices accounting for sparse characteristics and dynamics of the channel. Our experimental evaluation demonstrates that the proposed algorithm can obtain good channel estimation performance with small amount of computational power.
Integration of Tungsten Micro-Heaters and Polymer Microfluidic for the Cell Sorting Application

Cell sorting is an important diagnostic tool in medical field to separate different type of cells. Various techniques are employed for sorting with main mechanism being cell analysis and then separation either by electric or magnetic field. In this paper we explain the fabrication process for tungsten micro-heaters on silicon and polymer microfluidic for a formation of a generic platform that can be used for the various cell sorting applications. Polymer material is not only used for the formation of channel for fluidics movement but also used as bonding interface between the silicon wafer and the glass. We report on the bonding strength of the polymer material. It shows that our devices can withstand a pressure in excess of 3 bar which is more than sufficient for most microfluidic applications such as cell sorting. Characterization of micro stream bubble generation life cycle is presnted. The sorting efficiency of our device is reported, with initial characterization done beads to determine sorting rate and sorting yield. We achieved sorting rate of 1,000 beads/s with sorter purity of up to 98 % in initial investigations.