Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhacing Image Mosaicing with Adaptive Local Homographies

Image alignment is usually the first move in modern image stitching pipeline; and utilizing a global homography to align two images is justified on the condition that only pure rotation concatenates two views or the whole scene is planar. Ghosting effects frequently happen because aforementioned condition is rarely satisfied in practice; so we propose adaptive local homographies to improve image alignment further based on as-projective-as-possible warp (APAP). Specifically, we introduce an adaptive inlier selection scheme for fitting local homographies while APAP adopts moving DLT to do this with improper global inliers, so our warp can be more ”local” than APAP while still globally projective. As experiments show our warp moderately outperforms APAP in alignment accuracy and deblurring especially in the case of large parallax, which leads to a powerful alternative to moving DLT with acceptable overhead.
A Software Defined Radio based Multi-Function Radar for IoT Applications

Context and location services become increasingly important in many Internet of Things (IoT) applications such as smart home, disaster assistance, security and safety in smart factory and so on. Traditionally, these services are provided by independent systems. In this paper, we propose a software defined radio (SDR) based multi-function radar for respiratory rate and moving speed detections as well as ranging, which can be used to provide human body detection and tracking in human-centric IoT applications. Since SDR concept is adopted, the same hardware platform could be shared with wireless communications periodically by re-defining. The system architecture design is introduced. The detection algorithms are presented. The ranging performance is compared with some typical ones using simulations. The detections for respiratory rate, ranging and moving speed are carried out on the SDR platform consisting of FMCOMMS5 and ZC706. The system occupies 2MHz bandwidth. It is shown that the proposed multi-function radar achieves the estimation of the human respiratory rate with <5% errors compared to a clinic-grade device. The ranging resolution achieved is <0.3 m. The tiny movement as slow as 0.19 m/s can be detected.
Performance Analysis of Pairing-Based Elliptic Curve Cryptography on Constrained Devices

The paper deals with the implementation aspects of the bilinear pairing operation over an elliptic curve on constrained devices, such as smart cards, embedded devices, smart meters and similar devices. Although cryptographic constructions, such as group signatures, anonymous credentials or identity-based encryption schemes, often rely on the pairing operation, the implementation of such schemes into practical applications is not straightforward, in fact, it may become very difficult. In this paper, we show that the implementation is difficult not only due to the high computational complexity, but also due to the lack of cryptographic libraries and programming interfaces. In particular, we show how difficult it is to implement pairing-based schemes on constrained devices and show the performance of various libraries on different platforms. Furthermore, we show the performance estimates of fundamental cryptographic constructions, the group signatures. The purpose of this paper is to reduce the gap between the cryptographic designers and developers and give performance results that can be used for the estimation of the implementability and performance of novel, upcoming schemes.
Efficient DOA Estimation for Coprime Array via Inverse Discrete Fourier Transform

In this paper, we propose an inverse discrete Fourier transform (IDFT)-based direction-of-arrival (DOA) estimation algorithm for coprime array, where both DOAs and power of the sources can be efficiently estimated with an increased number of degrees-of-freedom. Specifically, the IDFT is generalized to realize the transformation between the defined angular-spatial domain and the spatial domain. With such a relationship, the IDFT is directly implemented on the second-order virtual signals characterized by the angular-spatial frequencies, and it is proved that both the DOAs and the sources power can be estimated from the resulting spatial response. Meanwhile, the window method and the zero-padding technique are sequentially incorporated to alleviate the spectral leakage and improve the estimation accuracy, respectively. The direct IDFT solution presents a remarkably reduced computational complexity as compared to the existing algorithms exploiting coprime array, and the simulation results validate the effectiveness of the proposed DOA estimation algorithm.
Radiation Hardening By Design Integrated Circuits Enabling Low-Cost Satellites for Internet-of-Things

Satellite technology serves as the key enabler to push the Internet-of-Thing (IoT) connectivity to its full potential. Unfortunately, present Integrated Circuits (ICs) for satellites are very expensive and dated. In this paper, we present an interesting investigation into the design of advanced ICs on the basis of a low-cost commercial bulk CMOS process. Subsequent to this, we further present a case study of the design of a high-reliability radiation-hardened voltage reference by means of an innovative Radiation Harden By Design (RHBD) approach.
MmWave beamforming for UAV communications with unstable beam pointing

Millimeter wave (mmWave) communications of unmanned aerial vehicles (UAVs) have drawn dramatic attentions for its flexibility on a variety of applications. Recently, channel tracking base on the spatial features has been proposed to solve the problem of beam misalignments due to the UAV navigation. However, unstable beam pointing caused by the non-ideal beam tracking environment may impact the performance of mmWave systems significantly. In this paper, an improved beamforming method is presented to overcome this shortcoming. Firstly, the effect of the beam deviation is analyzed through the establishment of the equivalent data rate. Then, combining the quantification of spatial angle and the improved orthogonal matching pursuit (OMP) algorithm, an optimized beam corresponding to the beam deviation is obtained. Simulation results show that the optimized beam of the proposed approach can effectively improve the spectral efficiency without improving the complexity when the beam pointing is unstable.
Multiple Sclerosis Identification by Grey-Level Cooccurrence Matrix and Biogeography-Based Optimization

This paper presents a new method amongst developing computer vision algorithms for the detection of multiple sclerosis (MS). Lesions caused by MS are detectable on MRI images. CV algorithms present subjective approaches in detection. In this study, we used the grey-level co-occurrence matrix to extract detailed texture features from the spatial distribution of greytone on MRI images. Multi-layered feedforward neural network was used as the classifier. Then, we selected biogeography-based optimisation algorithm to train this classifier. Through cross-validation, the method achieved sensitivity, specificity and accuracy of 92.75±1.31%, 92.76±1.65%, and 92.75±1.43% respectively. We validated the efficiency of the classifier, but overall, the method is inferior to state-of-art algorithms of MS lesion detection in all aspects.
SOA-Integrated Silicon Photonics Switch and Its Lossless Multistage Transmission of High-Capacity WDM Signals

We experimentally demonstrate the lossless transmission of wavelength division multiplexing (WDM) signals through a silicon-photonics 4 × 4 switch with a flip-chip bonded 4-channel semiconductor optical amplifier (SOA). We first optimized the input power and gain of the SOA-integrated switch to obtain the optimum operation point in terms of the transmitted signal quality. We then performed simultaneous transmission of 8-ch, 32-Gbaud, SP 16-QAM WDM (800 Gb/s) signals through all the four paths of the switch. The effect of crosstalk on the switch was very small, and thus could not be observed. We also examined multistage (up to four stages) transmission of the signals with circulating configurations. We show that even for a 4-stage transmission, the bit error rate of the transmitted signal is below the 20% forward-error-correction limit. Finally, we discuss approaches to improve the optical signal-to-noise ratio of the transmitted signals to enlarge the signal quality margin and increase the possible number of the cascading stages and/or WDM channels for wide applications.
Impact of Cooperative Spectrum Sensing on the Cognitive Relay Network With A Direct Link

This paper studies the performance of cognitive single-source and multi-relay cooperative communication, considering the two phases of cooperative spectrum sensing and cooperative communication, investigates the system’s reliability. In the phase of spectrum sensing, the cognitive secondary user’s (SU’s) source and relays sense the licensed primary user’s (PU’s) spectrum by the energy detection algorithm respectively, and get the final result by OR rule. Only when the final result of spectrum sensing indicates the presence of a spectrum hole, the cognitive SU can access the licensed frequency band. In the phase of cooperative communicating, for the two cases: (1) the cognitive SU detects the spectrum hole and the PU doesn’t access the frequency band; (2) the cognitive SU senses a spectrum hole but the licensed PU is using the frequency band, the relay selection strategy based on signal-to-interference-plus-noise ratio (SINR) is proposed respectively. In addition, this paper also investigates the system’s reliability with the SU destination adopting maximum ratio combining (MRC) and selection combining (SC), and analyze the performance with the number of relays in the system. It is shown that the increase of the cognitive SU relays can not only improve the performance of the spectrum sensing but also enhance the reliability of the cognitive network.
A 128 Gb/s PAM4 Silicon Microring Modulator With Integrated Thermo-Optic Resonance Tuning

We report the first demonstration of a silicon photonic microring modulator with modulation data rate up to 128 Gb/s (64 Gbaud PAM4). The microring modulator exhibits an electro-optic phase efficiency of V<inline-formula><tex-math notation="LaTeX">$_\pi \cdot$</tex-math></inline-formula> L = 0.52 V<inline-formula><tex-math notation="LaTeX">$\cdot$</tex-math></inline-formula>cm, an electro-optic bandwidth of 50 GHz, and a measured transmitter dispersion eye closure quaternary of 3.0 dB at this data rate. In addition, the resonant wavelength of the microring modulator can be tuned across a full free spectral range using an integrated heater with a thermo-optic phase efficiency of 19.5 mW<inline-formula><tex-math notation="LaTeX">$/\pi$</tex-math></inline-formula>-phase shift.
Comparison of Supervector and Majority Voting in Acoustic Scene Identification

Acoustic scene identification aims to identify the acoustic environment from the acoustic signal. Usually one first divides a piece of acoustic signal into multiple short-time frames and then calculates frame-level features. A natural question is then how to make use of these frame-level features for identification purposes. In this paper, we compare two feature aggregation methods. One method is Majority Voting (MV), which treats each frame-level feature as an independent feature vector and then perform identification using majority voting strategies. In this way, an acoustic signal is represented by multiple feature vectors. The other method is Supervector, which maps the frame-level features to a single feature vector. In this way, an acoustic signal is represented by one feature vector. Particularly, we consider three types of Supervector, which are Gaussian Supervector, Factor Analysis Supervector, and i-vector. We then compare Supervector with MV in an acoustic identification task. Different classifiers are employed, including Gaussian Mixture Model (GMM), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Deep Neural Network (DNN). Experimental results indicate that these two feature aggregation methods give very similar performances, nonetheless, each has its own advantages and disadvantages.
Website Classification Using Word Based Multiple N -Gram Models and Random Search Oriented Feature Parameters

Website classification is a convenient starting point for building an intelligent web browser and social networking sites that can understand the favorite categories of a user and also detect adult or harmful websites perfectly. Classifying the web sites using the information of the Uniform Resource Locator (URL) is an important and fast technique. A perfect result is needed for URL classification to make it usable in the real world applications. So we have proposed an improved approach for URL classification that is able to provide a better result. We have introduced the word-based multiple n-gram models for efficient feature extraction and multinomial distribution for Naive Bayes classifier under the Random Search pipeline for hyperparameter optimization that finds the best parameters of the URL features. The experimental result of our research is compared with the result of previous research works and we have shown a better result than the existing result. Our experimental result provides 88.77% in recall and 87.63% in F1-Score which is the best performance so far.
Robust MVDR Beamformer Based on Complex Gaussian Mixture Model With Phase Prior

This paper studies a robust beamforming algorithm for microphone array speech enhancement in the presence of speaker interference and background noise. Accurate steering vector estimation is essential for the performance of the beamformer as well as for the successful speech enhancement. Recently, a time-frequency masking technique based on complex Gaussian mixture model (CGMM) was proposed to efficiently estimate the steering vector for beamforming. However, its performance will degrade with observations that contain noise or/and interference only samples due to the inaccuracy of the CGMM parameter estimation. In this paper, a phase prior for a spatial correlation matrix (a CGMM parameter) is proposed to improve the steering vector estimation in the presence of speaker interference and background noise. Computer simulations are conducted to verify the advantages achieved by the proposed phase prior-based beamformer, in comparison with the conventional beamformer and the CGMM-based approach without prior.
Content Based Call for Papers Recommendation to Researchers

Call for papers is an invitation to researchers for paper publication. Finding relevant conference for paper publication is important as it has a direct impact on researchers' profile, papers' acceptance and future citation of research work. However finding relevant conference is a time consuming job for researchers due to increasing number of conferences on daily basis. To address these issues, various Recommender Systems (RS) have been developed. Most of them are based on collaborative approaches which exploit users' preferences for recommendations, however such RS suffer from cold start problem. On the other hand, systems based on content based approaches uses items' features for recommendations, however these RS face various problems including limited content analysis and irrelevant recommendations. We addressed these issues by developing a content based CFP (Call for Papers) recommender system using selected features that can reflect researchers' preferences. These features include title, abstract, keywords, cited papers' titles and cited events. Experimental results show that the proposed system solve problems that traditional CFP recommender systems face and produce quality recommendation results.
BlockChain for IoT Security and Management: Current Prospects, Challenges and Future Directions

The paper presents an in detailed case study of integrating BlockChain (BC) in IoT ecosystems in order to achieve security and privacy. Since the use in cryptocurrency, BlockChain has gained tremendous attraction due to the versatile application prospects it raises. As internet of things (IoT) is emerging towards the smart city requirements, device or data security seems to be a major concern. Therefore, BlockChain based decentralized and distributed system can meet the privacy preserving management in IoT ecosystems. In this paper, we discuss the necessity of utilizing BlockChain for IoT security, privacy, management. We present a literature overview of current progresses and security enhancement in sensor networks using BlockChain; their scope, prospects and limitations as well. Finally, we suggest some future directions to indicate further areas of improvement.
Fully Heterogeneous Collective Regression

Prior work has demonstrated that multiple methods for link-based classification (LBC) can substantially improve accuracy when the nodes of interest are interconnected. To date, however, very little work has considered how methods for LBC could be applied in domains that require continuous, rather than categorical, predictions. In addition, prior work with LBC has learned only one predictive model to use for all nodes of a given type, but some domains exhibit significant node diversity that is not well-suited to this approach. In response, we introduce fully heterogeneous collective regression (FHCR), a new method that learns node-specific models from data and uses these models to jointly predict continuous outputs. We apply FHCR to a voting prediction task, and create novel correlation-based links that outperform alternative methods. In addition, we introduce multiple new methods for inferring continuous outputs that can incorporate linkbased information, and show that regression-specific methods based on Bayesian inference outperform the naive approach of inserting regression into existing LBC methods. Overall, we demonstrate the viability of the new FHCR paradigm by producing results that are comparable or better than those of previous link-unaware methods, yet are at least two orders of magnitude faster.
An Artificial Life Simulation to Observe DNA Sequence Repeat Pattern

This study presents an artificial life simulation to observe DNA sequence repeat pattern to observe probable repetitions of DNA sequence of an agent in future generations. This also includes research into recognizing patterns in repetitions and the ratio of unique and repetitive DNA sequences along with observing agent's evolution and life expectancy. We chose a small representative of life namely ‘agents'. The agents have sufficient intelligence to make survival decisions and can adapt to minor scenario changes. The 2D plane is optimized so that agents can move freely on the plane without clogging corners. The agents use diploid reproduction system and two-point crossover to produce offsprings. The agents also use simple learning algorithms and weighted random selection based movement logic. Simple logic that imitates Evolutionary processes further adapts the agents to their environment by making them more efficient. The DNA sequences are stored and activities of the agents of a population are monitored and logged accordingly in our system. After gathering data from successful sessions we analyze these data to observe patterns in repetitions of a previous DNA sequence and detect anomalies, observe life's evolving nature with time and the survival rate. The agents are evolved to ensure optimal ratio between unique and repetitive DNA sequences.
MULTI-FIDELITY MODELS FOR DECOMPOSED SIMULATION OPTIMIZATION PROBLEMS

Hierarchical problem decomposition methods are widely used in optimization when the scale of the problem is large. The master problem is hierarchically decomposed to several sub-problems and the detail level of the sub-problems increases during the optimization from bottom to top. When simulation is used to estimate unknown functions, models with different detail are used at each level. However, the simulation outputs used to solve the sub-problems of a hierarchy level are not used anymore at higher levels. An approach is proposed in this paper to reuse these experiment data to improve the efficiency of the simulation-optimization algorithm. A multi-fidelity surrogate model is built in each sub-problem to guide the search of the optimum. The performance of the approach is numerically assessed with the goal of understanding its potentialities and the effect of algorithm parameters over optimization results.
Cooperative Operation of R-SFCL and Online Current Limitation Strategy of GCPVS Under Asymmetrical Faults

Grid-connected photovoltaic systems (GCPVS) should remain grid-connected and inject specified reactive current in accordance with fault-ride-through requirements. To minimize voltage ripples on the dc link voltage and restrict the maximum phase currents, a modified online current limitation strategy (OCLS) is presented in this paper. Transient performance of GCPVS is enhanced as resistive-type superconducting fault current limiter (R-SFCL) is applied under severe asymmetrical faults. By introducing proper resistance selection process, the cooperative operation of OCLS and SFCL is proved to be valid in performance improvement.
Statistical Analysis Applied to Simulating and Measuring Circularly-Polarized Millimeter-Wave Antennas [Measurements Corner]

A framework is presented to investigate the leading causes that determine deviations between simulation and measurement results in circularly polarized (CP) antennas operating in the millimeter-wave (mm-wave) range. For mm-wave antennas in general and CP antennas in particular, the whole validation procedure is considered to be especially challenging. The mechanical differences between the application design of the antenna and its measurable counterpart also have a significant impact on the antenna characteristics.
Pseudo-Full-Bridge Inverter With Soft-Switching Capability for a Quarter-Phase Ultrasonic Motor

This paper presents a high-efficiency inverter with the aid of the soft-switching technology, which is accomplished by the resonance of the in-series inductance with the snubber capacitance. The equivalent circuit of the quarter-phase ultrasonic motor is initially deduced, based on which the topology along with the operations of the novel inverter is subsequently presented. The configuration of the new quarter-phase inverter shows its uniqueness in terms of the shared arms and the especially designed snubber capacitance in comparison with the existing inverters. The circuit analysis infers that the dead time of the inverter is the key factor to arrive at the zero-voltage-switching or the zero-current-switching operations. Experimental measurements have demonstrated that the efficiency of the whole drive increases by a factor of 1.25 after replacing the traditional inverter with the proposed one. It should also be noted that the proposed inverter in association with the design method of the dead time not only can work for the quarter-phase ultrasonic motor, but also can be applicable for other kinds of quarter-phase devices. The soft-switching operations are achieved in the quarter-phase inverter for the first time, whose amplitudes of the output voltage and switching frequency are not required to be constant any more.
Deeplearning Model Used in Text Classification

Text classification is one of the most widely used natural language processing technologies. Common text classification applications include spam identification, news text classification, information retrieval, emotion analysis, and intention judgment, etc. Traditional text classifiers based on machine learning methods have defects such as data sparsity, dimension explosion and poor generalization ability, while classifiers based on deep learning network greatly improve these defects, avoid cumbersome feature extraction process, and have strong learning ability and higher prediction accuracy. For example, convolutional neural network (CNN)[I]. This paper introduces the process of text classification and focuses on the deep learning model used in text classification.