Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Performance of Fuzzy Rule-Based Classification Systems Based on a Nonaveraging Generalization of CC-Integrals Named $C_{F_1F_2}$-Integrals

A key component of fuzzy rule-based classification systems (FRBCS) is the fuzzy reasoning method (FRM) since it infers the class predicted for new examples. A crucial stage in any FRM is the way in which the information given by the fired rules during the inference process is aggregated. A widely used FRM is the winning rule, which applies the maximum to accomplish this aggregation. The maximum is an averaging operator, which means that its result is within the range delimited by the minimum and the maximum of the aggregated values. Recently, new averaging operators based on generalizations of the Choquet integral have been proposed to perform this aggregation process. However, the most accurate FRBCSs use the FRM known as additive combination that considers the normalized sum as the aggregation operator, which is nonaveraging. For this reason, this paper is aimed at introducing a new nonaveraging operator named <inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integral, which is a generalization of the Choquet-like Copula-based integral (CC-integral). <inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integrals present the desired properties of an aggregation-like operator since they satisfy appropriate boundary conditions and have some kind of increasingness property. We show that <inline-formula><tex-math notation="LaTeX">$C_{F_1F_2}$</tex-math></inline-formula>-integrals, when used to cope with classification problems, enhance the results of the previous averaging generalizations of the Choquet integral and provide competitive results (even better) when compared with state-of-the-art FRBCSs.
Wireless Power Transfer Across a Metal Barrier by Combined Capacitive and Inductive Coupling

This paper proposes a method to transfer power across a metal barrier wirelessly by combined capacitive and inductive coupling. At the primary side of the power transfer system a two plate coupling interface is designed to allow for a current flow in the metal barrier, which generates a magnetic field for transferring power via an inductive coil at the secondary side. The current distribution in the metal barrier is analyzed by taking the skin effect into consideration, based on which the magnetic field distribution around the metal surfaces is determined. A three-dimensional (3-D) model is established using a computer simulation technology (CST) package to simulate the magnetic field distribution, which gives consistent results compared to theoretical analysis. Finally, a wireless power transfer system is constructed, and practical measurements show that the magnetic flux density near the edges of the metal barrier is higher than that in the middle region. The output power around the edge is also higher although it is more sensitive to the variation of the distance and positioning of the receiving coil. It has demonstrated that more than 11 W of power can be transferred across an aluminum plate with a thickness of 2 mm when the power pickup is placed at an edge of the plate along the current path.
Wormhole Attack Detection in Wireless Sensor Network

A large number of nodes are deployed in the field, called sensor field. Sensor nodes are resource limited devices. Nodes are deployed through airplane or other vehicles. Nodes are not personally monitored. An attacker can easily launch an attack. Therefore, security is very crucial for wireless sensor network. Security comes from attacks. Major inside attacks on sensor network includes Sybil, selective forwarding, jamming, wormhole, flooding etc. Among all this attacks wormhole attack is very dangerous. Our proposed method for wormhole detection uses round trip time (RTT) and hop count. Simulation results show that our method has good detection accuracy.
Indoor Localization Design and Implementation Based on Software Defined Radio

This paper designs and implements a wireless system that can locate and trace a moving object indoor. The system is based on a flexible and powerful software-defined radio (SDR) platform called Software Universal Platform (SOUP) that can be used to transmit, receive and operate basic signal processing flexibly. To obtain TOF (Time of Flight), the system uses FMCW (Frequency Modulated Continuous Wave) signal to calculate the distance of moving object without precision sensors. The bandwidth of FMCW signal in the proposed scheme is 2.5GHz, and sweep time is 5 milliseconds. Taking static objects indoor into consideration, we use background subtraction to eliminate interference. The host of the system performs localization algorithm based on received data from SOUP. The experiments show the designed wireless system can calculate moving object's distance and draw it's moving path with accuracy within 10cm.
A Scheme of Multi-Domain Cooperative Cost Resource Management in Outdoor+Indoor Het-Net

In heterogeneous network (Het-Net) deployment, when multiple low-power node base stations (i.e., relay, pico, and femto) are deployed outdoor and indoor in the same frequency across macro coverage, the geographical coverage overlap can cause unbalanced cost resource allocation and severe co-channel interference. A scheme of multi-domain cooperative cost resource management in Het-Net is proposed for the mixed outdoor + indoor heterogeneous scenario, which can effectively allocate the load resource and power resource across domains. By the genetic algorithm, the optimal solution for minimizing the network cost resource can be obtained. In numerical evaluation, the cost resource usage in proposed scheme is better than that in the traditional intra-domain scheme.
Continuous Top-k Monitoring on Document Streams

In the age of the real-time web, online news media aggregators, like Google News, and social media sites, like Twitter, strive for effective and efficient filtering of great volumes of information for millions of users. Given the massive diversity and burstiness of information published on the web every minute, filtering, ranking as well as delivering content streams to interested users come to be a challenging task. Furthermore, feedback signals on the content, such as clicks or shares, offer beneficial information on content significance, but also need more complex manipulation techniques for filtering these streams. Scoring functions in this context cogitate both static as well as dynamic ranking factors, such as profile relevance, recency of information and dynamic feedback signals. Present works for the real-time web fail to handle such dynamic scoring functions in an online way as well as rather adjust an approach of iterative execution of snapshot queries. In this work we proposed effectual evaluation techniques of continuous top-k queries over text as well as feedback streams including generalized scoring functions which capture dynamic ranking features. As a first contribution, we generalize state of the art continuous top-k query models, by presenting a common family of nonhomogeneous scoring functions merging query-independent item significance with query-dependent content relevance as well as continuous score decay reflecting information freshness. Our second contribution involves in the definition as well as implementation of efficient in-memory data structures for indexing and evaluating this new family of continuous top-k queries. Our experimentations show that our solution is scalable and outperforms other present state of the art solutions, when restricted to homogeneous functions. Going a step further, in the second portion of this thesis we study the problem of incorporating dynamic feedback signals to the original scoring function in addition to propose a new general real time query evaluation framework with a family of new algorithms for resourcefully processing continuous top-k queries with dynamic feedback scores in a real-time web context.
Electric Vehicle Li-Ion Battery State of Charge Estimation Using Artificial Neural Network

State of Charge is an important parameter of the Battery Management System. It is an indication of the remaining battery capacity. It is very important to have an accurate estimate of the State of Charge, to avoid over charging and over discharging of Lithium ion batteries used in the Electric Vehicle. In this project, we have developed a novel algorithm for the estimation of SOC using Artificial Neural Networks. A comparison of the SOC calculations from the current Battery Management System used in the Electric Vehicle and SOC estimation from the Artificial Neural Network has been presented.
Inferring the Deployment of Source Address Validation Filtering using Silence of Path-Backscatter

IP source spoofing is a consequence of lack of packet level authentication in the Internet which allows attackers to carry out Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. Source address validation filtering is one of the most important scheme that is deployed in the Internet to deter such attacks by filtering the spoofed IP packets. In this paper, we propose a novel scheme to study the deployment of source address validation-filtering by using some special path backscatter messages that are generated by the spoofed traffic. We use the long term absence of such messages from an Autonomous System (AS) to classify it as non-spoofer AS. We use Caida's backscatter dataset for our study. We provide the list of spoofer and non-spoofer ASes from the given dataset. We also provide detailed mathematical analysis for calculating the amount of time we need to wait before declaring an AS as a non-spoofer. Besides, we use the normal approximation of binomial distribution to calculate confidence interval for the proportion of ASes allowing spoofing and to test the hypothesis regarding the spoofing activity in the Internet.
Multistage Fusion With Dissimilarity Regularization for SAR/IR Target Recognition

In this paper, we propose dissimilarity regularization with a multistage fusion stream for a synthetic aperture radar (SAR) and infrared (IR) sensor fusion using deep learning. The multistage fusion structures are composed of multiple layers for fusing all the feature maps generated by the convolutional neural networks. The proposed structure combines feature maps of equivalent levels, ensuring that the spatial information of the corresponding levels can be utilized for fusion. Dissimilarity regularization is the sum of the normalized cross-correlation between the features generated in two different single-sensor streams. The proposed regularization is added to the conventional learning problem of a single-sensor stream, and each single-sensor stream is promoted to learn the disparate types of features for fusion. To evaluate the proposed algorithm, we compare the recognition rate of the proposed algorithm with that of the conventional fusion approaches using the SAR and IR image databases. Finally, the effects of the proposed architecture and regularization on the fusion result are analyzed.
An Augmented Lagrangian Method for the Patch-based Gaussian Mixture Model In Image Deblurring

Since the image deblurring problem is ill-conditioned, a good regularization term can improve the quality of the deblurred result greatly. The expected patch log likelihood (EPLL) is a patch-based regularization prior working with small image patches, it has been shown to be effective and achieves good performance for image deblurring. However, in the EPLL method, it needs the auxiliary parameter becomes large which results to numerical difficulty. To avoid such a problem, we adapt the augmented Lagrangian method into the EPLL image deblurring algorithm. Experimental results show that the proposed deblurring method makes an improvement in image quality and outperforms the existing image deblurring algorithms.
Investigation of Field Plate Misalignment on Electrical Characteristics of AlGaN/ GaN HEMT

Simulation based comprehensive analysis of aigan/GaN Field Plate HEMT has been reported in this paper. The impact of doping levels of GaN cap layer (either p-type or n-type) and lateral misalignment of field plate and its effect on device performance like off-state breakdown voltage, ION/IOFFratio, transconductance and cut-off frequency has been presented. Improvement in off-state breakdown voltage has been seen from the device having thicker channel with n-cap doping and negative FP misalignment length. Enhancement in n-cap doping also results in the improvement in breakdown voltage. However reverse effect has been observed with p-cap layer. Irrespective of poor off-state breakdown voltage, higher Ion/Ioffratio, lower sub-threshold slope and better transconductance can be achieved with FP-AIGaN/GaN having thinner channel HEMT. But the cut-off frequency deteriorates with reduction in channel thickness at lower gate voltage (-5V). Lateral FP misalignment and cap layer type and doping has marginal influence on gate to source capacitance. ATLAS Silvaco software has been used to investigate various performance of the device at room temperature.
Performance Analysis of Dual Branch MRC Diversity over Non-identical Mixture of Gamma Distribution

In this work, dual branch maximal ratio combining (MRC) diversity analysis of generic composite distributions using mixture gamma (MG) fading model has been investigated and analyzed in terms of the probability density function (PDF), the outage probability, the amount of fading (AOF), and the moment generating function (MGF). First, we drive the PDF expression for sum of two MG random variables (RVs). Further, the results were extended to evaluate various performance metrics of the wireless system. The proposed solutions were generic and valid for entire range of fading parameters. The accuracy of the proposed solutions has been corroborated by comparing them with Monte-Carlo simulation.
Design of InP-Based High-Speed Photodiode for 2- $\mu$ m Wavelength Application

In this paper, we proposed and designed the high speed uni-traveling carrier photodiodes operating beyond <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula>. InGaAs/GaAsSb type-II multiple quantum wells (MQWs) were used as the absorption region of uni-traveling carrier photodiode to have optical response beyond <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula>. A rate equation model was developed to study the bandwidth characteristics of this photodiode. The carrier dynamics of the carrier sweeping out process was discussed, and the structure parameters of the quantum wells were optimized. Result shows a 3-dB bandwidth over 40 GHz can be achieved with the optimized design. We also studied the feasibility of dual depletion PIN (DD-PIN) photodiode with MQWs absorber. The optimized DD-PIN photodiode could achieve similar bandwidth under relatively high-bias condition.
Bandpass Frequency Selective Surface with Wide Shielding Effectiveness

This paper presents a novel frequency selective surface topology in X-band with 14.6 % fractional bandwidth from extending from 10.39 GHz to 12.02 GHz. Four transmission zeros at 8.5 GHz, 9.5 GHz, 12.6 GHz and 15.5 GHz have introduced to increase the shielding effectiveness in the stop band of the FSS. The shielding effectiveness is better than 10 dB near the lower and upper −3dB cut-off frequencies. It extends over 1.96 GHz in lower stopband and 4.24 GHz in higher stopband region. Due to the sharper transition in the transition region, it offers a high selectivity needed for many applications.
Optimization of Majority Rule Threshold in Double Threshold Based Cooperative Cognitive Radio Network

In this paper, we investigate a double threshold based cooperative spectrum sensing scenario. Our objective is to determine the optimal threshold for majority rule which must be selected for minimum error in final decision. The CR sensors are assumed to make local hard decisions based on conventional energy detection technique and communicate one bit decision information to the fusion center. Here we assume that sensors whose test statistics fall in ambiguity region do not report to the fusion center. A majority rule is applied at the fusion center in which at least threshold $n$* number of local sensor decision must favor for presence of primary user (PU) to make the final decision on presence of PU. Since choice of $n$* decides error in final decision, we formulate an expression to compute optimal value $n$*, i.e., n*opt which minimizes error in final decision. Further, due to uncertainty in number of sensors with test statistics in ambiguity region, the threshold n*opt also becomes a random variable. Hence we derive a statistical model to characterize the density function of number of sensors with test statistics in ambiguity region, and later exploit it to derive an expression for expected value of n*opt. Our simulation results validate our approach in which we show by selecting n*opt as threshold for majority rule, the error in final decision is at its minimum value.
Dual-Band Dual-Beam Microstrip Patch Antenna for Intelligent Transportation Systems Application

In this work, a compact dual-band and dual-beam patch antenna is reported for Intelligent Transportation Systems applications. The 5.9 GHz band is specifically allocated for the ITS purpose and enables flexible implementation of Intelligent Transportation Systems (ITS). CST Microwave Studio has been used in the full wave simulation of proposed antenna. The fabricated prototype has been characterized in an anechoic chamber. Measured and simulated reflection coefficient at desired WLAN/ITS band of this antenna is better than −20 dB at 5.3 GHz and −15 dB for 5.9 GHz while the measured gain of the antenna is 4.8 and 5.7dBi at 5.3 GHz and 5.9 GHz, respectively. The simulated far-field (3D) radiation pattern of the antenna shows dual-beam radiation pattern. The simple physical geometry of the antenna is attractive for use in intelligent transportation systems.
A Study on Highly Resonant Wireless Power Transfer Scheme Using Planar Coils

We have been studying the possibility of the highly resonant wireless power transfer (HR-WPT)using planer coils for making it applicable to portable devices. In this study, we use both experimental and numerical approaches to improving the transfer efficiency of the HR-WPT with planar coils. The result of the experiment showed that the each properties that define a planar coil configuration will interact each other in a complex manner, so it is found that experimental evaluation is inefficient. We simulated the planar coils in the finite difference time domain (FDTD)method and compared to the experimental result. The comparison results suggest that the numerical and the experimental result does not show good agreement to each other and the program needs fixing.
Study on Mutual Coupling Reduction Technique for MIMO Antennas

In recent years, multiple-input-multiple-output (MIMO) antennas with the ability to radiate waves in more than one pattern and polarization play a great role in modern telecommunication systems. This paper provides a theoretical review of different mutual coupling reduction techniques in MIMO antenna systems. The increase in the mutual coupling can affect the antenna characteristics drastically and therefore degrades the performance of the MIMO systems. It is possible to improve the performance partially by calibrating the mutual coupling in the digital domain. However, the simple and effective approach is to use the techniques, such as defected ground structure, parasitic or slot element, complementary split ring resonator, and decoupling networks which can overcome the mutual coupling effects by means of physical implementation. An extensive discussion on the basis of different mutual coupling reduction techniques, their examples, and comparative study is still rare in the literature. Therefore, in this paper, different MIMO antenna design techniques and all of their mutual coupling reduction techniques through various structures and mechanisms are presented with multiple examples and characteristics comparison.
Compressive Sensing Using Symmetric Alpha-Stable Distributions for Robust Sparse Signal Reconstruction

Traditional compressive sensing (CS) primarily assumes light-tailed models for the underlying signal and/or noise statistics. Nevertheless, this assumption is not met in the case of highly impulsive environments, where non-Gaussian infinite-variance processes arise for the signal and/or noise components. This drives the traditional sparse reconstruction methods to failure, since they are incapable of suppressing the effects of heavy-tailed sampling noise. The family of symmetric alpha-stable (SαS) distributions, as a powerful tool for modeling heavy-tailed behaviors, is adopted in this paper to design a robust algorithm for sparse signal reconstruction from linear random measurements corrupted by infinite-variance additive noise. Specifically, a novel greedy reconstruction method is developed, which achieves increased robustness to impulsive sampling noise by solving a minimum dispersion (MD) optimization problem based on fractional lower-order moments. The MD criterion emerges naturally in the case of additive sampling noise modeled by SαS distributions, as an effective measure of the spread of reconstruction errors around zero, due to the lack of second-order moments. The experimental evaluation demonstrates the improved reconstruction performance of the proposed algorithm when compared against state-of-the-art CS techniques for a broad range of impulsive environments.
Efficient Detection of Phishing Attacks with Hybrid Neural Networks

Many machine learning techniques and social engineering methods have been adopted and devised to combat phishing threats. In this paper, a novel hybrid deep learning model is proposed to identify phishing attacks. It incorporates two components: an autoencoder (AE) and a convolutional neural network (CNN). The AE is adopted to reconstruct features that enhances correlation relationship among the features explicitly. The results from the experiments show that the model is able to detect phishing attacks with a mean accuracy over 97.68%. , yet it has high generalization ability and can detect phishing attacks in the receivable time scale.
Suspended Frame Structure with Phononic Crystals for Anchor Loss Reduction of MEMS Resonator

In order for micro/nano-electro-mechanical systems (M/NEMS) resonators to make real impact to practical applications such as wireless communications and autonomous sensor networks, further improvements are needed in particular in the enhancement of quality factor $(Q)$. Herein, a novel geometry design with a suspended frame structure integrated with phononic crystals (PnC) unit cells was demonstrated to significantly reduce the anchor loss of ring-shaped thin-film piezoelectric-on-silicon bulk acoustic wave resonator and then enhance its $Q$. Finite-element-analysis (FEA) simulation was employed to systematically study the proposed design, which reveals that the $Q_{anc}$ of this novel geometry (i.e., R6T20FP-w5 with $Q_{anc}$ of 100000) achieved a maximum 83-fold enhancement compared with the pristine one (i.e., RNT10-w26 with $Q_{anc}$ is 1200).