Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Two Stage Operational Amplifier and Implementation of Flash ADC

In this paper, Flash Analog to digital converter is implemented whose resolution is 3-bits. The designed Flash ADC consists of a resistive ladder network, comparators, the thermometer to a binary encoder and the entire design is carried out using LTspice tools employing 180nm technology. The reference voltage applied to the resistive ladder network is 1.8V. A two-stage operational amplifier is used as a comparator in the flash ADC. Binary code is obtained from the thermometer code by utilizing a priority encoder. The major problem that usually appears in flash ADC is as the number of resolution bits increases, the Area, as well as the power consumption of the circuit, also increases. In this paper, we principally concentrated to lessen the power consumption of the ADC by optimizing encoder circuitry. With the purpose of reducing power consumption, Encoder is implemented using 2:1 mux based on various logics such as switch logic, pass transistor logic as well as CMOS logic. In addition to this, the Wallace tree encoder was also implemented. Performance parameters of Flash ADC such as conversion time as well as average power are calculated and compared. It is verified average power obtained using Wallace tree encoder is 910pW and it is less compared to encoder implemented with other designs.
Microstrip Memristive Switch and Its Applications to RF Devices

In order to achieve the characteristics of non-volatility and low power consumption, a memristive RF switch is proposed on a microstrip line with good performance up to 5 GHz. Based on Conductive bridging random access memory (CBRAM) technology, this switch uses metal-insulator-metal (MIM) structure, Copper/Nafion/Au specifically. Then, a reconfigurable bandpass filter is designed at 0.91 and 1.0 GHz with the ON-/OFF-state memristive RF switches, respectively. A 180° reflective type phase shifter is designed at 5 GHz with the microstrip memristive RF switches. These memristive RF devices may push the envelope for intelligent RF systems.
Optimized Design of Low Loss Differential Mode Bilateral Coplanar Waveguide

The optimized design of a low loss differential mode bilateral coplanar waveguide (DBCPW) is presented. It is based on the printed circuit board (PCB) technology without using the vias. The conductor loss is reduced by decreasing the current crowing on the signal lines. The simulation results show that after the optimization the magnitude of S21 of the proposed DBCPW reaches to -0.190 dB at 37.5GHz with a length of 40 mm. It is the best one compared with that of optimized microstrip line, coplanar waveguide (CPW) and differential coplanar waveguide (DCPW), which is -0.473 dB, -0.249 dB and -0.219 dB, respectively. It was found that the optimized results from the lowest loss may also give the reasonable value of the characteristic impedance. The propagation phase constant of DBCPW is also given, which presents its low dispersion characteristic. The proposed DBCPW is useful both in circuits and in antennas, especially for millimeter wave antenna arrays with electrically large size where the requirement on the loss of the transmission line is quite critical.
Unified Approach to Evaluation of Real and Complex Repetitive Controllers

Repetitive controllers (RCs) are known for their ability of controlling periodic exogenous signals, even if these signals have high harmonic content. Due to the variety of repetitive controllers proposed in the literature, which are significantly different from each other, a comparative evaluation in a collective way is analytically complex. This fact implies a greater difficulty to select the appropriate RC strategy when designing a control system, what makes most control system designers to not use this class of controllers. In order to solve this problem, the present paper develops a unified approach for representation of (real and complex) repetitive controllers, which is based on the use of multiple primitive repetitive cells in parallel. Through this approach the main characteristics of a repetitive controller, such as stability properties, dynamic response, set of harmonic components that are effectively compensated and computational burden, are easily identified. Furthermore, in order to validate the potential of the proposed unified approach, comparative studies of $nk \pm m$ RCs and $nk+m$ RCs are performed. An experimental application based on a three-phase shunt active power filter is implemented to validate the theoretical evaluation presented in this paper.
Direction-of-Arrival Estimation of Coherent Sources with Coprime Array Interpolation

Compared with traditional uniform linear array, coprime arrays are capable of achieving increased degrees of freedom (DOF) in virtue of the idea of difference coarray. However, the existence of holes in coarray prevent the utilization of the full coarray. The interpolation based on the covariance matrix reconstruction approach (CMRA) and the unclear norm minimization (NNM) are effective to deal with such hole problem, but its performance would severely deteriorate for dealing with coherent source. In this paper, an efficient coprime array interpolation method based on atomic norm minimization (ANM) is proposed. By using dual method and Vandermonde composition, coherent sources can be identified efficiently. The numerical simulations are presented to verify the improved performance of the proposed method.
Analysis of Various Sentiment Analysis Techniques of NLP

Sentiment Analysis and opinion mining complement each other. This approach is concerned with classifying the opinions of public regrading a product using NLP. This approach considers sentiments and viewpoint of individuals about the occurrence of an episode. Assessment mining is generally helpful in different domains such as business brand surveys, web-based media study, and movie analyses, and so forth. The sentiment analysis is an important method in formation of frameworks based on recommendations. The people give the content audits like web-based reviews, feedback or remarks on the web-based media and web-based business sites. This textual content is an essential wellspring of client’s conclusions. In sentiment analysis, the main aim is to classify the users’ sentiments into positive, negative and unbiased. This type of analysis indicates the ubiquity or importance the product in the marketplace. Every human being has their different opinion, feelings, thoughts, and emotion for an event this can be known with the help of sentiment analysis. The extraction of features and classification are the steps in sentiment analysis. This work is centered around implementing a fresh approach to sentiment analysis. The sentiment analysis techniques have various phases which include pre-processing, feature extraction and classification. The various machine learning algorithms for sentiment analysis are reviewed in terms of certain parameters.
Data Fusion Method and Internet of Things (IoT) for Smart City Application

Internet of things is the advanced technological enhancement to make every city as smart. Recent days everywhere across the world IoT became one of the leading technologies. The popularity behind this technology is lies in its diverse nature as it integrates various heterogenous systems to work together. This heterogeneous nature lead to many challenging tasks. Apart from all these aspects Data fusion is one the peak improvement in any autonomous systems, it improves the systems functionality by applying fusion algorithms. This paper is going to discuss the challenging things of data fusion and will propose specific fusion algorithm to make any autonomous system as perfectly autonomous. Lastly, it is going to present how Data Fusion is achieved in IoT. Managing heterogeneous Bigdata and timely data and sensor fusion in IoT is a challenging task meanwhile data analysis, accuracy, reliability and decision-making is also vital role, these aspects are all presented in this paper. It has taken connected car as an example to discuss the above said.
Intelligent Framework Using IoT-Based WSNs for Wildfire Detection

IoT-based WSNs have proved their significance in delivering critical information pertaining to hostile applications such as Wildfire Detection (WD) with the least possible delay. However, the sensor nodes deployed in such networks suffer from the perturbing concern of limited energy resources, restricting their potential in the successful detection of wildfire. To extenuate this concern, we propose an intelligent framework, Sleep scheduling-based Energy Optimized Framework (SEOF), that works in two folds. Firstly, we propose an energy-efficient Cluster Head (CH) selection employing a recently developed meta-heuristic method, Tunicate Swarm Algorithm (TSA), that optimizes the five novel fitness parameters by integrating them into its weighted fitness function. Secondly, we perform a sleep scheduling of closely-located sensor nodes based on the distance threshold calculated through a set of experiments. Sleep scheduling methodology plays a pivotal role in abating the number of data transmissions in SEOF. Finally, we simulate SEOF in MATLAB under different scenarios to examine its efficacy for the various performance metrics and scalability features. Our empirical results prove that SEOF has ameliorated the network stability period for two different scenarios of network parameters by 35.3% and 216% vis-à-vis CIRP.
A Simulation-Optimization Approach for Locating Automated Parcel Lockers in Urban Logistics Operations

Experts propose using an automated parcel locker (APL) for improving urban logistics operations. However, deciding the location of these APLs is not a trivial task, especially when considering a multi-period horizon under uncertainty. Based on a case study developed in Dortmund, Germany, we propose a simulation-optimization approach that integrates a system dynamics simulation model with a multi-period capacitated facility location problem (CFLP). First, we built the causal-loop and stock-flow diagrams to show the APL system’s main components and interdependencies. Then, we formulated a multi-period CFLP model to provide the optimal number of APLs to be installed in each period. Finally, Monte Carlo simulation was used to estimate the cost and reliability level for different scenarios with random demands. In our experiments, only one solution reaches a 100% reliability level, with a total cost of 2.7 million euros. Nevertheless, if the budget is lower, our approach offers other good alternatives.
Density Gain-Rate Peaks for Spectral Clustering

Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes. Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic community. However, spectral clustering still cannot well handle some shapes of sample distributions in the space of extracted features, and density peak clustering encounters performance problems because it cannot mine the local structures of data and well deal with non-uniform distributions. In order to solve above problems, we propose the density gain-rate peak clustering (DGPC), a new type of density peak clustering method, and then embed it in spectral clustering for performance promotion. Firstly, in order to well handle non-uniform sample distributions, we propose density gain-rate for density peak clustering. Density gain-rate is based on the assumption that the density of a clustering center will be higher with the reduce of the radius. Even under non-uniform distributions, the cluster center in low density region will still have a significant density gain-rate thus can be detected. We combine density gain-rate in density peak clustering to construct DGPC method. Then in the framework of spectral clustering, we use our new density peak clustering to cluster the samples by their extracted features from a similarity graph of these samples, such as the neighbor-based similarity graph or the self-expressiveness similarity graph. Compared with the previous spectral clustering and density peak clustering, our method leads to better clustering performances on varying shapes of sample distributions. The experiment measures the performances of our clustering method and existing clustering methods by NMI and ACC on seven real-world datasets to illustrate the effectiveness of our method.
A Miniaturized 60GHz FMCW Radar for Short Range and High Precision Detection

A miniaturized, low-power and short-range 60 GHz frequency modulated continuous wave (FMCW) radar system is presented for small size target detection. It mainly consists of two antennas, a transceiver and a signal processor. The system design, subsystem integrated realization and performance verification is developed. Experimental results using our fabricated prototype show that the detection accuracy of 0.2m can be achieved for 15cm*15cm metal targets with detection distance of 15m under the system total size of 29mm in diameter and 5mm height conditions, which agrees well with our design.
Ku-band Waveguide Slot Filtering Antenna Based on Metallic-Nail Metamaterial

A waveguide slot filtering antenna is studied for Ku-band satellite communications in this paper. In order to improve the signal isolation and signal-to-noise ratio (SNR) of the received signal at 12.25-12.75 GHz, it is necessary to filter the transmitted signal at 14-14.5 GHz. Therefore, unlike traditional waveguide slot antennas, a metallic-nail metamaterial is used to embed at the bottom of the inside waveguide. Thus, a bandgap that cannot propagate electromagnetic waves is obtained. To validate the effectiveness of the antennas, numerical simulation results are presented. Sufficient radiation and significant radiation suppression are achieved at the receiving frequencies and transmitting frequencies, respectively. The studied 8-slot antenna has a gain suppression effect of 14.45-31.8 dB at the transmitting frequency.
Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.
Fast Impedance Measurement of Li-Ion Battery Using Discrete Random Binary Excitation and Wavelet Transform

Electrochemical impedance spectroscopy (EIS) is a widely used means for characterization of the dynamics of batteries and electrochemical energy conversion systems in general. EIS is useful for on-line condition monitoring since it contains valuable information on the internal condition of the batteries. The conventional approach to the EIS of batteries relies on their successive excitation with mono-component sinusoidal signals at the pre-defined frequencies. When inferring about the battery’s state-of-health, the low-frequency part of the impedance characteristic is of particular interest. Excitation in the low-frequency region can take an excessively long time. Moreover, maintaining stable experimental conditions over long time intervals i na way that the external disturbances will not affect the estimated impedance, might be demanding, especially in the in-field applications. To alleviate the said limitations, and to minimally intrude the process operation, in this paper we apply broadband electrochemical impedance spectroscopy based on a discrete random binary sequence for perturbation of the battery input. The impedance is evaluated by processing voltage and current signals with continuous Morse wavelet transform. The main contributions of the paper refer to (i) the accurate evaluation of the impedance spectra from $\mu $ Hz to kHz range with high-frequency resolution (more than 200 points per decade) and (ii) the evaluation of the uncertainty region of the impedance characteristic. The entire characterisation takes only a fraction of the time required by the classical sine-based electrochemical impedance spectroscopy. The algorithm is successfully demonstrated on a commercial Li-ion battery, which, together with the all datasets are available for download at https://repo.ijs. si/gnusev/supplementary_material. git.
Carbon Nanotube Through-Silicon Via: Modeling, Design and Applications

This paper provides a brief description of research on the topic of carbon nanotube filled through-silicon vias (CNT TSVs). The impact of CNT kinetic inductance on the electrical performance of CNT TSVs is considered by utilizing the concept of effective complex conductivity. By virtue of the complex conductivity, the electrical characteristics of the CNT TSVs are obtained and analyzed. The multi-bit CNT TSVs are investigated for transmitting differential signal and improving the power integrity. Further, the variation in CNT kinetic inductance, which cause significant performance degradation,
A Novel Filtering Patch Antenna with Wide Stopband and Improved Selectivity

This paper provides a novel filtering patch antenna with wide stopband and improved selectivity. The antenna consists of two half-wavelength step-impedance resonators (SIRs) and a rectangular patch. The SIRs have the same fundamental frequency, but different higher order frequencies, which could suppress the spurious response and achieve a wide stopband. To improve the selectivity, the cross-coupling theory is used in this paper. The two SIRs are both coupled to the radiating patch, acting like a CT bandpass filter. Such a structure introduces a transmission zero on the high side of the passband, which could highly improve the selectivity of the antenna.
Study of Flexible RF Front-end Module Based on Liquid Crystal Polymer Substrate

In this study, a miniature and high performance RF front-end module based on liquid crystal polymer (LCP) substrate is presented. The module consists of microstrip low pass filter (LPF) and low noise amplifier (LNA). The LPF with high rejection is directly fabricated on the LCP substrate, and LNA is mounted on the surface of LCP substrate cavity. The novelty of the proposed module is that it can remain well performance at different curvature. Good results have been achieved with noise figure less than 1dB and signal gain better than 18dB, which indicates that the module will have a great development prospect in the flexible circuit application.
An Array Antenna for 24GHz Automotive Radar Application

A transmitting array antenna for both long- and medium- range automotive radar application is proposed in this paper. The array antenna consists of six linear series-fed patch subarrays and a network based on substrate integrated waveguide (SIW). With adoption of the broadband self-compensating phase shifter, the antenna has a good performance in wide band. Simulation results show that impedance bandwidth is from 23.9GHz to 25.1GHz, the relative bandwidth with flat-shoulder pattern is 2%.
A New Method to Design Steerable First-Order Differential Beamformers

First-order differential microphone arrays (FODMAs), which combine a small-spacing uniform linear array and a first-order differential beamformer, have been used in a wide range of applications for sound and speech signal acquisition. However, traditional FODMAs are not steerable and their main lobe can only be at the endfire directions. To circumvent this problem, we propose in this letter a new method to design steerable FODMAs. We first divide the target beampattern into a sum of two sub-beampatterns, i.e., cardioid and dipole, where the summation is controlled by the steering angle. We then design two sub-beamformers, one is similar to the traditional approach and is used to achieve the cardioid sub-beampattern, while the other is designed to filter the squared observation signals and is used to approximate the dipole sub-beampattern. The overall beampattern resembles the target beampattern for any steering angle. Simulations and experiments are performed to justify the effectiveness of the developed method.
Vehicle Location in Edge Computing Enabling IoTs Based on Bistatic FDA-MIMO Radar

Edge computing has a wide range of applications in the Internet of Things (IoT), especially suitable for low latency, high bandwidth, and high reliability. Edge computing enabling IoT can locate the vehicles rapidly with the help of edge computing. The sensors of IoTs can construct the Frequency diverse array (FDA) radar system, which has been widely concerned by scholars in recent years. The monostatic FDA and multiple-input-multiple-output (FDA-MIMO) is a research hotspot in parameter estimation field, but the research based on bistatic FDA-MIMO radar is insufficient. In this paper, we propose a tensor-based target location method in bistatic FDA-MIMO radar, which implements joint direction of arrival (DOA), direction of departure (DOD) and range parameters estimation. First of all, subarrays with different transmission frequency increment are used to construct the transmitting array to overcome the coupling between DOD and range. Then the tensor-based third-order signal model is established, which saves the multidimensional structure characteristics of received signal. And the signal subspace of each subarray is estimated by high-order-singular value decomposition (HOSVD). Furthermore, the phase period ambiguity is eliminated by limiting the range of the target, and the method for DOA, DOD and range parameters matching is provided. Theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of the proposed method.
Dynamically Tunable of Terahertz Waves Based on Graphene Metasurface

A dynamically tunable metasurface based on graphene is proposed in this paper, which consists of split-ring resonator and graphene nanoribbon array. The simulated results show that not only the proposed structure could achieve the transmission with 87 %, but also the reflectivity of 25 % within a certain frequency range from 0.57 THz to 0.63 THz, meanwhile, which also can achieve 20° reflection phase modulation. By changing the chemical potential (Fermi energy) of the graphene strips, the amplitude and phase of terahertz wave can be dynamically controlled. This designed graphene metasurface structure will be applied to control the mode competition in the terahertz gyrotron through manipulating the reflection wave, which is one of the most important factor influencing the mode competition.