Data Selector Research Articles

Observer‐based finite‐time security fault‐tolerant control for nonlinear multi‐agent systems under byzantine adversaries

AbstractThis article investigates the issue of security‐based adaptive output feedback finite‐time fault‐tolerant control (FTC) for nonlinear multi‐agent systems subjected to Byzantine attacks. In this work, the fuzzy logic systems are employed to approximate the unknown nonlinearities, and unmeasurable states are estimated by the designed state observer. In addition, a data selector is designed to detect extremely malicious data of the controlled system. Considering the actuator faults, a novel Nussbaum function is utilized to compensate for the uncertainties of attacks and intermittent failures. Then, by introducing first‐order filter, an observer‐based distributed adaptive output feedback finite‐time security FTC algorithm is developed, which demonstrates all signals of the system are bounded, and the feasibility and effectiveness of the developed control scheme can be verified by simulation results.

Ternary combinational logic gate design based on tri-valued memristors

Traditional binary combinational logic circuits are generally obtained by cascading multiple basic logic gate circuits, using more components and complicated wiring. In contrast to the binary logic circuit design in this method, ternary combinational logic circuit implementation is more complicated. In this paper, a ternary circuit design method that does not require cascading basic ternary logic gates is proposed based on a tri-valued memristor, which can directly realize specific logic functions through a series connection of memristors. The ternary encoder, ternary decoder, ternary comparator, and ternary data selector are implemented by this method, and the effectiveness of the circuits is verified by LTspice simulations.

Event-Triggered Tracking Control of Nonlinear Systems Under Sparse Attacks and its Application to Rigid Aircraft

This study is aimed at addressing the event-based state estimation and tracking control problems for a class of nonlinear systems, the output of which is measured by multiple sensors, but the adversary can manipulate nearly half of the measurements simultaneously. First, a sampled-data-based event-triggered strategy is developed to reduce unnecessary data transmissions under sparse sensor attacks, and the transmitted data are filtered by a data selector to obtain reliable data. Subsequently, an output-prediction-based continuous-discrete observer is improved so that it can estimate continuous-time system states from the event-triggered output, rather than being limited to time-triggered sampled output. Further, to design a tracking controller with the segmentally differentiable estimated states, a backstepping method incorporating tracking differentiators is proposed. Finally, the effectiveness of the proposed method is demonstrated by applying it in the simulation of a rigid aircraft.

A glitch energy reduction method with low complexity dynamic element matching for data converters

Due to the appearance of huge glitch while using traditional Dynamic Element Matching (DEM) in data converters, different kinds of glitch-reduce structures are designed into DEM. However, most of these structures will work with high complexity. Based on random-rotation-binary-weighted selection (RRBS), this paper presents a novel low complexity glitch-reduced DEM algorithm, which can greatly reduce mismatch. The proposed DEM constructs glitch-reduce structure through data selector and comparator, so the complexity is reduced through using less components. As a result, the glitch-reduced DEM increases spurious free dynamic range (SFDR) to 92.47dB at 7.6MHz. Moreover, compared with conventional RRBS, glitch-reduced DEM makes the glitch reduce by 49% with low complexity in a 12-bit digital-to-analog converter (DAC).

Profit-based deep architecture with integration of reinforced data selector to enhance trend-following strategy

Profit-based deep architecture with integration of reinforced data selector to enhance trend-following strategy

Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning

Federated Learning (FL) is a machine learning paradigm in which multiple clients participate to collectively learn a global machine learning model at the central server. It is plausible that not all the data owned by each client is relevant to the server's learning objective. The updates incorporated from irrelevant data could be detrimental to the global model. The task of selecting relevant data is explored in traditional machine learning settings where the assumption is that all the data is available in one place. In FL settings, the data is distributed across multiple clients and the server can't introspect it. This precludes the application of traditional solutions to selecting relevant data here. In this paper, we propose an approach called Federated Learning with Relevant Data (FLRD), that facilitates clients to derive updates using relevant data. Each client learns a model called Relevant Data Selector (RDS) that is private to itself to do the selection. This in turn helps in building an effective global model. We perform experiments with multiple real-world datasets to demonstrate the efficacy of our solution. The results show (a) the capability of FLRD to identify relevant data samples at each client locally and (b) the superiority of the global model learned by FLRD over other baseline algorithms.

Saturated Threshold Event-Triggered Control for Multiagent Systems Under Sensor Attacks and Its Application to UAVs

This paper investigates the secure consensus tracking problem for continuous-time nonlinear multiagent systems with sensor attacks. By designing a secure data selector, the unattacked output data is extracted from a group of output measurements under sparse sensor attacks. Then, by virtue of the obtained data and neural networks, a state observer is constructed to estimate the unavailable system states, where the convex combination theory is introduced to reduce the difficulty of solving observation gains. To utilize the limited communication resources reasonably, a novel saturated threshold event-triggered control strategy is proposed to reduce control updates, and then each update is encoded into a binary signal (0 or 1) to further reduce the occupation of communication bandwidth. The designed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded, and its effectiveness is verified via a simulation of attitude control of unmanned aerial vehicles.

Selective transfer learning with adversarial training for stock movement prediction

Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield satisfactory prediction result. This paper contributes a new stock movement prediction model, Selective Transfer Learning with Adversarial Training (STLAT). Our STLAT method advances existing solutions in two major aspects: (i) tailoring the pre-trained and fine-tuned method for stock movement prediction and (ii) introducing the data selector module to select the more relevant training samples. More specifically, we pre-train the shared base model using three different tasks. The predictor task is constructed to measure the performance of the shared base model with source domain data and target domain data. The adversarial training task is constructed to improve the generalisation of the shared base model. The data selector task is introduced to select the most relevant and high-quality training samples from stocks in source domain. All three tasks are jointly trained with a loss function. As a result, the pre-trained shared base model can be fine-tuned with the stock data in target domain. To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset. Extensive experiments demonstrate the superiority of our STLAT method. It outperforms state-of-the-art stock prediction solutions on ACC evaluation of 3.76%, 4.12%, 4.89% on ACL18, KDD17 and CN50, respectively.

Adaptive Detection in Partially Homogeneous Environment With Limited Samples Based on Geometric Barycenters

To solve the problem of adaptive detection in partially homogeneous environment with outliers and limited samples, a class of two-step detectors are designed based on geometric barycenters. The first step is to construct a data selector based on generalized inner product and eliminate sample data containing outliers. The second step is to construct detection statistics of the adaptive coherence estimator using covariance matrix estimators, which are based on geometric barycenters. The detectors utilize geometric barycenters of the positive definite matrix space without any knowledge of prior probability distribution of sample data. The performance of the proposed two-step detectors is evaluated in terms of the probabilities of correct outliers excision, false alarm, and detection. Experiment results, based on simulated and real data, show that the proposed approach has better detection performance than the existing ones based on traditional covariance estimator.

Augmented Data Selector to Initiate Text-Based CAPTCHA Attack

In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.

Applications of Multiplexers and Demultiplexers in Network Communication

The aim of this research paper is to provide a general overview of Mux and Demux. This research paper starts with a brief description of these electronic devices. A multiplexer, also known as a data selector, is a system that can merge two or more data lines into a single data line for transmission to another venue. A demultiplexer, also known as a data distributor, is a device that divides a single, multiplexed data transmission line into individual data signals. This article addresses the 1 to 4 multiplexer and demultiplexer. Various applications of mux and demux in the fields of telephone and satellite communication, as well as uses of computer science, are discussed.

Low Power Analog Multiplexers for ECG Applications

This paper demonstrates the design of the 8 channel and 16 channel biomedical data selector with passtransistor switches. Pass transistor switches are designed with NMOS switch logic in 45nm technology. Data selectors are the important input circuitry to instrumentation amplifier in the Electrocardiogram (ECG). Generally, Inputs for ECG are very low voltages in terms of millivolts and frequency also in HZs. The proposed designs are useful in 3 lead 6 lead and 12 lead ECGs. The power dissipation is 11.055 pW at 1HZ and its calculated for supply voltages of 0.5 V and frequency analysis are done in between 1HZ to 10 KHZ.

Implementation of Web Scraping on News Sites Using the Supervised Learning Method

Indonesia is one of the highest internet users in the world, including in the penetration of information on the internet, online news media. But in general news sites not only display news information, but most sites also display other information such as advertisements and also forms of navigation that interfere with news site readers and interfere with reader’s comfort, from these problems this study aims to implement web scraping techniques with supervised learning methods and analyzing the form of DOM tree and XPath news sites. The supervised learning approach method is the method used in this study, which is one of the methods of machine learning. By combining these web scraping techniques with supervised learning, the aim is to be able to implement and optimize web scraping techniques to gather news information from various sites. To do basic web scraping namely knowing DOM patterns, XPath structure as a data model or selector at each site. The results of research in the form of a web scrap application that can retrieve news site content without copy paste and the data is stored in a database and displayed to the user application form for the reader without any ads and navigation that disturb the reader.

Router design for nano-communication using actin quantum cellular automata.

Logic expressions can be designed from actin filaments. It is a protein that makes the cellular structure and plays an important role in intracellular communication. Nano communication technique has been established using actin cellular automata. Among several rules, (1, 30) and (4, 27) rules have been used to design 2 to 1 multiplexer, 4 to 1 multiplexer, 1 to 2 demultiplexer and 1 to 4 demultiplexer. Router or data selector has been made of using multiplexer and demultiplexer. Three novel circuits such as multiplexer, demultiplexer and nano-router have been designed using the projected mechanism. The primary focus of this proposed technique is on different designs of the multiplexer, demultiplexer and minimum cell count with minimum time steps. The different router circuits have been simulated with the help of Simulink by which output has been verified for different circuits. Stuck at fault analysis is also done in this study. Device density and power consumption have also been included in this study. A comparative analysis of the different designs of the router provides a better concept of circuit optimisation. Furthermore, this study analyses convenient forthcoming applications in nano-technology and nano-bio-molecular systems involving the proposed parameters.

DESIGN OF GPS AND COMPASS HELMET VISUAL DATA USING MICROCONTROLLER FOR MARINE FORCE OF INDONESIAN NAVY

The Marines are a component of the combat forces owned by the Navy. In carrying out their duties as infiltration forces and landing forces, sophisticated equipment is needed to support each movement in terms of knowing the position of coordinates in the form of latitude longitude and compass direction to facilitate the movement of troops in carrying out operational tasks. For this reason, a tool was made to visually know the position of the latitude and longitude of the compass, making it easier for troops to carry out their duties both on the training ground and on the battlefield. This tool is in the form of a Design Combat Helmet for Visual Data Viewer GPS and Microcontroller-based Compass for the Navy Navy. This tool uses Arduino Mega Pro Mini 2560 as a microcontroller, V.KEL 2828U7G5LF as a GPS module, HMC5883L as a Compass module and Wemos Oled LCD 64x48 0.66 "as an analog data viewer. This tool has a working principle like Google Glass that can display data visually. How it works in order to display data visually is to use the reflection of a small mirror that will be enlarged using a convex lens or loop lens and then transmitted the reflection to a visual viewer that is placed in front of the eye. In order to get data in the form of latitude longitude and compass direction alternately a push button is used which is designed as a data selector switch or counter. In testing, this tool proved to be accurate in taking GPS data and compass direction after compared with equipment that has the same function. For the V. KEL2828U7G5LF GPS Module compared to FURUNO GPS and Google Map, while for the HMC5883L Compass module compared to the Android Compass Application.
 
 Keywords: GPS data, Compass, Visual Combat Helmet

Design of Low Power & Area Efficient of 8-Bit Comparator using GDI Technique

In this paper we are design a circuit based on data selector and distributor networks in which we will not realize the circuit based upon the expressions but off course the circuit which have designed will have internally some expression. In the recent trends the need for low power and less on-chip area is on high note for the portable devices. In this project we want to focus on the design constraints of VLSI. Innovative design of 8-Bit GDI based Comparator will be proposed and implemented. Optimization depends on selection of GDI Cell as well as selection of primary inputs to the terminals of GDI cell. 8-Bit GDI based Comparator will be designed and simulated using Tanner EDATool. Comparator has three main outputs where it can compare the weight of two words and generates three functions. GDI has the advantage of low power consumption because the total number of logic devices needed willbe less and it can also operate with high speed due to affective realization of logic using minimal hardware. Comparator circuits is designed using tanner tools and also observe the simulation results in H-SPICE attaining low power and less delay.

High Performance SAR ADC with Mismatch Correction Latch and Improved Comparator Clock

We propose a high performance 10-bit 100-MS/s (million samples per second) successive approximation register (SAR) analog-to-digital converter (ADC) with mismatch correction latch and improved comparator clock. Using a high-low supply voltage technology, the bias output impedance of the preamplifier of the comparator is increased. Therefore, the common mode rejection ratio (CMRR) of the comparator is improved, and further diminishing the signal-dependent offset caused by the input common-mode voltage variation. A digital-to-analog converter (DAC) control signal correction latch is proposed to correct the control signal error resulted from process mismatch. The 30-point Monte Carlo mismatch simulated results demonstrate that the minimum spurious-free dynamic range (SFDR) of the ADC is improved by 2 dB with this correction latch. To ensure sufficient high bit switching time of the DAC and sufficient low bit comparison time of the comparator, a data selector used in the comparator clock is presented. The optimized time distribution improves the performance of the SAR ADC. This prototype was fabricated using a one-poly-eight-metal (1P8M) 55 nm complementary metal oxide semiconductor (CMOS) technology. With measured results at 1.3 V/1.5 V supply and 100-MS/s, the ADC achieves a signal-to-noise and distortion ratio (SNDR) of 59.4 dB and consumes 2.1 mW, resulting in a figure of merit (FOM) of 31 fJ/conversion-step. In addition, the active area of the ADC is 0.018 8 mm2.

A Geometric Barycenter-Based Clutter Suppression Method for Ship Detection in HF Mixed-Mode Surface Wave Radar

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system.

基于PPLN波导的全光数据选择器的设计与研究

基于PPLN波导的全光数据选择器的设计与研究

Non‐parametric detector in non‐homogeneous clutter environments with knowledge‐aided permutation test

In this study, a new kind of knowledge-aided (KA) permutation test (PT) detector is proposed for the problem of performance deterioration of the PT in complex non-homogeneous clutter environments. The proposed detector consists of two parts, a data selector and a conventional PT. The data selector uses the geographic information system (GIS) to select the reference cells which have similar clutter characteristics with the cell under test. Samples from the reference cells selected by the data selector are used to determine the detection threshold of the PT. Because the PT is a non-parametric method, the proposed detector can maintain a constant false alarm rate without knowing the clutter's distribution type, which is very important in complex clutter environments. Considering the computation complexity of the PT, the authors also provide an implementation algorithm for the PT called ‘valid strategy method’, which is more efficient than the direct calculation method. In order to prove the excellent performance of the KA-PT detector, this detector is compared with some conventional detectors in terms of the false-alarm number and detection probability with the real data collected by IPIX radar. The results show that incorporating the non-parametric method with the prior GIS can significantly improve detectors’ performance in practice.