Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Wideband Waveguide Delay Lines to Fixed Beam Direction Antenna using Genetic Algorithm

This paper proposes a algorithm of design and modeling wideband waveguide delay lines using GA. As an example, a fixed beam direction of 20deg of planar array slot antenna based on the optimization delay lines is achieved in a bandwidth of 5%. At the same time, the VSWR of each port is less than 1.8. This method can be extended to various waveguide antennas. The proposed algorithm and antenna are well suited to low-profile communication systems.
Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain–Computer Interface

A brain–computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets—AMUSE and PASS2D—using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.
Stress Detection With Single PPG Sensor by Orchestrating Multiple Denoising and Peak-Detecting Methods

Stress is one of the major causes of diseases in modern society. Therefore, measuring and managing the degree of stress is crucial to maintain a healthy life. The goal of this paper is to improve stress-detection performance using precise signal processing based on photoplethysmogram (PPG) data. PPG signals can be collected through wearable devices, but are affected by many internal and external noises. To solve this problem, we propose a two-step denoising method, to filter the noise in terms of frequency and remove the remaining noise in terms of time. We also propose an ensemble-based multiple peak-detecting method to extract accurate features through refined signals. We used a typical public dataset, namely, wearable stress and affect detection dataset (WESAD) and measured the performance of the proposed PPG denoising and peak-detecting methods by lightweight multiple classifiers. By measuring the stress-detection performance using the proposed method, we demonstrate an improved result compared with the existing methods: accuracy is 96.50 and the F1 score is 93.36%. Our code is available at https://github.com/seongsilheo/stress_classification_with_PPG.
A swarm intelligence approach for regression analysis of surface plasmon resonance curves in Otto chips

The use of the Otto configuration, as an alternative to Kretschmann’s, in the constriction of Surface Plasmon Resonance (SPR) sensors is an underdeveloped research area. Recently, a version of an Otto based device, baptized as Otto chip, was manufactured. Regression analysis procedures can be used to help the chip characterization by adjusting parameters of the model reflectance curve. However, as in any classical regression procedure, the initial guess must be close enough to the final solution to avoid convergence to a local minimum. An alternative approach to the classical regression procedure is the use of computational techniques inspired on swarms. Swarm intelligence, as this area of computer science is known, has successfully been used by engineers in various optimization problems. One prominent algorithm is the Particle Swarm optimization (PSO) that stands out for its low computational cost, simplicity of implementation and high efficiency on finding global optimum solutions. This paper describes the use of PSO in regression analysis of experimental SPR curves. It was shown that the PSO technique yields better results when compared with classical regression analysis methods.
A Combined Simulation and Machine Learning Approach for Real-Time Delay Prediction for Waitlisted Neurosurgery Candidates

In this study, we present a method to predict whether a patient seeking admission to the neurosurgery ward of a large public tertiary care hospital in north India receives admission within a prespecified duration. The prediction needs to be made at the time the patient is seeking admission at the ward, so that they can then decide whether to wait for admission into the neurosurgery ward or seek care elsewhere. We accomplish this by simulating the admission and patient stay processes at the neurosurgery ward, and use the simulation to generate data to train machine learning algorithms to predict whether the patient is admitted as a function of the state of the simulation at the time the patient is seeking admission at the ward. With ensemble tree classifiers, we achieve generalization area under the curve scores of 95% for all patients taken together and between 80-95% depending upon patient subtype.
A Wideband 6-Bit Phase Shifter MMIC for C-Band Application

This paper presents a wideband C-band 6-bit digital phase shifter monolithic microwave integrated circuit. The phase shifter is fabricated with a 0.25-μm GaAs pHEMT process technology. The synthetic design of different phase bits is introduced to this phase shifter, which has taken the all-pass network and the high-pass/lowpass network into consideration. The phase shifter has achieved the root mean square (RMS) phase error of less than 3.7° in the frequency range of 4.4-6GHz, the insertion loss better than 5.5 dB, and the voltage standing wave ratio (VSWR) less than 2.43 at all phase-shifting state. The chip size is 2.7×1.3 mm2.
Optimization of Shaped Log-Periodic Antennas

Wire antennas have been widely used in the past in TV communication systems, and more recently have experienced a recrudescence in their popularity and R& D interest with the digitalization of television signals. Dipoles, Yagis and Log-Periodics are common examples of wire antennas and possess ideal lengths and geometries accordingly to the desired TV channels. In this work, a new shape for a Log-Periodic antenna is numerically obtained through angular optimization with the aid of computer codes. The new geometry yields a better response in a desired frequency bandwidth, when compared to the classical straight or V-shaped geometries.
A Compact Sequential Rotation Array Antenna for Satellite Applications

A compact sequential rotation array antenna for satellite applications is presented in this paper. The antenna consists of a main body with four radiation elements driven by a feeding network. The radiation elements are sequentially rotated planar inverted-F antenna (PIFAs) and four ports with equal magnitude and sequentially 90-degrees phase difference drive the main body. In particular, by bending the horizontal radiation plane, the structure is compact with a small size (ϕ0.21λ0 × 0.09λ0). Due to sequential rotation structure, the antenna pattern has good symmetrical property. The antenna proposed in this paper has a 3-dB axle ratio (AR) beamwidth of 185 degrees and a half-power beamwidth of 119 degrees.
Usability and Accessibility Assessment of Saudi Arabia Mobile E-Government Websites

The rapid spread of smart mobile technology is transforming the way how governments provide information and services to their citizens. We all are rely more on our devices, namely smartphones, laptops, or desktop to get information and a wide range of services from government websites. Such heavy usage of government websites results in an increased need for efficient and effective delivery of government services. Therefore, mobile government websites’ usability and accessibility are essential dimensions that determine the quality and accessibility of mobile e-government. The main objective of this research is to analyze the accessibility and usability aspects of selected public sector websites in Saudi Arabia. This study investigates how well the Saudi mobile e-government websites comply with usability standards and accessibility guidelines recommended in the Web Content Accessibility Guidelines (WCAG). Websites assessments were conducted using manual evaluation and complemented by different automated analysis tools. This study applies a number of evaluation techniques to assess Saudi government websites accessed from desktop or mobile devices, such as an automated website testing technique, mobile-friendliness testing, and content observation technique. Various tools have been used for site evaluation, such as GTmetrix (PageSpeed Score, YSlow Score), WAVE, Google mobile-friendly test, and Dareboost for mobile websites. The study uncovers shortcomings regarding non-compliance to international web standards recommendations. The findings revealed usability and accessibility problems that affect the performance of government websites. In order to improve these websites within these aspects, several recommendations were suggested for improving the usability and accessibility of websites in Saudi Arabia that will make sure different groups of citizens are satisfied with the website features and services provided by them.
Ordinal Optimization with Generalized Linear Model

Given a number of stochastic systems, we consider an ordinal optimization problem to find an optimal allocation of a finite sampling budget, which maximizes the likelihood of selecting the "best" system, where the "best" is defined as the one with the highest mean. The statistical characteristics of each system are described by the generalized linear model, where unknown parameters are estimated using maximum likelihood estimation. To formulate the problem in a tractable form, we use the large deviations theory to characterize the structural properties of the optimal allocation. Further, motivated by Euclidean information theory, we obtain an approximate solution for the optimal allocation, which is leveraged to design a sampling strategy that is near-optimal in a suitable asymptotic sense. The proposed sampling strategy is computationally tractable, and we show via numerical testing that it performs competitively even in the presence of model misspecification.
Adaptive Sensing Voltage Modulation Technique in Cross-Point OTS-PRAM

Phase-change random access memory with an ovonic threshold switch (OTS-PRAM) has become increasingly popular as an alternative to resolve the problem caused by the small capacity of dynamic random access memory and the high latency of NAND flash memory in computing systems. However, an OTS has a temperature-dependent OFF-current (<inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {off}}$ </tex-math></inline-formula>) and threshold voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {TH}}$ </tex-math></inline-formula>). This causes <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {off}}$ </tex-math></inline-formula> of a cell (<inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {off\_{}CELL}}$ </tex-math></inline-formula>) and <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {TH}}$ </tex-math></inline-formula> of a cell (<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {TH\_{}CELL}}$ </tex-math></inline-formula>) to become temperature-dependent, inducing a sensing error during read operations. In this article, an adaptive sensing voltage modulation (ASVM) technique is proposed that adaptively controls the bitline and wordline voltage depending on the change in temperature to compensate for the temperature-dependent variation in voltage drop caused by <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {off\_{}CELL}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {TH\_{}CELL}}$ </tex-math></inline-formula>. The HSPICE simulation results, with an industry-compatible 250-nm complementary metal–oxide–semiconductor process for the 20-nm PRAM technology, show that the OTS-PRAM with the proposed ASVM can achieve a bit error rate below 0.1 ppm within the operating temperature range of 0 °C–85 °C.
Transmission Loss Analysis of Grounded and Ungrounded SSPP Transmission Lines

In this paper, the transmission losses of grounded and undergrounded spoof surface plasmon polariton (SSPP) transmission lines (TLs) are investigated over a wide frequency range. To overcome the effects of transition structures of SSPP transmission lines, the multiline method is utilized for the de-embedding purpose. Attenuation constants of grounded SSPP (G-SSPP) lines, ungrounded SSPP (U-SSPP) lines and traditional microstrip (MS) lines in the same size are compared in detail. The impact of dielectric loss tangent on attenuation performance is also discussed.
A Collective Data Mining Approach to Predict Customer Behaviour

The customer churn is a demanding area of research in e-commerce. This introduces a number of challenges in discovering a more accurate prediction of the information. The classification algorithms are the supervised machine learning techniques. The various classification algorithm has its own advantages. In the proposed approach, the various classification approaches such as support vector machine, Bayesian Classifier and the Random Forest Algorithm are used to analyze the customer churn and a collective approach is applied to develop an effective model. The collective value from various classification methods makes the proposed method more effective. The accuracy metric, precision metric, recall metric and F-Score metric values are calculated to show the effectiveness of this approach. The feature selection method is implemented on the data. The method will lead the education of institutions to predict the churn status and help them to increase the customer.
Deep Deterministic Policy Gradient for Portfolio Management

Portfolio management is a financial problem that has been the subject of much research over the years. It is a planning task where an agent constantly redistributes resources across a set of assets in order to achieve investment objectives and thereby maximize return. However, it remains difficult to obtain an optimal strategy in an environment as complex and dynamic as the financial market. Our article focuses on solving this stochastic control problem in order to obtain an optimal strategy that would allow us to make profitable decisions by interacting directly with the environment. To do this, we explore the power of deep reinforcement learning which differs from traditional Machine Learning by combining the task of predicting stock behavior and analyzing the optimal course of action in a single unit, thus aligning the problem of Machine Learning with the investor's objectives. As a method, we propose to use the Deep Deterministic Policy Gradient which is an off-policy algorithm and is used for environments with continuous action spaces. The obtained results demonstrate that the model achieves a higher rate of return than the strategy of “Uniform Buy and Hold” stocks and the strategy of “Buy Best Stock in last month”.
Application of Association Rule:Apriori Algorithm in E-Commerce

Associative classification frameworks have been effectively used to build classification frameworks. The significant strength of such strategies is that they can utilize the most exact guidelines among a comprehensive rundown of class-affiliation rules. This clarifies their great exhibition as a rule, however to the inconvenience of a costly figuring cost, acquired from affiliation rules revelation calculations. We address this issue by proposing an appropriate procedure dependent on FP-development calculation. In a shared nothing design, subsets of arrangement rules are created in equal amounts from a few information segments. A between processor correspondence is set up to settle on worldwide choices. This trade is made distinctly in the main degree of recursion, permitting each machine to thus deal with all its allotted errands autonomously. The last classifier is worked by a lion’s share vote. This methodology is outlined by a definite model, and an examination of correspondence cost.
High-efficiency Elecmigration Analysis for RF Switch Using AMG

High-efficiency electromigration (EM) analysis is urgent needed with the rapid development of IC. Therefore, in this paper a RF switch is taken as an example to analysis the interconnect reliability using the method of automated model generation. It can greatly improve the efficiency of the simulation and shorten the time for modeling. Therefore, AMG modeling is an important contribution to do further IC reliability analysis.
Synchronous SFQ Address Encoder for Superconductor Detector Arrays

With the advances in superconductor detectors and single flux quantum (SFQ) logic circuits, megapixel imagers have become possible. Various detectors such as superconducting stripline detectors (SSLD), kinetic inductance detectors, nanowire single-photon detectors have quite matured in terms of speed and performance. However, scaling of the read-out electronics is still a challenge. We propose an SFQ based synchronous address encoder to read-out the response of an SSLD array. The encoder circuit provides two sets of outputs at each clock period: one is the time-division multiplexed, serial data output, and the other is the synchronized address output associated with the time-multiplexed data. Thus, the number of connections to the room temperature is substantially reduced. We experimentally demonstrate a 2-bit encoder. Then, we present the scalability of the circuit to larger arrays and show the simulation results of a 4-bit encoder, which is suitable to read-out a 16 × 16 detector array.
Internet of Things (IoTs)- Review and It’s Multiple Classification

With the increasing advancement in technology in the world, Internet of Things (IoT) is implements a huge number of applications according to the need of the era. IoT is the process to obtain knowledge about something and apply it independently. Different IoT concepts as smart city, smart agriculture, smart bin, smart parking, etc. are present everywhere. Not only use of IoT with different domains is seen but it is also increasing some security issues. This article also presents the review of the current scenario of IoT as well as applications regarding making IoT cost-effective, more efficient, and feasible to implement. Nowadays antenna is a very important aspect in terms of communication. Their small size and high efficiency have made the implementation of IoT more feasible. Every electronics have embedded systems and IoT with embedded helped researchers to set a new level of IoT. IoT concerns changing applications and needs in the current scenario. This article also reviews AUTOCON-IoT, and Node-MCU which consists of an exhaustive review of the security of IoT discussing challenges and proposed solutions to it.
3-D Trajectory Optimization for Fixed-Wing UAV-Enabled Wireless Network

Unmanned aerial vehicles (UAVs) is a promising technology for the next-generation communication systems. In this article, a fixed-wing UAV is considered to enhance the connectivity for far-users at the coverage region of an overcrowded base station (BS). In particular, a three dimensions (3D) UAV trajectory is optimized to improve the overall energy efficiency of the communication system by considering the system throughput and the UAV’s energy consumption for a given finite time horizon. The solutions for the proposed optimization problem are derived by applying Lagrangian optimization and using an algorithm based on successive convex iteration techniques. Numerical results demonstrate that by optimizing the UAV’s trajectory in the 3D space, the proposed system design achieves significantly higher energy efficiency with the gain reaching up to <inline-formula> <tex-math notation="LaTeX">$20\,\,bitsJ^{-1}$ </tex-math></inline-formula> compared to the <inline-formula> <tex-math notation="LaTeX">$14\,\,bitsJ^{-1}$ </tex-math></inline-formula> maximum gain achieved by the 2D space trajectory. Further, results reveal that the proposed algorithm converge earlier in 3D space trajectory compare to the 2D space trajectory.
Reconfiguration Aware Orchestration for Network Function Virtualization With Time-Varied Workload in Virtualized Datacenters

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.
Internal Sensor Based Kinematic Parameters Estimation using Acceleration/Deceleration Motion

Motion measurement systems play an important role in a wide range of fields such as robot motion control and human motion analysis. Motion measurement methods using a camera, which is an external sensor, have problems such as low sampling rate and limited measurement range. On the other hand, the method using the encoder or inertial sensor, which is an internal sensor, has almost no limitation on the measurement range. Moreover, it can be measured at a high sampling rate. However, when using the internal sensor, it was necessary to use the kinematic model and kinematic parameters of robots and humans. Errors in these parameters lead to reduced accuracy in kinematic calculations. Therefore, the control performance and analysis accuracy are reduced. To solve these problems, we propose a method for estimating kinematic parameters using the inertial sensor. The proposed method uses a kinematic relational expression in the acceleration dimension. Therefore, kinematic parameters can be estimated without using absolute position information. In this paper, the proposed method is applied to the 3-link manipulator and the human body. The effectiveness of the proposed method is evaluated by comparing the estimated link length with the measured value.