Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Log-Based Anomaly Detection with Component-Aware Analysis

Logs are universally available in software systems for troubleshooting. They record system run-time states and messages of system activities. Log analysis is an effective way to diagnosis system exceptions, but it will take a long time for engineers to locate anomalies accurately through logs. Many automatic approaches have been proposed for log-based anomaly detection. However, most of the prior approaches did not consider the corresponding system component of a log message. Such component records the log location, which can help detect the location-sequence-related anomalies. In this paper, we propose LogC, a new Log -based anomaly detection approach with Component-aware analysis. LogC contains two phases: (i) turning log messages into log template sequences and component sequences, (ii) feeding such two sequences to train a combined LSTM model for detecting anomalous logs. LogC only needs normal log sequences to train the combined model. We evaluate LogC on two open-source log datasets: HDFS and ThunderBird. Experimental results show that LogC overall outperforms three baselines (i.e., PCA, IM, and DeepLog) in terms of three metrics (precision, recall, and F-measure).
Selected methods of parametrization in problem of automatic classification classical music from the Renaissance era against the classical works from other eras

In this article we present the results of our work in the field of automatic classification of classical music pieces. The studied works were compositions of classical music composed in four eras: Renaissance, Baroque, Classicism and Romanticism. In the work we described selected methods of parameterization of music files, so that they emphasize the characteristic of Renaissance. The parameters we use are of a horizontal nature, i.e. they do not penetrate the vertical structure of the piece (e.g. chords progression). We used a base of 571 works of classical music, both secular and religious. The files were stored in MusicXML format and contained 187 Renaissance pieces, 146 Baroque, 119 classics and 119 stylistically belonging to the Romantic era, respectively. The results of the studies were presented using 4, 13 and 113 parameters. An artificial neural network and Support Vector Machine were used to classify the era to which the song belongs.
A Novel Method for Leaf Recognition Based on D-LLE and Polar Coordinate Feature Extraction

By extracting low-level features under the rectangular coordinate system, traditional leaf recognition methods typically have properties such as high dimensionality of extracted features, high-computational requirement and weak generalization performance. Based on the manifold learning algorithm D-LLE, we proposed a novel leaf recognition method under the polar coordinate system. The method first extracts from the leaf images high-dimensional features associated with polar coordinate as the preprocessing. Consequently, D-LLE is harnessed to reduce the features' dimensionality. In the low-dimensional space, we use the nearest neighbor classifier to make final determination. Experimental results exhibit higher effectiveness and efficiency of our method compared with classical traditional methods.
Utilization of Innovative Materials toward Permanent Magnet Synchronous E-Motors for Traction Application: A Review

This paper examines recent optimization attempts of Permanent Magnet Synchronous Machine (PMSM) design involving the utilization of innovative materials. Recent attempts at material based enhancements to PMSM design are divided into several sections based on key machine components. In the area of core design, the replacement of electromagnetic steel with new material is highlighted. With regard to winding design, several materials have been examined as replacement for the conductive copper used as the traditional winding material. The use of enhanced materials for next generation magnets is also investigated in this paper. Additionally, the utilization of novel materials for the weight intensive PMSM motor housing, cooling channels, and rotor shaft are investigated. Lastly, the utilization of enhanced materials for PMSM insulation of enhanced thermal performance is examined. Overall, this paper will highlight the strong potential of advanced materials to greatly enhance PMSM design and their viability for use in traction applications.
Optimization of PID and FOPID controllers with new generation metaheuristic algorithms for controlling AVR system: Concise Survey

Automatic Voltage Regulator (AVR) system is one of the major devices broadly used in many industrial applications for regulating the voltage of the synchronous generator within its nominal values. Consequently, providing a suitable controller for the AVR system becomes a necessity to prevent instability and error in the system’s output response. Studies from past works have shown that an adequately tuned PID controller will maximize the efficiency of the AVR system. In recent decades metaheuristic algorithms have become increasingly prevalent due to their tremendous success in addressing real-world optimization problems. This paper, therefore, presents a concise survey of the optimization of the PID and FOPID controllers with new generation metaheuristic algorithms for controlling the AVR system. A short description of each algorithm is presented with papers published in various reputable journals. Finally, the paper presents some future directions for research.
Wearable Beam Steering Antenna for Body-Centric Communication

A low profile reconfigurable wearable antenna operating in ISM band (5.725-5.875 GHz) is presented for body area network. Two PIN diodes are used as RF switches for achieving beam steering in -17°, -12°, +21° and +29° directions in azimuth plane. The peak directive gain was 8.76 dBi for different configurations when the beam was steered through a steerable range. The EVA foam substrate is used to provide flexibility and mechanical stability in the structure. The specific absorption rate (SAR) value of 0.97 W/Kg for 1 g of human tissue is satisfying the SAR criteria.
A high-performance low-power two-stage OPAMP realized in 90nm CMOS process for biomedical application

The functionality of modern system on chip (SOC) technology is realized by integrating both digital and analog components. To realize complex analog circuitry, designers need digital-to-analog converter (DAC), analog-to-digital converter (ADC), filters, bias voltage generator, switched-capacitor circuit etc. where optimization of power, performance, area, noise etc. are the design target. A two-stage OPAMP is the core component of many high-performance analog system. The target performance parameters of an OPAMP are power, gain, phase margin, CMRR, offset voltage, SQNR, slew rate, GBW and so on. In this paper, we discuss about our designed OPAMP for bio-medical applications, which is implemented in 90nm CMOS process and supplied by 1.5 Volt. The CADENCE spectra simulation results show that the circuit dissipates 0.21mW power with 71.3dB gain, 66.3° phase margin, 45.2dB SNR, 103dB CMRR, and 50ns delay. The circuit has the unity GBW of 3.3 GHz and the slew rate of 34.7V/µs.
Bandwidth Control Mechanism and Extreme Gradient Boosting Algorithm for Protecting Software-Defined Networks Against DDoS Attacks

Software-Defined Networking (SDN) is an emerging network architecture that addresses the limitation of the traditional network by providing centralized management through a central controller that decouples the control and data planes. However, this development has made the controller a severe target for malicious users to execute attacks such as Distributed Denial of Service (DDoS) attacks. Several schemes have been proposed to mitigate DDoS attacks in SDN, but the challenges still exist. This paper proposes a DDoS mitigation scheme for SDN to ensure accurate attack detection and efficient network resource utilization. The scheme employs two stages: a bandwidth control mechanism and Extreme Gradient Boosting (XGBoost) Algorithm. The bandwidth control mechanism utilizes an adaptive bandwidth profile-based threshold and bandwidth control algorithm that trigger the XGBoost algorithm in case of threshold violations. The use of multiple bandwidth profiles in stetting the threshold ensures the threshold’s adaptivity to consider the network traffic variation and reduce the packets drop ratio, which shows an outstanding result. The XGBoost algorithm classifies network traffic flow that violates a set threshold into normal or abnormal traffic. We evaluated the performance of our scheme using CICDDoS2019, NSL-KDD, and CAIDA datasets. Furthermore, we validated our proposed solution in real-time with the SDN environment. The results obtained show that our scheme protects SDN against DDoS attacks with high accuracy, low error, and efficient utilization of the network resources. The proposed system achieved 99.9% accuracy in detecting DDoS attacks with a low false-positive rate of 0.0002% in SDN.
Estimation of Pain from Normalized Energy of IMFs of EMG Signal

Pain is one of the most complicated and subjective sensation. As of now, doctors need to verbally communicate with the subjects for assessment of pain. In many cases, proper assessment of pain is not possible, especially for infants, deaf and dumb subjects, unconscious subjects etc. It is reported that pain stimulus excites the sympathetic nervous system (SNS) that changes the activity of few bio-signals that are directly related to SNS activities and the facial expression as well. In this present work, EMG of zygomaticus muscle which is related to change in facial expression is analyzed. Considering non-stationary nature of the EMG signal stimulated through pain, Empirical Mode Decomposition method is applied on EMG for its data adaptive nature. Standard Logistic Regression classifier is used on selected IMFs to classify between ‘no pain’ and different pain condition. Gradual increase in classification accuracy is noticed with the increase of pain intensity.
A Synthetic Aperture Based Method for Reflector Positioning via Moving Tag in UHF RFID System

As a general rule, the multipath propagation will reduce the reliability of radio frequency identification (RFID) based localization technologies. In this paper, we investigate the reflection of electromagnetic waves and the polarization characteristics of commonly used reader antennas and tag antennas, and model the multipath superposition of signals, so that the multipath effect can be calculated using known environmental information. Based on the multipath superposition model, a novel reflector positioning algorithm is proposed, which can estimate the orientation, length and location of a planar reflector. The proposed algorithm leverages the synthetic aperture radar (SAR) RFID localization idea, that is, from the relative movement of tag and reader antenna, we can obtain more correlated information to enhance the localization reliability. The performance of the proposed reflector localization algorithm is evaluated by simulations.
An Overview of Network Architectures and Technology for Wearable Sensor-based Health Monitoring Systems

As the population ageing is one of the most significant trends of the 21st century, the traditional methods for monitoring and diagnosis of elderly people are no longer sufficient for providing them with a satisfactory quality of life. One of the promising approaches that helps towards addressing such challenges is the employment of communication and information-based systems, allowing automated remote health monitoring and data analysis. The evolutionary transition to digital healthcare requires the definition of new concepts, architectural models and functionalities, subject of study by the scientific community and technology standardization bodies. In the present paper, we overview a wide range of architectures and technological solutions that were recently proposed for the needs of health monitoring, with focus on systems that are based on communication connectivity. We provide a comprehensive analysis and some critical comments on the potential technological shortcomings and challenges of these solutions.
High-Performance Metal-Ferroeletric-Semiconductor Nanosheet Line Tunneling Field Effect Transistors with Strained SiGe

Nanosheet line tunnel-field effect transistors (NLTFETs) are for the first time proposed by utilizing the advantages of ferroelectricity through HZO materials. Three ferroelectric line TFETs have been proposed and investigated. Among these, the metal-ferroelectric-semiconductor (MFS) structure has shown superior performance than the other two variants. The factors of electric field and electron barrier tunneling have been addressed to govern the performance of these structures. In addition, the effects of the ferroelectric (Hf0.5 Zr0.5 O2) thickness (tFE) and the dielectric constant have been discussed. The MFS NLTFETs can effectively utilize the advantages of ferroelectric than the other variants. High on-current of 175.6 $\mu \mathrm{A}/\mu \mathrm{m}$ and low off-current of 38.4 aA/$\mu \mathrm{m}$ are achieved at tFE of 4 nm through proper utilization of gate-overlap on to the drain side. Furthermore, the proposed MFS structure successfully delivers low average and minimum subthreshold swings even at very thin tFE.
One Class Support Vector Machine with a New Feature Selection Method for Fault Detection of Gas Turbine Generators in Thermal Power Plants

Abstract: This paper proposes OCSVM with a new feature selection method for fault detection of GTGs in thermal power plants. There are three requirements for practical systems of fault detection of GTGs in thermal power plants. Firstly, it is necessary to detect fault using only normal data. Secondly, it is necessary to consider non-linear correlation of GTG data. Thirdly, limited features should be selected with only normal data considering development cost of the system. Using actual GTG data in thermal plant, effectiveness of the proposed method from the view point of calculation time and fault detection accuracy is verified. Especially for fault detection accuracy, OCSVM with the proposed feature selection method is compared with OCSVM with all features and OCSVM with the basic time-consuming feature selection method.
Uninterrupted Connectivity Using Conditional Time Split Energy Extraction With Beamforming System for Disaster Affected Wireless Networks

The use of MIMO networks in wireless communication results in increased data rate but leads to severe interference issues and power losses. Promising beamforming (BF) architecture with Conditional Time split-Energy Extraction (CT-EE) is proposed to maximize the Energy Efficiency while optimizing the Mean Square Error (MSE) and Achievable Sum Rate (ASR). A distributed BF system using Minimum Mean Square Error algorithm is jointly implemented at all cooperating nodes. Energy Extraction (EE) from RF signal at the relay nodes facilitates full connectivity among the users. The signal transmission and EE phases are jointly implemented using time-split architecture, without using fixed pre-assigned time slots. Time split from information decoding to EE phase is done only if the battery life is found to be critical. The paper investigates the scope of bypassing the EE phase after ensuring the required power level. The performance of the proposed architecture is compared with the conventional BF and existing EE methods in terms of MSE, ASR and energy efficiency. The proposed architecture is well suitable to establish uninterrupted connectivity between user nodes which are frequently used and have severe power drain-off issues as they can be a part of natural disaster like flood affected wireless networks.
Landmark Augmentation for Mobile Robot Localization Safety

As more robots, such as autonomous vehicles, are deployed in life-critical situations it is imperative to consider safety, and in particular, localization safety. While it would be ideal to guarantee localization safety without having to modify the environment, this is not always possible and one may have to add landmarks or active beacons for landmark-based localization. As such, this work introduces a method to identify the minimum places in an environment where landmarks can be added to ensure localization safety, as quantified using integrity risk, the probability of undetected sensor errors causing localization failure while accounting for measurement faults. The letter formulates the problem as a systematic minimization: given the robot's trajectory and the current landmark map, add the minimum number of new landmarks such that the integrity risk along the trajectory is below a given safety threshold. The letter proposes three algorithms: a naive approach, Integrity-based Landmark Generator (I-LaG), and Fast I-LaG. The computationally expensive naive algorithm serves as a reference to illustrate simple scenarios. I-LaG adds relatively fewer landmarks than the Fast I-Lag algorithm but is more computationally expensive. Simulation and experimental results validate the proposed algorithms.
A Skeleton-based Community Detection Algorithm for Directed Networks

Directionality is a key feature that represents information, energy or influence transmitting between connected objects in complex systems, community detection with consideration of such feature is a prior tool to investigate the real-word networks, in this paper we propose a novel skeleton-based community detection algorithm for directed networks. Firstly we extend the term of the edge intensity for undirected graphs to directed ones, then the skeleton chain is extracted out as a profile of the original directed network; with iterative splitting of network skeleton and the extended intensity-based modularity, disjoint communities for directed networks can be accurately retrieved. Experimental results on real and synthetic networks show higher accuracy of our algorithm than the existing methods. In addition to helping detect communities, network skeleton provides a different view with a whole new meaning into network research and can be used into many applications.
Wireless Link Reliability in Cyber Physical System with Internet of Things

Link quality is routing criteria in cyber physical systems using the internet of things. Link quality is a metric for routing protocols. Link quality measurement is classified as software or hardware parameters. This paper investigates the impact of the hardware and software parameters on the routing performance. The paper considers wireless channel signal strength and link quality as a hardware parameter. Moreover, the paper considers the expected transmission count used by the RPL protocol as a software and routing parameter. The evaluation was performed by a simulator commonly used for simulating the internet of things called COOJA. The numerical results show that the routing performance and link quality are divided into three grade zones as a performance metric based on the packet delivery ratio. These zones are white, gray, and black zone.
Experiments with Interactive Fault Localization Using Simulated and Real Users

Fault localization is considered a difficult and time consuming activity. However, tool support for automated fault localization is still limited because state-of-the-art algorithms often fail to provide efficient help to the user. They usually offer a ranked list of suspicious code elements, but the fault is not guaranteed to be found among the highest ranks. In Spectrum-Based Fault Localization (SBFL) – which uses code coverage information of test cases and their execution outcomes to calculate the ranks –, the developer has to investigate several locations before finding the faulty code element. Yet, all the knowledge she a priori has or acquires during this process is not reused by the SBFL tool. We propose an approach in which the developer interacts with the SBFL algorithm by giving feedback on the elements of the prioritized list. We exploit contextual knowledge of the user about the next item in the ranked list (e. g., a statement), with which larger code entities (e. g., a whole function) can be repositioned in their suspiciousness. First, we evaluated the approach using simulated users incorporating two types of imperfections, their knowledge and confidence levels. On SIR and Defects4J, results showed notable improvements in fault localization efficiency, even with strong user imperfections. We then empirically evaluated the effectiveness of the approach with real users, which also showed promising results.
Re-Configurable Graphene-Based Two Port Dual-band and MIMO Antenna for THz Applications

This paper presents a flexible graphene-based antenna for terahertz applications. A single antenna can work as a tunable dual-band as well as tunable multi-input-multi-output (MIMO) antenna. In this proposed technique, two patches are used instead of a single patch. This allows us to use the different graphene chemical potential in a single antenna. The tunability of the two ports is achieved by using different chemical potentials. If both the port has the same chemical potential the designed works as a dual-band reconfigurable antenna. In another case, if the chemical potential is tuned at the same frequency the antenna works as the MIMO antenna. In both the case, the antenna is tunable over the frequency range 1.45 THz to 2.17 THz.
Sterilization and decontamination of surfaces by plasma discharges

Abstract: This chapter discusses the possible application of non-equilibrium plasma discharges for the sterilization and decontamination of surfaces. First, the basic properties of electrical discharges are reviewed and a survey of different methods of plasma generation both at atmospheric pressure and reduced pressures is provided. The chapter subsequently discusses the interactions of plasma with diverse biological systems such as bacteria and bacterial spores, endotoxins and proteins.
Performance of SPS Control with External and Internal Phase Shift for DAB DC-DC Converter

This paper deals with a performance evaluation of SPS (single phase shift) control for a dual active bridge. The SPS control is implemented on DSP with external and internal phase shift. Moreover, a design procedure for the development of high-power density dual active bridge (DAB) DC-DC converter for EV charging is presented. A 6.6 kW prototype is developed and tested with SPS modulation with both external and internal phase shift control. A loss model is also developed for comparing the losses occurred in both phase shifts. Simulated and experimental results show a comparison of SPS modulation with both phase shifts.
Stereoscopic Image Coding Using a Global Disparity Estimation Algorithm Optimizing the Compensation Scheme Impact

This paper focuses on the disparity-compensated stereoscopic image coding. This coding scheme is the most implemented technique taking advantage of the typical stereoscopic image redundancy. Namely it first predicts one view using the other view and a disparity map. A compensated view is then computed by coding the remaining difference between the view and its prediction. This paper concerns the computation of the disparity map combining two existing techniques. The first one, more efficient at low bitrate, is an iterative search reducing the bit cost of losslessly storing the disparity map at the expense of a small increase in distortion. The second one, more efficient at high bitrate, selects disparities minimizing the distortions of the compensated view, assuming that JPEG is used in the compensation. This combination appears to be fruitful, achieving increased performance at low- and mid-range bitrate, when tested on a few stereoscopic images and compared to these two techniques.