Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle Efficiency Prediction using Machine Learning Algorithms

The performance analysis and efficiency of a vehicle play a prominent role and a very necessary step to do in today's scenario. There are various instances when the user feels reluctant to discard the vehicle. In such cases where the user is ignorant of the fact to discard the car, the concerned authorities must come forward to check whether the user is using the car beyond the limit. Therefore, there is an increasing need to save the environment and nature to live a sustainable life. The performance analysis of the car is based on the engine type, number of engine cylinders, fuel type, etc. This study predicts the mpg value by using machine learning models like Random Forest (RF), K-Nearest Neighbors (KNN), XG-Boost, Ridge Regression, Lasso Regression, etc. and based on that it is compared with the optimum value of mpg and hence one can reach to a decision to discard the vehicle.
The Application of Hybrid Optimization For FIR Filter Design

In this research, the finite impulse response (FIR) filter design procedure is analyzed with use of novel design technique of optimization while considering the specified parameters. This technique will define the coefficients in optimized form for the different FIR filters- LP, HP, BP and BS filters with the objective of getting the desired frequency response. The error function of filter is considered as an optimization function in which the values for δp and δs are specified individually. A new optimization algorithm is undertaken in this with the utilization of a hybrid technique composed of Bat and Seeker optimization. The design is implemented with the use of various optimization approaches as Particle Swarm Optimization (PSO), Seeker, Bat and Hybrid of Seeker and Bat optimization innovations in order to fulfil the ideal impulse response attribute of FIR filters. Furthermore, the comparative analysis of all the techniques is presented in terms of the variant attributes of FIR filters and also for the different orders of FIR filters. The hybrid approach depicts its superiority in comparing the design results of all the techniques and it shows the best results among all.
Modeling Tendon-actuated Concentric Tube Robots

Mechanics-based models have been developed to describe the shape of tendon-actuated continuum robots. Models have also been developed to describe the shape of concentric tube robots, i.e., nested combinations of precurved superelastic tubes. While an important class of continuum robots used in endoscopic and intracardiac medical applications combines these two designs, existing models do not cover this combination. Tendon-actuated models are limited to a single tube while concentric tube models do not include tendon-produced forces and moments. This paper derives a mechanics-based model for this hybrid design and assesses it using numerical and physical experiments involving a pair of tendon-actuated tubes. It is demonstrated that, similar to concentric tube robots, relative twisting between the tendon-actuated tubes is an important factor in determining overall robot shape.
Spiking Neural Network Implementation on FPGA for Multiclass Classification

Spiking Neural Network (SNN) is a particular Artificial Neural Networks (ANN) form. An SNN has similar features as an ANN, but an SNN has a different information system that will allow SNN to have higher energy efficiency than an ANN. This paper presents the design and implementation of an SNN on FPGA. The model of the SNN is designed to be lower power consumption than existing SNN models in the aspect of FPGA implementation and lower accuracy loss than the existing training method in the part of the algorithm. The coding scheme of the SNN model proposed in this paper is the rate coding scheme. This paper introduces a conversion method to directly map the trained parameters from ANN to SNN with negligible classification accuracy loss. Also, this paper demonstrates the technique of FPGA implementation for Spiking Exponential Function, Spiking SoftMax Function and Dynamic Adder Tree. This paper also presents the Time Division Component Reuse technic for lower resource utilization in the FPGA implementation of SNN. The proposed model has a power efficiency of 8841.7 frames per watt with negligible accuracy loss. The benchmark SNN model has a power efficiency of 337.6 frames per watt with an accuracy loss of 1.42 percent. The reference accuracy of the ANN model is 90.36 percent. For comparison, the specific model of the SNN has an accuracy of 90.39 percent.
ProcessorFuzz: Processor Fuzzing with Control and Status Registers Guidance

As the complexity of modern processors has increased over the years, developing effective verification strategies to identify bugs prior to manufacturing has become critical. Inspired by software fuzzing, a technique commonly used for software testing, multiple recent works use hardware fuzzing for the verification of Register-Transfer Level (RTL) designs. However, these works suffer from several limitations such as lack of support for widelyused Hardware Description Languages (HDLs) and misleading coverage-signals that misidentify ‘‘interesting’’ inputs. Towards overcoming these shortcomings, we present ProcessorFuzz, a processor fuzzer that guides the fuzzer with a novel CSR-transition coverage metric. ProcessorFuzz monitors the transitions in Control and Status Registers (CSRs) as CSRs are in charge of controlling and holding the state of the processor. Therefore, transitions in CSRs indicate a new processor state, and guiding the fuzzer based on this feedback enables ProcessorFuzz to explore new processor states. We evaluated ProcessorFuzz with three real-world opensource processors — Rocket, BOOM, and BlackParrot. ProcessorFuzz triggered a set of ground-truth bugs $1.23 \times$ faster (on average) than DIFUZZRTL. Moreover, our experiments exposed 8 new bugs across the three RISC-V cores and one new bug in a reference model. All nine bugs were confirmed by the developers of the corresponding projects.
Electrocardiogram (ECG) Outline Exploration And Classification Through The Range of CWT (Cross Wavelet Transform)

The electrical activity of the heart is measured by an electrocardiogram (ECG), and any variation from the typical rhythm indicates a potentially harmful change in the pathophysiological state of the organ. The cardiologists have a good understanding of the format and structure of a normal, healthy electrocardiogram (ECG). They examine the clinical abnormality that is present in the ECG by visually inspecting the morphology of the signal using their years of experience. This is done with the electrocardiogram. It is vital to select an appropriate representation for the signal itself before we can move forward with developing a system that is capable of performing automatic ECG analysis. The raw time series can be used as a sufficient description of the data that has to be analyzed, and this is the way that is the simplest to utilize. Despite the fact that this method is enticing owing to the inherent simplicity it possesses and the ease with which it can be implemented, it is plagued by a number of significant shortcomings. This study work presents a classification methodology for screening patients with antero septal myocardial infarction that is constructed utilizing the closest neighbour (NN) method (ASMI). The amplitude of the QRS and the height of the T wave, both of which are diagnostically relevant parameters, are retrieved here from leads V1-V4. For the purpose of merging the effects of the four leads, a score value approach is utilized. The NN classifier uses both the Euclidian and the Mahalanobis distance metrics in its calculations. It was discovered that the latter provides a better level of performance.
Vision-Based Shared Control for Telemanipulated Nasopharyngeal Swab Sampling

Telemanipulation enables people to perform tasks in dangerous environments without exposing them to any risk. This also applies for medical applications. Many infections, such as the SARS-CoV-2 virus, spread over the air and can infect the staff while, e.g., taking samples. This paper proposes a shared control algorithm for a telemanipulation system that enables medical staff to easily perform nasopharyngeal swab samplings from a safe distance while maintaining the safety of the patient. We propose a vision-based virtual fixture approach to guide the operator during the approach towards the nostril. Force feedback and velocity scaling is used to improve dexterity and safety during the sampling. We further prove the stability of the system by introducing an energy tank that ensures passivity at all times. Finally, we test the approach on a real telemanipulation setup and demonstrate the improved usability resulting from the guidance of the shared control.
Modelling and Analysis of different types of TSV for Three Dimensional Integrated Circuits

The majority of systems on the market today are made up of intricate SoCs with integrated processors, large quantities of memory and FPGAs, but they do not offer a complete system solution for real-world systems [1]. Designers are faced with numerous technological and financial obstacles when attempting to combine such different technologies onto a single chip [2]. Additionally, vertical integration was shown to be a desirable alternative due to the continuously growing needs for low cost, smaller chips with more capability, and shorter time to market for portable systems [3]. Three-dimensional integration is the vertical interconnection of unpackaged or packaged semiconductors [3]. The most promising technique for designing ICs and systems with high performance, functionality, and lower power consumption than the 2D technologies is three dimensional integrated circuits (IC) in the form of three-dimensionally stacked chips. Using inter-tier interconnects that directly cross the substrate, various dice can be joined in 3D integration [4]. This is referred as Through Silicon Via (TSV) based 3D integration technology. To provide uniform environmental conditions throughout all tiers, TSV tapering is done to achieve the least amount of voltage drop and delay difference possible. When the resistance values and current demand for each tier are known, thevenin network architecture may be used for TSV sizing.
Follow me: interleaving human tracking and interacting with a new assistant robot

The development of socially-aware autonomous robots for being part of our daily lives is a pressure matter. Although almost all current robots use multiple sensors, the limitations of these autonomous systems to coexist with people are still evident: they have difficulties fusing all this information in an agile and consistent way and thus understand human intentions and act accordingly. The main consequence of these limitations is that today’s robots do not respond adequately to these human behaviours and, for example, result in forced and unnatural navigation. Today’s robots strive to have complete knowledge of the surrounding environment to make the right decisions at the right time. However, this purpose requires a perspective that integrates sensors of different natures and, based on it, a multi-modal perception of actuation. This paper presents a multi-modal and distributed architecture for the perception of the environment that uses information given by the robot sensors. The results of the proposed architecture are validated in the Follow Me use a case in a real environment.
Exploring tactile sensing to perform the power grasp of a human-robot handshake

The way humans relate to robots is constantly evolving. The trust that we are gaining towards them, allows an evolution in the cooperation between us and them. Vizzy, a social robot that helps to promote physical exercise among the elderly, was previously programmed to perform handshakes. Its fingers were equipped with magnetic tactile sensors. However, the sensors on the fingers did not produce enough information regarding when to start a handshake, or when to end it, among other information, due to their localization. This work presents an upgrade of its tactile sensing capabilities, introducing a new silicone palm with magnetic sensors that allows to obtain more information during the grasping. In addition, we design a controller that closes the hand in an autonomous way, tailored for handshakes. This work presents the hardware design and implementations, as well as all the software needed to enable the use of the sensors (sensor calibration and design of the new control system). Finally, the results obtained prove a solid calibration, and a positive response to the implemented control system. Furthermore, the silicone palm produced was highly praised for the comfort introduced to the interaction.
A Coverage-Driven Systematic Test Approach for Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is a prerequisite for accurate navigation of autonomous vehicles. Although this is often safety- critical, systematic approaches for testing the correctness and accuracy of SLAM algorithms are missing. In this paper, we present an approach for automated and systematic testing of SLAM algorithms. We identify challenging environmental features for SLAM, define coverage criteria that characterize the SLAM problem’s input space, and develop a method for automatically generating high-coverage tests. We demonstrate the effectiveness of our approach with a case study on an existing FastSLAM implementation.
Low-Latency Masking with Arbitrary Protection Order Based on Click Elements

Masking is the main countermeasure against side-channel attacks due to its sound formal proof of security and the scalability of its protection parameters. However, effective masking increases the implementation complexity by requiring additional silicon area, random number generators and higher latency. Thus, reducing the masking implementation costs while conserving its robustness under side-channel attacks is a relevant branch of research in hardware security applications. Relying on the two-phase bundled-data protocol, this work presents a low-latency masking implementation with arbitrary protection order. In particular, we base our approach on the click elements to control the handshake logic, allowing us to implement asynchronous circuits using conventional synthesis tools. In this manner, we are able to obtain an effective single-cycle and protected implementation of the AES S-box requiring smaller silicon area and potentially lower power consumption compared to the state-of-the-art. Additionally, we detail the asynchronous design methodology that can be applied in different scenarios to improve the latency of secure hardware designs. Finally, we assess leakages to evaluate the robustness of our approach against side-channel attacks.
Constraint-Guided Automatic Side Object Placement for Steering Control Testing in Virtual Environment

Side objects are the common road objects placed alongside roadways, such as traffic signs, trees or street lights. Even though such objects do not obstruct a driving path directly, the visual perception-based autonomous features may recognize them in an unintended way impeding their ideal behavior. We propose a framework that systematically places various types of side objects in the virtual environment for testing the perception-based steering control. Firstly, we give the mathematical constraints that characterize both linear and non-linear geometric aspects in placing the side objects including the distances from a roadway or other side objects, and their placement patterns. Secondly, we define the placement distribution criteria that characterize how well the side objects are to be distributed along the roadways. Finally, our placement generation algorithm automatically determines the position of the side objects via the SMT (Satisfiability Modulo Theories) solver, and the generated placements are guaranteed to conform to both the constraints and distribution criteria. The experiment shows the scalability of the placement algorithm as to how fast a large number of side objects can be generated conforming to the aforementioned properties. In addition, we show how the Convolutional Neural Network (CNN)-based steering controller alters its behavior under multiple environments generated from our framework according to the RMSE and disengagement metrics.
Topology Evaluation of Impedance Source Inverter for Solar Based EV Applications

Z-Source Inverter (ZSI) topology plays a main role as a part of advanced power electronics topologies to achieve the high efficiency and performance with minimum complexity. The ZSI eliminates the dual stage process of conversion by eliminating the boost converter stage before the inverter to increase the low-level input voltage. The required voltage levels to drive the load can be achieved by a single stage of conversion using ZSI with the help of impedance source network associated with switching operation of inverter switches. Hence, the ZSI is widely used for the applications requires to attain the high voltage AC output from the low voltage DC. ZSI have considered to be a best solution for the electric vehicle applications due to the ability of the converter to increase the inverter output range with high gain and provides the improved stability of a motor drive system. This paper mainly emphases on the review and comparison of the various ZSI topologies for EV application driven by a PV source of energy. Also provides the gain parameter comparison for the selected topologies with the resulted data from modelling and simulation of selected topologies with simple boost variable duty ratio methods.
Dynamic Vulnerability Classification for Enhanced Cyber Situational Awareness

Cyber-threat landscape and adversarial capabilities have strengthened significantly due to the digital transformation and increased computational capacity of individuals. To stay ahead in the game, a cyber defender must have full situational awareness of any existing infrastructural vulnerabilities. Lever- aging vulnerability reports from NVD, MITRE, Twitter, etc., is an uphill task as one must find the existing vulnerabilities first, find vulnerability reports for the same, and then prepare a mitigation plan by going through each report individually. Moreover, human attention is needed to understand the context and decide whether the risk is acceptable or actionable. In this work, we architect and implement an AI-based prediction engine for our Cyber-threats and Vulnerability Information Analyzer (CyVIA) framework to classify vulnerability reports based on inferred attack types. This AI-engine speeds up the vulnerability analysis process for cyber defenders by providing the applicable attack types on the evaluated infrastructure. We test various unsupervised and supervised machine learning models to classify vulnerability reports. Furthermore, we compare the results, tune the best-observed models, and propose a final fully trained model with the highest accuracy for classifying new vulnerability reports.
Factors of the City Inhabitants' Involvement in Housing Resident Chat Rooms

The paper analyses the factors for the city residents' involvement in local online groups. These groups are connected by their place of residence, activity or a specific problem. The global Internet is abounding with small communities, and social practices are constantly transforming. Being or not being affiliated with a community is not always determined by age or information technology proficiency. Axiological characteristics that affect the participation and contribution to housing resident chat rooms are revealed. The results of the study can be used in the development of digital government programs for forecasting and improving management strategy, and in the promotion of digital services and applications for “smart house”, “digital city” and “digital government”.
H-Plane Substrate Integrated Waveguide Horn Radiator For 5G Application

The millimetre wave (mmW) high speed, wide bandwidth, antennas are required for the current generation of radiators for high-speed vehicles like trains, airplanes, and satellites. The substrate integrated waveguide (SIW) technology reduces the losses above 10 GHz frequencies and is a most fascinating technique for the current and future generation of mmW applications. An optimum H-plane horn radiator with high gain, wide bandwidth, and low penetration losses is proposed on multilayer dielectric substrates (Rogers RT5880 (thickness = 0.79 mm) and Rogers RO3003 (thickness = 0.75mm)) of size 27.66 x 35.06 mm2to cover 26.33-35.55 GHz frequency range (bandwidth 9.22 GHz). The proposed single port SIW H-plane horn radiator exhibits radiation efficiency of 96.25-98.2%, and 6.07 - 9.6 dBi gain in -10 dB return loss frequency band.
Automation of Call Center Workforce Management Using NeuralProphet

This research addresses some of the current as well as widely used agent headcount prediction or forecasting models in the call center systems. Performance provided by the models used today are reasonably good, however there are some drawbacks which can be fixed with the help of AI based time series algorithms. In this paper, four such algorithms namely Holt-Winter's, ARIMA, SARIMA and NeuralProphet were used. For this purpose, the data of hourly call arrivals for a period of 3 years was collected and training and testing was performed on it. The accuracy of the models was compared on the basis of the root mean squared error (RMSE) test performed on them. The proposed work benefits Small Organizations who do not have the money or the resources to afford a separate planning team for effective headcount planning and can only afford limited number of agents in the call center system. The number of calls provided by the model helps to enhance the accuracy of staffing based on call arrival patterns (duration and time of calls), average call handling time and seasonality-time of year (holidays and end of the year calls).
Adaptive Backstepping Control of Electro Hydrostatic Actuator with Improved Parameter Estimation

Electro hydrostatic actuator (EHA) is a pump-controlled hydraulic system and has been successfully applied in the aeronautical field by the advantages of miniaturization and energy-savings. However, due to the high order dynamics and the existence of various nonlinearities and uncertainties in the EHA system, it is a challenge to achieve accurate motion control. Most of the recent research on adaptive hydraulic control only focuses on the position tracking control performance while the parameter adaptation performance is unsatisfying. Therefore, this paper proposes a adaptive backstepping control algorithm for EHA system, especially considering improved hydraulic parameter estimation. Firstly, the parameter estimation errors are formulated by introducing an auxiliary filter operation that avoided used the system acceleration information. Then a novel adaptive law is designed to update the parameter estimation using both the position tracking errors and the estimation errors which improve the parameter estimation performance compared traditional adaptive law. Moreover, due to the higher-order dynamics of the system, the motion controller is designed using a modified backstepping method, in which the proposed adaptive law is used to on-line estimate the unknown parameters at each step. Theoretically, the stability of the EHA system and asymptotic motion tracking and parameter convergence performance can be proved by Lyapunov function. Finally, comparative simulations are conducted to vaelidata the effectiveness and superiority of the proposed control approach.