Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Knowledge Based Assistive Technology Using Robot to Enhance Cognitive Ability for Intellectual Disability

Intellectual disability is a term for a person with intellectual abilities that are below average compared to normal children. Therefore, an interactive learning system was designed to be able to improve intellectual disability children's cognitive with the help of robots, so that it can make it easier to educate intellectual disability children. The system was tested with one subject of Moderate Intellectual Disability. Assessment of learning capability of subject then was compared with the conventional learning method. The average learning score $\mathbf{77.68} \pm\ \mathbf{15.17}$ was superior to the conventional learning one $\mathbf{31.40}\ \pm \mathbf{16.01}$. The Results show that utilizing the proposed learning assistive robot could be expected to improve subject focus and affected in improving cognitive abilities of the subject. Improvements would be addressed to the development database of learning that is needed for further analysis of cognitive abilities.
On bootstrapping M-estimated residual processes in multiple linear regression models

It is shown, under fairly general conditions, that Efron's bootstrap procedure captures the limit distribution of weighted empirical processes based on M-estimated residuals in multiple linear regression models. As an application, we construct bootstrap confidence bands for the error distribution function F. The main result can also be used to design distribution-free goodness-of-fit tests for F without any recourse to the split-sample estimation of the regression parameters.
Lightweight multi-modal emotion recognition model based on modal generation

Emotion recognition is a very important part of HCI, and its application is very extensive. Therefore, emotion recognition has become a research hotspot in recent years. This paper proposes a lightweight emotion recognition network based on mode generation. Specifically, this paper uses audio mode as the input raw data, and converts audio data into text data through the mode generation algorithm to form a bimodal emotion recognition model based on audio and text; In addition, the input audio data is converted into MFCC (Mel Frequency Cepstral Coefficients) to increase the feature quantity of audio modes. Finally, a lightweight network is used to extract its features; Finally, attention mechanism is introduced to fuse the features of the two modes at feature level. The experimental results show that the lightweight emotion recognition network based on mode generation proposed in this paper can greatly reduce the parameters of the model and enable the model to be deployed and implemented on the mobile terminal, while ensuring the accuracy of emotion recognition.
Discrete reachability of hybrid systems

This paper concerns hybrid systems subject to discrete-event supervisory control. It investigates the discrete reachability, where the hybrid system should be moved from a discrete initial state z init into a given discrete goal state z goal by a sequence of discrete inputs. The reachability analysis is carried out in three steps. First, a discrete-event model of the hybrid system is set up. Stochastic automata are used to describe all state sequences which may be generated by the hybrid system. Such a model is called complete. Second, methods for the reachability analysis of stochastic automata are elaborated which concern a weak and a strong condition for reachability. Third, these methods are applied to the hybrid system. It is shown that the reachability of the automaton implies the discrete reachability of the hybrid system, because the model is complete. Therefore, the weak and the strong condition for reachability of the stochastic automaton yield a necessary and a sufficient condition for discrete reachability of the hybrid system.
Research on Pointing Acquisition Tracking Technology of Shipborne Laser Communication

The Pointing Acquisition Tracking (PAT) technology, as a powerful guarantee for fast establishment and stable communication of wireless laser communication links, is the key to improve the practicality of shipboard wireless laser communication. In this paper, the initial pointing method of the shipboard laser communication terminal is designed by using the position and attitude information obtained from the GPS/INS module. Since the system optical relationship varies with the coarse tracking mechanism attitude during tracking, the optical decoupling model from the coarse tracking mechanism to the CMOS detector is measured using the optical matrix method. On this basis, the robust linear active disturbance rejection control (LADRC) algorithm is used for spot tracking. Designing the acquisition and tracking experiment of maritime laser communication, using the initial pointing algorithm to obtain the initial pointing angle of the antenna boresight and successfully capture the beacon light. Experiments show that the tracking peak error of coarse tracking is around 40urad, and the standard deviation is below 20urad. the tracking peak error of fine tracking is around 15urad, and the standard deviation is below 10urad. The experimental data show that the tracking algorithm meets the requirements of stable, reliable and high-precision tracking.
Smart IoT based Early Stage Drowsy Driver Detection Management System

This study aimed to develop an innovative alert management system for creating smart cars that detect drowsy driving and prevent it automatically. However, sleepiness is a common physiological occurrence in humans that can occur for various reasons. To prevent the accident's cause, a reliable management system must be designed. In this suggested work, it is proposed that a drowsy driver warning system was created utilising a method where the Video Stream Processing (VSP) used an Eye Aspect Ratio (EAR) and Euclidean distance of the eye to study the eye blink concept. The facial landmark method is also used to distinguish eyes with accuracy. The IoT module delivers a warning message with collision incidence and position information, alarms via voice speaking, and notifies nearby traffic/the owner of the car over the Raspberry Pi tracking system when the driver's fatigue is detected. The suggested model excels in that it can identify tiredness in both daytime and nighttime vision with obstacles at different distances with an accuracy greater than 98%.
Energy Management Algorithm in Wireless Sensor Network for Pipeline Monitoring

Energy consumption modelling is the starting point for the development and assessment of accurate Energy Management (EM) schemes in Wireless Sensor Network (WSN). Energy consumption is the greatest obstacle to the widespread deployment of WSNs. EM is crucial for remotely deployed sensor nodes with energy constraints. As energy is considered a finite resource for sensor nodes, an effective EM scheme is essential for managing the energy resource. In this paper, an energy consumption model and duty cycle-based EM algorithm for acceleration based WSN are proposed. On a 21 mx1Sm outdoor experimental testbed of acceleration based WSN, this algorithm was tested and evaluated. The testbed was configured with six valves labelled V1 through V6. During the experiments, we manually adjusted the valves to three different states, namely fully closed (FC), half-opened (HO), and fully opened (FO), to simulate rupture on the pipeline network. The simulated events demonstrated that acceleration amplitude (g-force) is proportional to rupture size and rupture distance from the sensor node location. These measured values were used to determine the magnitude and location of the pipeline network rupture. Comparing the established EM algorithm to the network operating continuously without the algorithm, nearly 95% of the energy was conserved.
Study of Tunnel Field Effect Transistors for Biosensing Applications: A Review

The modern and advanced molecular biology require the reliable and cost-effective biosensing techniques for the detection of different biological species. The detection of wide range of such bio-molecular species is necessary for the successful prognosis and early-stage detection of serious mankind diseases. For this, FET-based biosensors have been addressed as the most promising candidates due to their ultra-sensitivity, mass production and low cost. Further, to take a potentially revolutionize step in the field of biosensing, tunnel field effect transistors (TFETs) were introduced. TFETs are hinged on the principle of quantum band-to-band-tunneling mechanism, which is helpful for suppressing the disadvantages of conventional MOS devices. In the field of biosensing, low detection time, low cost, mass productivity, high sensitivity and good reliability are the need of the time. Therefore, to further promote the understanding of TFET based biosensors, this review article presents different TFET based device structures along with their sensitivity for biosensing applications. In the end of this article, the status map of different TFET-based biosensors is presented in terms of their drain current sensitivity. This status map of biosensor is helpful to envision next steps in medical and clinical researches.
Multi-Domain Thermal Object Detection Using Generative Adversarial Networks

Autonomous driving (AD) is undeniably gaining a substantial role in the automotive industry. AD exploits emerging advances in object detection that benefit from improvements in deep learning algorithms, especially convolutional neural networks (CNNs). Scene perception is a crucial task in self-driving vehicles. Scene perception in AD is the ability to extract relevant data from the surroundings. The efficacy of perception largely depends on the sensors and cameras used in capturing the scene. It also depends on the surrounding environmental conditions, which affect the sensors and cameras.
A Distributed Software Agent-Oriented Traceability Milieu for Blockchain-Enabled Supply Chain

Supply chain is a potential area that can immensely benefit from blockchain technology (BCT), making supply chain operations more reliable, safer, traceable, and trustworthy for all its stakeholders. However, there are numerous challenges such as scalability, coordination, safety, and traceability-related issues which are yet to be resolved. Multi-agent systems (MAS) offer a completely new dimension for sustainability and operational traceability in distributed culture. MAS consists of an assembly of automated agents which can be deployed to track operational states both internally and externally in a distributed system. In this work, an effort has been made to develop a comprehensive traceability framework consisting of multiple agents for a large-scale product manufacturing supply chain enabled by blockchain technology. The agents are designed to monitor and drive the supply chain workflow to ensure traceability of the system. This framework can eliminate the limitations of the existing supply chains concerning traceability.
MODELING AND SIMULATION OF BINARY APPROACH BY ENERGY CONSERVATION ANALYSIS

Abstract A new model for approach processes of two unequal or equal sized drops or bubbles is proposed based on a parallel film concept and energy conservation analysis. This model can estimate the interaction time and the maximum film area for an approach and the effects of Weber number, size ratio and curvature of film. It also gives a good explanation why the effective virtual mass coefficienl in the model of Chesters and Hofman (1982) changes with the size ratio of drops or bubbles.
3D Finite Element Simulations of Indentation Fracture Test of ZrO2-Ceramics with Taking into Account its Microstructure

The present contribution is aimed to identify the microstructure of zirconia ceramics possessing the higher fracture toughness. 3D-FE-simulations of indentation testing with taking into account damage evolution in the cut-out of a microstructure have been performed in ZrO2-ceramics by using the FEM-Software ABAQUS CAE. We have performed the simulation of failure behavior of ZrO2-ceramics, considering the crack propagation both in the interior of grains (with Element Elimination Technique) as well as in grain boundaries (with Cohesive Zone Modeling approach of two different micro-structures. The constitutive behavior of the grains has been considered to be orthotropic elastic. The performed simulations could serve as an example of the solution of specific multiscale fracture mechanical problem and its application to design of materials.
Two Perpendicularly Colocated Cardioid Sensors of Different Cardioidicity Indices/Orders– Their Design Guidelines Based on Their Polar-Azimuthal Direction-Finding Cramér–Rao Bound

A cardioid sensor has a remarkably directional gain pattern of $[ \alpha + (1 - \alpha) \cos (\beta) ] \cos ^{k}(\beta)$, where $\alpha$ represents the sensor’s cardioidicity index, $k$ symbolizes the sensor’s directivity order, and $\beta$ refers to the incident angle with respect to the cardioid sensor’s axis. Cardioid microphones/hydrophones are commercially available through numerous manufacturers and widely adopted in workaday acoustics. Nonetheless, this letter is first in the open literature to consider any array comprising cardioid sensors of (possibly) different cardioidicity indices/orders. That is, this analysis pioneers the versatile possibility of $(k, \alpha)$ being different between the two cardioids that constitute the pair. These two foresaid cardioids are perpendicularly oriented, but spatially colocated in this article. First, their dissimilar orientations facilitate the estimation of an incident signal’s 2-D direction-of-arrival over the azimuth and the elevation, despite the deployment of simply two sensors. Second, their spatial colocation decouples the incident signal’s time/frequency dimensions from the signal’s azimuth/elevation directional dimensions, thus simplifying any subsequent signal-processing computations. This article will analytically explore such a pair’s azimuth/elevation direction-of-arrival estimation precision, as measured by the Cramér–Rao lower bound, producing action-able insights for the system engineer to choose what combination of $(k_{x}, \alpha _{x})$ and $(k_{y}, \alpha _{y})$ for the two cardioids forming the pair.
Design and Development of PV Powered Intergraded DC - DC Converter Fed Multilevel Inverter for Standalone Application

The use highlighter amount of nonconventional energy is causing greenhouse gas emissions worldwide, which is affecting the climate adversely. To avoid this, all counties keen to swapping conservative energy foundations to non-conservative foundations. There are variety of DC to DC converters reported in literature with various power-boosting performances which comprises of energy storing passive elements (in capacitors and inductors), with either 1: n transformers in aggregation along with power electronics switches (MOSFETs and Diodes), which are named as isolated and non-isolated converters respectively. These converters are selected for the specific application depends on the circuit complexity, reliability and cost. Sometimes, the reduction of power circuit components and/or power switches is need to this converter industry to meet out the growing demand, cost, and size. With respect to PV application straightforward increase larger voltage gain DC to DC converters are an option and this should a coordination to operate with DC to AC converter (inverter). Compare to the conventional 2-level inverter, multilevel inverter (MLI) is a better selection since it has a quality to suppress the harmonic distortion on the current and voltage. Although reduced switch MLI have been well recognized as a striking resolution for intermediate power PV and wind power generations’ claims, still challenges and opportunities are looking forward for the reduction voltage harmonic, voltage profile enhancement, increasing the voltage transfer gain, reduction in the switching losses etc. Hence, the proposed is dealt with a suitable high efficient DC to DC converter and MLI inverter can be improve the stand-alone PV power system. In this work, the generalized simulation and practical implementation of PV connected single switch performed seven-level MLI for 100W PV system The suggested work accurately models the relationship between voltage gain and boost duty ratio using theoretical data. investigation is verified experimentally.
SDBNet: Lightweight Real-Time Semantic Segmentation Using Short-Term Dense Bottleneck

A popular choice when designing a semantic segmentation model is to adopt a pre-trained Deep Convolution Neural Network (DCNN) as a backbone and add extra modules for better semantic representation and competitive segmentation results. However, the large number of parameters and substantial memory footprint of these DCNN architectures make these large models unsuitable for real-time applications on mobile devices. To address the issue, this study proposes a very lightweight model, called Short-term Dense Bottleneck Network (SDBNet). By staging a series of bottleneck blocks, an efficient module, termed SDB, is carefully designed and it provides diverse field-ofviews for better contextualization of varied geometrical objects in a complex scene. For precise localization, a shallow branch is deployed in parallel to SDB which shares the spatial details with the SDB module at multiple stages. At the decoder end, a simple, yet effective feature refinement and semantic aggregation module is deployed for better context assimilation and region identification. The proposed model is evaluated using three public benchmarks and the results on Cityscapes (70.8%), Camvid (73.2%) and KITTI (51.8%) test sets clearly demonstrate a competitive performance under the real-time category. Among the real-time scene parsing models under 1.5 million parameters, the proposed SDBNet produces the state-of-the-art (SOTA) results on all three datasets.
Cosmic-ray-induced photodissociation and photoionization rates of interstellar molecules

In the Prasad-Tarafdar mechanism, ultraviolet photons are created in the interior of dense interstellar clouds by the impact excitation of molecular hydrogen by secondary electrons generated by cosmic-ray ionization. Detailed calculations of the emission spectrum are described, and the resulting photodissociation and photoionization rates of a wide range of interstellar molecules are calculated. 84 refs.
Two dimensional modeling of dual material double gate TFET in stacked hetero-dielectrics with split high-K materials

this article develops the analytical modelling of a dual material double gate TFET including gate engineering using a hetero–dielectric gate stack with split high K dielectrics. The parabolic approximation method of charge potential is used to solve 2D Poisson equation. The dielectric stack of gate consists of SiO2 and split high–K materials. The study incorporates the effect of various high-K dielectrics stacked above dielectric and find its influences on band to band tunneling, OFF and ON currents, energy band bending and drain currents. It is evident that the proposed device structure offers a low OFF current (10-17 A/µm) and enhances the ON current. The numerical results are simulated using SILVACO ATLAS simulation software.
Blockchain in Consumer-Centric Electricity Markets: An Overview

The power and energy sector transition into decentralized and distributed models brings a set of challenges that need to be overcome. Among them is consumer empowerment, which requires secure economic transactions and reliable system operation. Thus, new technologies and procedures such as blockchain have gained prominence, due to their reliability and privacy-preserving capabilities. These characteristics are essential for the proliferation of energy community markets. Therefore, this paper provides an overview of blockchain technology applicability to consumer-centric electricity markets, highlighting existing projects and initiatives. Additionally, key enablers and barriers to blockchain deployment in local electricity markets are discussed, followed by a roadmap for the comprehensive adoption of such technology.
Managing Projects More Successful: Evaluation of a Concept for Implementing Critical Success Factors in Practice

One of today's challenges for manufacturing companies is the compliance of their products with increased new technological and regulatory requirements from official bodies, such as the European Commission. For the construction equipment industry, these regulations particularly entail the development of emission free machines alongside corresponding software components, for instance a battery management system. Since manufacturers in this industry can still be considered as traditional in their product offer, as compared to e.g. the automotive industry, expertise in these fields of product development must be further established. As the interdependence of, e.g., hardware and software projects is one of the drivers of project complexity, the purpose of this study is to investigate a concept for an improved management of this type of projects. To this end, the implementation of critical success factors for project management was conceptualized, partially employed, and empirically assessed. By adapting the Design Research Methodology framework, a literature review, a series of interviews, online workshops, and a survey were conducted in cooperation with a construction equipment manufacturer. The results show that the CSF concept is perceived to be rather easy to use and rather useful. This study provides a proposal and first-time empirical assessment of guidelines for applying the CSF concept in project management.
Optimal Control Strategy of CLLC Bidirectional Resonant Converter under Light Load

CLLC resonant bidirectional DC-DC converter has characteristics of isolation and high efficiency. The burst mode under light load is an effective way to increase the light load efficiency. In this paper, pulses burst mode control strategy is proposed to improve the light load efficiency by optimizing the pulse width in a fixed burst on time. First, a modeling method of time domain analysis is adopted to obtain the accurate converter model, Then, the subsequent two pulses work at the resonant frequency point. The burst off time is adapted to the load change in the burst mode. This pulses burst mode control strategy does not need to measure the resonant capacitor voltage, simplifies the circuit, improves the efficiency of light load and minimizes the output voltage ripple. Simulation results verify the efficiency of the method.
Electron dynamics in molecules: a new combination of nuclear quantum dynamics and electronic structure theory

An efficient approach to describe electron dynamics in molecules is developed which exploits quantum dynamics and quantum chemistry in a new way. The photodissociation of D+2 which can be controlled via the carrier-envelope phase of an ultrashort laser pulse is chosen as a test system. In this system, the approach is checked against more rigorous theories as well as experiments which show excellent agreement. The electron dynamics is visualized in several ways including the phase information of the electronic wavefunction. The detailed analysis of the electron motion after different ionization events reveals the underlying complex dynamics which are hidden in the experiment. The interplay between the carrier-envelope phase and electron control is elucidated. The ansatz is based on the highly developed electronic structure theory and can be implemented quite easily. The method allows for a successive extension to multi-electron systems and simultaneously enables a quantum-dynamical description of the nuclear motion.