Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Time-to-Amplitude Converter and a Low-Cost Wide Dynamic Range FPGA TDC for LiDAR Application

This article demonstrates a low-cost wide dynamic range field-programmable gate array (FPGA) time-to-digital converter (TDC) and a time-to-amplitude converter (TAC) based on an embedded XOR gate of the FPGA and a low-complexity circuitry. TACs and TDCs are important for distance measurements in light detection and ranging (LiDAR) systems in a wide range of applications from self-driving cars to topographic mapping. LiDAR’s high cost, relatively large size, and complexity are important implementation challenges that need to be addressed. In the literature, external circuits are used to provide low phase noise clock signals into the FPGA TDC to measure long-range time intervals adding to the complexity and cost. Our TDC can measure a wide dynamic range of 1 ms with high precision using a low-cost FPGA board. Calibration using dynamic phase-shifting (DPS) is conducted with a resolution of 18 ps. The performance measurements gave us insight on the relative temperature insensitive regions for operating the TDC. The ideal range was found to be 24 °C–39 °C. We also present a novel TAC design to demonstrate that one XOR gate of a low-cost Artix-7 FPGA and a simple low-cost circuitry can detect a phase difference as low as 18 ps. The TAC’s linearity is presented through integral nonlinearity (INL) and differential nonlinearity (DNL) test results. For every additional channel, our TAC requires only one XOR gate in the FPGA chip and a low complexity circuitry. This allows for multichannel operation and larger area availability for use in other implementations. Performance measurements show our TAC to be highly insensitive to temperature and voltage changes.
Recent Advances in Modeling of Perovskite Solar Cells Using SCAPS-1D: Effect of Absorber and ETM Thickness

With the massive breakthrough recorded in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) from 3.8 % to > 25 %, PSCs have attracted considerable attention in both the academia and industries. However, some challenges remain as barrier in realizing its deployment. To develop a highly efficient PSCs as well as environmentally benign device, simulation and optimization of such devices is desirable. Its impractical as well as wastage of time and money to design a solar cell without simulation works. It minimizes not only the risk, time and money rather analyzes layers’ properties and role to optimize the solar cell to best performance. Numerical modeling to describe PV thin layer devices is a convenient tool to better understand the basic factors limiting the electrical parameters of the solar cells and to increase their performance. In this review article, we focused on the recent advances in modelling and optimization of PSCs using SCAPS-1D with emphasis on absorber and electron transport medium (ETM) thickness.
Monaural Music Source Separation Using Deep Convolutional Neural Network Embedded with Feature Extraction Module

In this paper, we propose a novel deep convo-lutional neural network (DCNN) embedded with our feature extraction module (FEM), for monaural music source separation. The UN et ++ is introduced into our FEM for highly flexible feature fusion. At first, an improved encoder-decoder is designed to preliminarily extract multi-scale features from a magnitude spectrogram of the mixture music. Then we use the FEM to further obtain fine features in different scales, and soft masks are finally generated for the separation of each source. The proposed network can capture the main features of multi-scale spectrogram images and make use of the parameters it has learned. We conducted experiments on the MIR-IK dataset and the DSD100 datasets. Our network achieved outstanding performance on the MIR-1K dataset and acquired competitive results on the DSD100 dataset compared with state-of-the-art methods in singing voice separation and source separation tasks.
Moving Pedestrian Localization and Detection With Guided Filtering

Detecting a moving pedestrian is still a challenging task in a smart surveillance system due to dynamic scenes. Locating and detecting the moving pedestrian simultaneously influences the development of an integrated but low-resource smart surveillance system. This paper proposes a novel approach to locating and detecting moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv2, YOLOv3, and YOLOv4 within the provided ROI. Our proposed method resulted in more processing frames per second compared with previous approaches. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. It costs around ~50 fps in CPU to classify moving pedestrians and maintain a highly accurate rate. Due to its fast processing, the proposed approach is suitable for IoT or smart surveillance device which has limited resource.
Device-Free Multi-Location Human Activity Recognition Using Deep Complex Network

Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which include being device-free, privacy-protected, unaffected by light, etc. Owing to the development of artificial intelligence techniques, existing methods have made great improvements in sensing accuracy. However, the performance of multi-location recognition is still a challenging issue. According to the principle of wireless sensing, wireless signals that characterize activity are also seriously affected by location variations. Existing solutions depend on adequate data samples at different locations, which are labor-intensive. To solve the above concerns, we present an amplitude- and phase-enhanced deep complex network (AP-DCN)-based multi-location human activity recognition method, which can fully utilize the amplitude and phase information simultaneously so as to mine more abundant information from limited data samples. Furthermore, considering the unbalanced sample number at different locations, we propose a perception method based on the deep complex network-transfer learning (DCN-TL) structure, which effectively realizes knowledge sharing among various locations. To fully evaluate the performance of the proposed method, comprehensive experiments have been carried out with a dataset collected in an office environment with 24 locations and five activities. The experimental results illustrate that the approaches can achieve 96.85% and 94.02% recognition accuracy, respectively.
ANNarchy - iCub: An Interface for Easy Interaction between Neural Network Models and the iCub Robot

Models and algorithms developed in the computational neuroscience or machine learning domain are able to solve more and more complex tasks. At the same time there is an increasing demand to test these algorithms in virtual or real robot platforms. The Python programming language is commonly used as interface language for machine learning tools (e. g. Keras) or neural simulators (e. g. ANNarchy, Brian2, PyNEST) as the language is easy to use and therefore allowing a quick development of algorithms or models. Robotic platforms, as the here investigated iCub, are using the YARP middleware written in C/C++ as it offers low-level access and high performance. Hence, to implement a closed loop interaction between a model and the robot one needs to connect these two languages without keeping the modeler in the loop. Therefore, we present an ANNarchy-iCub interface providing the user a Python API to a selected set of operations offered by the iCub. In contrast to the YARP Python API, the proposed interface is specialized for the requirements of neural network models and encapsulates the interaction with the robot. The interface capabilities are shown by a simple demonstration where the iCub moves his arm along a given trajectory which can be interrupted by an obstacle. We also provide some insight into the performance considering two important tasks: image processing and joint motion, by comparing our interface implementation with the Python API provided for YARP.
FMS jobshop scheduling using Lagrangian relaxation method

This paper tackles a FMS jobshop scheduling problem with alternative routings. The objective function is to minimize the weighted flow time of jobs. We use Lagrangian relaxation method to relax the precedence constraints and propose a two-stage approach to solve the decomposed one-machine multi-operation problems. Three examples are tested and the computational results are compared to the Lagrangian relaxation method proposed by Hoitomt (1993). The comparison studies show that with the minimum weighted flow time of jobs as the objective of the scheduling problem the method proposed in this paper yields better solution with less computational time than the existing Lagrangian method.
Vote-Share Contracts and Learning-by-Doing CER-ETH

We examine the interaction between vote-share contracts and learning-by-doing. Candidates for a political oﬃce are allowed to oﬀer vote-share thresholds. The elected politician has to achieve at least this threshold value in his reelection result to remain in oﬃce for a second term. We assume there are learning-by-doing eﬀects for incumbents and show that competition leads to vote-share contracts implementing the socially optimal threshold, which is above one-half. Vote-share contracts improve the average ability level of a reelected politician and increase eﬀort in the ﬁrst term of an incumbent. On the other hand, vote-share contracts reduce the probability that learning-by-doing takes place. However, the overall eﬀect of vote-share contracts is welfare-enhancing, even under the assumption of learning-by-doing.
A Concise Presentation of Doubly Fed Induction Generator Wind Energy Conversion Systems Challenges and Solutions

There is increased worldwide wind power generation, a large percentage of which is grid connected. The doubly fed induction generator (DFIG) wind energy conversion system (WECS) has many merits and, as a result, large numbers have been installed to date. The DFIG WECS operation, under both steady state and fault conditions, is of great interest since it impacts on grid performance. This review paper presents a condensed look at the various applied solutions to the challenges of the DFIG WECS including maximum power point tracking, common mode voltages, subsynchronous resonance, losses, modulation, power quality, and faults both internal and from the grid. It also looks at approaches used to meet the increasingly stringent grid codes requirements for the DFIG WECS to not only ride through faults but also provide voltage support. These are aspects of the DFIG WECS that are critical for system operators and prospective investors and can also serve as an introduction for new entrants into this area of study.
Pan-cancer analysis of the angiotensin II receptor-associated protein as a prognostic and immunological gene predicting immunotherapy responses in pan-cancer

Background: Understanding interior molecular mechanisms of tumorigenesis and cancer progression contributes to antitumor treatments. The angiotensin II receptor-associated protein (AGTRAP) has been confirmed to be related with metabolic products in metabolic diseases and can drive the progression of hepatocellular carcinoma and colon carcinoma. However, functions of AGTRAP in other kinds of cancers are unclear, and a pan-cancer analysis of AGTRAP has not been carried out. Methods and materials: We downloaded data from The Cancer Genome Atlas and Genotype-Tissue Expression dataset and The Human Protein Atlas databases and then used R software (version 4.1.1) and several bioinformatic tools to conduct the analysis. Results: In our study, we evaluated the expression of AGTRAP in cancers, such as high expression in breast cancer, lung adenocarcinoma, and glioma and low expression in kidney chromophobe. Furthermore, our study revealed that high expression of AGTRAP is significantly related with poor prognosis in glioma, liver cancer, kidney chromophobe, and so on. We also explored the putative functional mechanisms of AGTRAP across pan-cancer, such as endoplasmic reticulum pathway, endocytosis pathway, and JAK-STAT signaling pathway. In addition, the connection between AGTRAP and tumor microenvironment, tumor mutation burden, and immune-related genes was proven. Conclusion: Our study provided comprehensive evidence of the roles of AGTRAP in different kinds of cancers and supported the relationship of AGTRAP and tumorous immunity.
The effects of preoperative focused cardiac ultrasound in high‐risk patients: A randomised controlled trial (PREOPFOCUS)

Surgery is the third most common cause of mortality worldwide. Focused cardiac ultrasound (FOCUS) yields information on cardiac status and discloses the presence of unknown pathology. Preoperative FOCUS changes patient treatment, allowing for a patient‐tailored anaesthesia. We hypothesised that preoperative FOCUS would reduce the proportion of patients who were either admitted to hospital for more than 10 days or who were dead within 30 days after high‐risk, non‐cardiac surgery.
A Two-Finger Soft Gripper Based on Bistable Mechanism

Soft grippers are widely studied and utilized in soft robotics owing to their advantages in terms of compliance, safety, and adaptability. Limited by the mechanical design and driving methods, their response speed is usually unsatisfactory. To achieve fast and safe grasping, a novel design scheme for a two-finger soft gripper is proposed. The two fingers are tendon actuated and employ two types of bistable mechanisms for quick response and linkage bending. The geometric relationship between the thumb and length of the hyperelastic rings was analyzed in the bending process, from which a potential energy model was established. A high-speed camera was used to capture the movement of the gripper, and it indicated that the grasping process time was less than 0.2 s. Moreover, the gripper can grasp various objects, including fragile and irregularly shaped items. Consequently, the current soft gripper can integrate speed, safety, and compliance through simple mechanical designs.
A Comparison of Leximancer Semi-automated Content Analysis to Manual Content Analysis: A Healthcare Exemplar Using Emotive Transcripts of COVID-19 Hospital Staff Interactive Webcasts

Effective consumer centred healthcare incorporates consumer and clinician perspectives into decision making, in addition to traditional quantitative measures. This information is usually captured in qualitative data that requires manual analysis. Healthcare systems often lack resources to systematically incorporate qualitative feedback into decision making. Semi-automated content analysis tools, such as Leximancer, provide an efficient and objective alternative to time consuming manual content analysis (MCA). Literature on the validity of Leximancer in healthcare is sparse. This study seeks to validate Leximancer against MCA on a broad emotive conversational dataset gathered in a healthcare setting. At the outset of the COVID-19 pandemic, a large Australian hospital and health service conducted interactive webcasts with staff to provide updates and answer questions. A manual thematic analysis and a Leximancer content analysis were conducted independently on 20 webcast transcripts. The findings were compared, along with the time required to the complete each analysis. The Leximancer analysis identified nine concepts, while the manual analysis identified 12 concepts. The Leximancer concepts mapped to five of the concepts identified in the manual analysis, which accounted for 74% of mentions tagged in the text through the manual analysis. Leximancer missed concepts which required an emotional or contextual interpretation. The Leximancer analysis took 21 hours (excluding time to learn the program), compared to 73 hours for the manual analysis. Semi-automated content analysis provides an efficient alternative to manual qualitative data analysis, shifting it from a small-scale research activity to a more routine operational activity, albeit with some limitations. This is critical to be able to utilise at scale the rich narratives from consumers and clinicians in healthcare decision making.
Numerical Simulations of Pharmaceutical Particles Depositing in the Human Respiratory System

Millions of people world wide suffer from asthma, and respiratory diseases are one of the leading causes of death. Patients can be treated or relieved by inhalation of medicine, but the efficiency of the treatment is often dependent on on the local deposition of the inhaled drug. In this thesis, a virtual laboratory for simulating the deposition of pharmaceutical particles inhaled into the human respiratory system has been developed. The virtual laboratory is composed of the Navier-Stokes solverOasis, a particle tracking framework, and several scripts for analyzing flow properties and post-processing of data. The Navier-Stokes solver Oasis has been thoroughly validated on problem specific test cases, and has proven capable of simulating turbulent-like flows with high accuracy and minimal numerical dissipation, at a reasonable computational cost. Based on considerations on the flow properties in the human respiratory system, a particle motion algorithm has been developed and implemented in the framework of LagrangianParticles.py within the open source software fenicstools. The algorithm was verified second order accurate, and performed well under validation against established numerical and experimental results. The airflow in the human respiratory system for moderate and high inhalation rates was simulated, with inflow conditions that mimic the effect of a spray inhaler. The flow field was found to exhibit turbulent-like structures enhanced by asymmetries in the geometry. Finally, simulations of particles inhaled into the human respiratory system were performed, from which connections between particle size and deposition pattern were determined in overall good results reported in the literature.
Changes in vitamin D metabolites at the time of critical illness and 6 months later—A prospective observational study

Despite multiple studies suggesting that low 25(OH)D‐vitamin levels are associated with worse outcomes in critically ill individuals, attempts to mitigate the outcomes by fixed dose enteral supplementation unguided by baseline or target blood levels have been unsuccessful. Since a single measurement of 25(OH)D may not optimally reflect an individual's vitamin D status, we studied the plasma concentration of different vitamin D metabolites and their recovery during and following resolution of acute critical illness.
MLPacker: A Unified Software Tool for Packaging and Deploying Atomic and Distributed Analytic Pipelines

In the last years, MLOps (Machine Learning Operations) paradigm is attracting the attention from the community, extrapolating the DevOps (Development and Operations) paradigm to the artificial intelligence (AI) development life-cycle. In this area, some challenges must be addressed to successfully deliver solutions since there are specific nuances when dealing with AI operationalization such as the model packaging or monitoring. Fortunately, interesting and helpful approaches, both from the research community and industry have emerged. However, further research is still necessary to fulfil key gaps. This paper presents a tool, MLPacker, for addressing some of them. Concretely, this tool provides mechanisms to package and deploy analytic pipelines both in REST APIs and in streaming mode. In addition, the analytic pipelines can be deployed atomically (i.e., the whole pipeline in the same machine) or in a distributed fashion (i.e., deploying each stage of the pipeline in distinct machines). In this way, users can take advantage from the cloud continuum paradigm considering edge-fog-cloud computing layers. Finally, the tool is decoupled from the training stage to avoid data scientists the integration of blocks of code in their experiments for the operationalization. Besides the package mode (REST API or streaming), the tool can be configured to perform the deployments in local or in remote machines and by using or not containers. For this aim, this paper describes the gaps this tool addresses, the detailed components and flows supported, as well as an scenario with three different case studies to better explain the research conducted.
Sustainable Employability: Precariousness, Capabilities, and Functioning of Special Education Teachers in Namibia

Institutions cannot ignore the need for the sustainable employability of people. This study aimed to investigate the sustainable employability of special education teachers from the perspective of employment precariousness, capabilities, and functioning (flourishing and intention to leave) of special education teachers in Namibia. Using a cross-sectional survey design, teachers (n = 200) across seven Namibia regions took part in this study. The Precarity Position Profile, Capability for Work Questionnaire, Flourishing at Work Questionnaire, and Intention to Leave Questionnaire were administered. The results showed that precarious employment was negatively associated with the capability set and with flourishing (emotional, psychological, and social well-being) and positively associated with intention to leave. Low job insecurity and the capability set were associated with emotional well-being. Moreover, low job insecurity, professional development, and the capability set were associated with psychological well-being. Furthermore, precarious work conditions, job insecurity, low professional development, and low scores on the capability set were associated with low social well-being. Precarious work conditions and low scores on the capability set were associated with teachers’ intentions to leave. Two dimensions of precarious employment, namely salary and precarious work conditions, indirectly affected teachers’ emotional, psychological, and social well-being via the capability set.
Deformation measurement system for the fuselage of an unmanned aircraft vehicle

The paper presents a description of the research possibilities of the developed test stand for testing deformations of unmanned aerial vehicle fuselages. The test stand was developed to realize a project of a unique construction of an unmanned aerial vehicle. This design determined the requirements for the test stand. Therefore, part of the work was devoted to the description of the tested object. Due to the specific working conditions of the designed fuselage, the developed test stand allows for simulating the load corresponding to different conditions of the flight, e.g. hovering or progressive flight. Additionally, it is possible to change the forces acting on the fuselage simultaneously for all servos or each of them separately (if necessary). Finally, example results showing the displacement of control points in one of the observed parts of the fuselage are presented. The tests carried out using the developed test stand allowed to verify the maximum deformation of the fuselage. Based on the results obtained it is possible to define necessary modifications to increase the stiffness of the tested object, i.e. to reduce the level of deformations while keeping the mass requirements.