Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Maximum Pushing Force of Robot Manipulators to Physically Assist Caregivers

Robotic use cases where a robot must perform a force application can lead to overloaded joints due to excessive external joint forces. This results in performance degradation or even hardware damage in the worst case. Such cases are conceivable, for example, in physical human-robot interaction, where the collaborating robot should remain relatively small and light, but large loads have to be handled several times a day in collaboration with humans, e.g., in nursing care. For this purpose, we present an approach to estimate suitable configurations of a predetermined task with a start and goal robot pose to generate a robot manipulator path which possesses suitable conditions to leverage the manipulator's maximum pushing force potential. We also evaluate different metrics to show that robot base placement plays a major role when utilizing the robot manipulator's pushing capability for different tasks. Based on the robot's configuration and/or placement, the pushing force potential can differ by several magnitudes.
Visual Feedback Control of Print Trajectory in FDM-Type 3D Printing Process

In recent years, additive manufacturing of FDM: Fused Deposition Modeling type 3D printing has been rapidly developed. However, failures in molding are inevitable, leading to loss of time and materials. In this study, we propose a motion control method that suppresses failures during the build process by using visual feedback control with an RGB camera. A camera installed near the nozzle detects the molding line and generates a new path from the coordinates obtained by the camera, thereby realizing path modification in real-time. In this paper, we conducted experiments to verify the effectiveness of the proposed path modification method and evaluated the tracking and modification effects. Moreover, we conducted a modeling experiment with visual feedback and confirmed the effectiveness of the vision-based path modification method to suppress failures beforehand. This vision-based compensation system is expected to contribute to automation and quality improvement of the additive manufacturing process and the production system.
Interactive Dynamic Characteristics of Angle Stability of Multi-Converters System

The investigation of the dynamic characteristics of multi-converter systems is usually based on detailed modelling of the converters. This requires detailed control-related information about the converters, which is not suitable for grid operators and third-party research institutions to perform practical investigations of multi-converter grids. This paper proposes a validation and operation-oriented investigation approach to construct a system dynamics matrix of a multi-converter system with information on the grid topology, grid impedance and converter’s setpoints. In this paper, a three-converters system is modelled and the information of the stable equilibrium surfaces is investigated. According to the continuity of the stable equilibrium surfaces, the grid-forming converter has better robustness to the loss of synchronisation of the grid-following converter. On the other hand, the grid-following converter is not able to maintain stable operation in case of loss of synchronisation of the grid-forming converter.
Fusion of Multisource Satellite AOD Products via Bayesian Maximum Entropy With Explicit A Priori Knowledge

Fusing multiple satellite aerosol optical depth (AOD) products to produce high-quality aerosol records is necessary for climate-related research. The Bayesian maximum entropy (BME) approach has unique advantages in this regard. However, the large accuracy differences and redundancy characteristics between aerosol products are not considered by BME resulting in limited fusion quality and large computational consumption. Therefore, in this study, we try to explicitly introduce a priori knowledge about the multisource AOD product accuracy, called the accuracy ranking matrix, into the BME fusion process to test whether the above issues can be alleviated. Eight publicly released aerosol products over China are used in the fusion experiments, and the fusion results are validated with ground-based measurements. Results suggest that, compared with fusion directly, the fusion accuracy and efficiency are significantly improved by introducing the accuracy ranking matrix; the proportion of satisfying the Global Climate Observing System (GCOS) accuracy requirements increases by 12.2%, and the consumption time decreases by 74.6%. Specifically, the accuracy improvement in vegetated and clean air conditions outperforms that in arid and polluted conditions, with the most significant improvement in clean conditions, where the GCOS fraction increased by 22.9%. Moreover, the spatial distribution of fusion results in typical regions indicates that the introduction of the accuracy ranking matrix makes the AOD spatial variation smoother. These results demonstrate that introducing the accuracy ranking matrix can generate higher quality AOD distributions with improved efficiency, which is expected to guide the fusion of AOD products or other remote sensing products.
ANN-LSTM Assisted Intrusion Detection for Next Generation Core Networks

The evolution of smart home automation technologies in Fifth Generation (5G) networks demand for near-instant Internet connectivity to modern conveniences. With so much good coming from technology, it's easy to forget that every item and platform has the potential to be a threat. Despite society's positive opinion of modern technology, cyber security dangers posed by modern technology are a serious hazard. Intrusion Detection System (IDS) is critical in monitoring and detecting intrusion threats in core networks. Traditional firewall approach on data filtering does not detecting all sorts of attacks in real time. This research intends to examine contemporary IDS research using a Deep Learning (DL) methodology, with a focus on datasets, DL methods, and metrics. IDS based on deep learning techniques are particularly good in effectively processing massive volumes of data and identifying any malicious behavior for effective management and prompt detection of these types of attacks. The intrusion detection system's detection rate was improved by focusing on false alarm rate and other performance indicators. The results of studies showed that the DL model LSTM classifier had the best accuracy rate 97%, followed by ANM 95% while the CNN classifier had the lowest accuracy rate 81%.
Software Compilation Using FPGA Hardware: Register Allocation

Malicious attackers are constantly attacking software compilers and attempting to exploit various security vulnerabili-ties. By executing carefully chosen machine instructions already present in the program, an attacker can perform harmful actions arbitrarily. In this paper, we propose hardware/software codesign techniques to perform software compilation steps in hardware, specifically the register allocation step on a Field Programmable Gate Array (FPGA). Our experiment incorporates two key features: 1) Advanced RISC Machine (ARM) instruction set architecture (ISA)-based register allocation algorithms to calculate variable liveness as well as map virtual registers to physical registers and 2) the feasibility of executing the register allocation algorithms on a Cyclone V FPGA. Our experimental results show the timing efficiency and resource efficiency - while diminishing security risks - when performing register allocation of the gcd program on the FPGA.
Multilevel Security Model for Secure Information Flow Inside Software Components Employing Automated Code Generation

The development of safety critical systems with substantial communication to the outside world opens attack vectors through information flow over communication interfaces, directly or indirectly connected to the outside world. Software deployed in electronic control units must therefore provide interfaces for secure information exchange. This holds true for software components executed on the same processing unit or on different processing units and connected over bus systems. Malicious agents can cause incorrect data to be transmitted, resulting in unwanted and potentially harmful behavior of system components. Implementing checks for correct information reception inside embedded software is complex and error-prone. In this paper, a multilevel security model is introduced and used for static code analyses, tracing system variables and their potential harmful impact in case of malicious intent. This model is subsequently used for instance analysis and providing fixing suggestions. We evaluate our approach using a sample automotive use case with modeled components of different security levels. The model is able to automatically generate code for dynamic information flow tracking for a given subset of variables.
Machine Learning Models for Channel Status Classification in M-Mimo Systems Using Limited CSI Feedback

In next generation networks, knowing if a channel has a Line of Sight (LOS) path between the transmitter and the receiver is becoming increasingly important. For example, researchers have optimized channel estimation and wireless localization algorithms for both LOS and Non-LOS (NLOS) scenarios. Knowing the LOS status of a channel will allow system performance enhancement by employing the best algorithm available. This study explores the use of various machine learning classifiers to identify the LOS status of simulated massive-MIMO channels. The classifiers make their predictions based on limited Channel State Information (CSI) feedback received at the base station. This study identifies and properly manages the class imbalance problem present in LOS/NLOS identification. Promising results are achieved and demonstrated using a synthetic benchmark.
A 223-GHz FMCW Imaging Radar With 360° FoV and 0.3° Azimuthal Resolution Enabled by a Rotationally Stable Fan-Beam Reflector

A mechanically scanned high-resolution 2-D imaging radar system with full 360° field of view (FoV) is presented. The system is composed of a multifunction 222–228-GHz frequency-modulated continuous-wave (FMCW) radar and a moment of inertia optimized 3-D-printed offset reflector antenna. The reflector geometry is made from an elliptic toroidal surface which is shown to produce a high-gain fan beam with an 11:1 aspect ratio. The reflector is rotated at speeds up to 10 Hz for high-throughput imaging. The wobble generated by the high-speed rotation is suppressed by optimizing the mass distribution of the reflector body. It is fabricated in-house using a low-cost table-top fused deposition modeling (FDM) 3-D printer. The reflecting surface is metallized with a silver-coated copper paint and processed using simple steps to ensure smoothness and accuracy. The reflector achieves beamwidths of 4.6° in elevation and 0.42° in azimuth, with a peak gain of 38.7 dBi. The 3-dB azimuthal resolution of the radar two-way beam is measured to be 0.3°. The system is tested in real-world scenarios, and the resulting images and videos are of unprecedented quality due to the large area of coverage, high resolution, high speed, and excellent dynamic range. To the best of our knowledge, this is the first demonstration of a compact radar system that is comparable in resolution, speed, and angular coverage to lidars, while offering higher dynamic range and immunity to severe weather and lighting conditions.
Spectrum Sensing in Cogintive Radio Using Cooperative Spectrum Sensing

Spectrum sensing is an essential part of cognitive radio's ability to protect licenced users from harmful interference and find available spectrum for optimal spectrum use. The main objective of this research is to assess the spectrum sensing abilities of cognitive radio networks and to identify possible interference between these networks. The secondary goal is to study how these networks can be used in order to provide more efficient spectrum utilisation. In practise, receiver ambiguity, shadowing, and multipath fading all have a negative impact on detection performance. Cooperative spectrum sensing, which makes use of spatial diversity to enhance detection performance, has been created to lessen the impact of these problems. While cooperative sensing might result in cooperative overhead, it can also produce cooperative gain, including increased detection effectiveness and lower sensitivity requirements. Coop sensing is the cooperative process of observation, whereas uncoop sensing refers to when all of the data is gathered by each node. Overhearing refers to all the extra sensing time and energy devoted to knowing more than just your own node, as well as any performance loss resulting from this extra processing. When compared to svm, we get 90% efficiency from math lab.
A Set-Oriented Perspective on Solving Counting Problems.

For the Learning of Mathematics 34, 2 (July, 2014) FLM Publishing Association, Fredericton, New Brunswick, Canada The purpose of this article is to present and describe one particular perspective on solving counting problems (a setoriented perspective) that emerged during a study involving post-secondary students. The notion of a set-oriented perspective is presented both theoretically and empirically and is integrated with Harel’s (2008a, 2008b) ways of thinking. I make the case that such a perspective offers a valuable lens through which to study the learning and teaching of counting and may be an effective factor in helping students correctly solve counting problems. There is clear evidence in the literature that students at all levels struggle with solving counting problems correctly. Many researchers have identified student difficulties with combinatorial topics and reported low success rates (e.g., Eizenberg & Zaslavsky, 2004), and others speak to the overall difficulty of teaching and learning counting (Annin & Lai, 2010; Martin, 2001; Tucker, 2002). Some researchers have taken strides toward helping students become more successful at solving counting problems (e.g., Batanero, Navarro-Pelayo & Godino, 1997; Eizenberg & Zaslavsky, 2004; English, 1991; Lockwood, 2011; Maher, Powell & Uptegrove, 2011), but there remains a need for further examination of the ways of thinking that students bring to counting problems. In particular, the current combinatorics education literature has not explicitly highlighted the importance of focusing on sets of outcomes in students’ solving of counting problems. A set of outcomes is the set of things (objects, arrangements, partitions, etc.) a student is trying to count, the cardinality of which is the answer to the given counting problem. I call this focus on outcomes a set-oriented perspective on counting, which is a way of thinking about counting that involves attending to sets of outcomes as an intrinsic component of solving counting problems. The point is that by adopting such a perspective, in which facility with outcomes is a natural part of how they approach and solve counting problems, students may avoid common pitfalls such as incorrectly applying a formula, inadvertently overcounting, or confusing issues of order. I elaborate this perspective throughout the remainder of the article, illustrating the ideas with examples from my research.
Confocal-Cavity Terahertz Gyrotron based on Sheet e-beam

Serving as one of important sources of terahertz, the output and efficiency is limited by the size of devices and ‘unemployed’ electrons of the beam. To achieve higher electron efficiency and output power, the sheet electron beam is introduced in gyrotrons. We here present a design of a gyrotron with a quasi-optical confocal cavity which proves to possess good mode selectivity with the sheet electron beam. Besides, 3-D particle simulation is also carried out. The modified gyrotron is working at 220GHz, reaching 15kW output and 15% efficiency with lower current. In the future, the improved structure may be applied in frequency-tunable gyrotrons with high power.
Thermography: Features and utilization of thermal infrared camera and its application on human body in sports medicine

The purpose of this paper is to describe and study the capacity, potential and limitations of using thermal imaging camera to indicate the effects and track the changes caused by the physical exercises performed in the way that the certain part of body and muscles are stimulated. The methodology described here can be used as a reliable tool to prevent sport injuries and to track their recovery or showing quality of sportsmen training.
IoT Framework for Real Time Weather Monitoring using Machine Learning Techniques

Weather forecasting and weather warnings are used to protect human lives and property. Temperature, Outlook, Humidity, and Wind forecasts are critical for farmers, as well as traders in product markets. Since weather data analytics necessitates extreme precision, high-performance computing is required to handle the massive amount of data. The significant variability of climatic observations obtained in a day makes weather forecasting difficult. The objective of this project is to forecast the weather parameters for the next 24 hours using Auto ARIMA model and to use machine learning techniques to reliably predict the weather. Machine learning predicts the weather conditions for the day using strong and highly significant results based on current data. A cost effective IoT frame work is designed to read the real time input using sensors integrated with Arduino platform. By inputting average temperature, humidity, pressure, and other variables, decision trees and the Random Forest Algorithm will be utilized to predict events such as fog, rain, dry, windy, clear, breezy, and thunder. The algorithm is evaluated based on various performance metrics that include precision, recall, F score and accuracy.
Long-Period Grating Fiber Optic Sensors Coated With Gold Film and Pulse-Electroplated Iron–Carbon Layer for Reinforcing Steel Corrosion Monitoring

Long-period grating (LPG) fiber sensors sputter coated with an internal gold film and pulse electroplated with an external iron–carbon coating are characterized for reinforcing steel corrosion monitoring. The surface feature of the gold film was analyzed by using atomic force microscopy and microscopy. The transmitted light spectrum of iron–carbon coated and the microstructure of the iron–carbon coating was examined by using scanning electron LPG fiber sensors was measured using an optical instrument, and the corrosion degree of the iron–carbon coating is simultaneously measured with electrochemical methods. Experimental results demonstrate that corrosion of iron–carbon coating leads to a shift of the resonant wavelength, which is linearly correlated with the corrosion penetration depth of the iron–carbon coating. The fiber optic corrosion sensors investigated in this study exhibit better performance than those with inner silver nanowire-/graphene-based or silver-based films and outer direct current-electroplated iron–carbon coatings.
Automated Cerebral Vessel Segmentation Using Deep Learning for Early Detection of Cerebrovascular Diseases

Cerebrovascular diseases are a group of neurological disorders that result from weakened, blocked, or extravasated blood flow in the cerebral arteries. Although cerebrovascular diseases are the third leading cause of death worldwide, they are the primary cause of disability after the disease. Therefore, accurate cerebral vessel segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) data are crucial for clinical applications such as early disease diagnosis and surgical planning. This study focuses on vessel segmentation of major cerebral arteries using deep learning-based technologies, which have gained significant popularity in imaging problems and achieved unimaginable success. The study consists of two stages. In the first stage, we created a labelled dataset by combining Hessian-based filters and various image processing algorithms. In the second stage, we applied state-of-the-art architectures commonly used in segmentation problems, such as U-Net, ResUNet, ResUNet++, and TransUNet, to the vessel segmentation problem and compared their performance. Our experimental results demonstrate that the ResUNet++ architecture achieved the highest score in vessel segmentation, particularly on major cerebral arteries, with a mean intersection over union (mIoU) score of %91.6 compared to other architectures. These findings highlight the potential of deep learning-based approaches for accurate and efficient cerebral vessel segmentation in many clinical applications.
Multi-Step-Ahead Stock Index Prediction Based on a Novel Hybrid Model

Accurate prediction of stock prices is crucial both for government policy-making and for market participants to make investment decisions. This paper proposes an intelligent financial time series forecasting model. The model integrates Ensemble Empirical Mode Decomposition (EEMD), Least Squares Support Vector Regression (LSSVR) and weighted k-nearest neighbor (KNN), and proposes a new decomposition-cluster-ensemble stock price prediction method. This model is innovative in the construction of the feature extraction process, and proposes an improved algorithm that is more effective than the simple KNN prediction. The empirical results confirm the prediction effect of the model on Chinese stock index.
Interaction of Terahertz Waves with the RNA Hairpin

The vibrations of low frequencies and the functional groups of nucleic acid molecules, hydrogen bonds, van der Waals, and other weak intermolecular interactions are in the THz band. Theoretically, nucleic acid molecules are capable of interacting with THz waves. The vacuum electron THz source can be used to study the interaction of THz waves with nucleic acids. This paper simulates the effect of the THz waves on the binding energy of the folded WT-30nt. Molecular dynamics (MD) simulations show that the THz waves at 4 THz, 23.8 THz, and 37.4 THz can weakly affect the stability of WT-30nt, while the others cannot. This suggests that even THz waves with stronger amplitudes can rarely affect the binding ability of double-stranded RNAs by affecting hydrogen bonds, and it is estimated that THz waves would have difficulty affecting the stability of RNAs with more complex structures and more nucleotides.
Integrating Conditional Shape Embedding with Generative Adversarial Network -to Assess Raster Format Architectural Sketch

Automatic assessments of building plans are uncommon in the early design stages, especially when schematic sketches are in raster format. Existing design evaluation tools, such as fire code reviewers, primarily evaluate vector format images that contain complete building information in the late design stage. These tools use conditional shape-embedding techniques to analyze the vector images. However, there are limitations to identifying and evaluating drawings through vector-shape relationships. Our research aimed to develop tools that can automatically assess schematic sketches in raster format to overcome the limitations of existing tools. We integrated a conditional shape-embedding tool, named Shape Machine, to assess vector images, with machine learning techniques to assess raster sketches. This integration enables the evaluation of fire evacuation sketches in the early stages of the design process, thereby improving design efficiency and reducing costs. Moreover, in the future, this integration could allow the evaluation of designs in multiple image formats.