Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Path Planning Method of Intelligent Robot Based on A * Algorithm

With the development of science and technology, a new handling method has emerged in the industrial automation industry - handling robots. Handling robot vehicles are called AGVs (Automatic Guided Vehicles), which are multi-wheel drive robots. Produced by the interdisciplinary application, it integrates automatic control, mechanical devices, image transmission and processing, computer communication, electronic information and other disciplines and technologies. It is widely used in various aspects. The path planning and obstacle avoidance problem is the leading research problem in applying the handling robot system. It is of great theoretical and practical significance to study the handling robot's path planning and obstacle avoidance algorithm. In this paper, the grid method is used to simulate the environment of the formal path, and the A * algorithm is used to improve the algorithm. We establish the global congestion model and local collision model of the robot and establish the dynamic path planning problem of the robot. At the same time, the global congestion problem is done Analysis and processing, complete the anti-collision prediction and automatic obstacle avoidance strategy analysis, and use the A * algorithm to improve the ant colony algorithm results for comparison.
Performance Modeling of Scalable Resource Allocations with the Imperial PEPA Compiler

Advances in computational resources have led to corresponding increases in the scale of large parallel and distributed computer (PDC) systems. With these increases in scale, it becomes increasingly important to understand how these systems will perform as they scale when they are planned and defined, rather than post deployment. Modeling and simulation of these systems can be used to identify unexpected problems and bottlenecks, verify operational functionality, and can result in significant cost savings and avoidance if done prior to the often large capital expenditures that accompany major parallel and distributed computer system deployments. In this paper, we evaluate how PDC systems perform while they are subject to increases in both the number of applications and the number of machines. We generate 42,000 models and evaluate them with the Imperial PEPA Compiler to determine the scaling effects across both an increasing number of applications and an increasing number of machines. These results are then utilized to develop a heuristic for predicting the makespan time for sets of applications mapped onto a number of machines where the applications are subjected to perturbations at runtime. While in the current work the estimated application rates and perturbed rates considered are based on the uniform probability distribution, future work will include a wider range of probability distributions for these rates.
Towards acceptable emerging technologies for homemonitoring and care: a feasibility study with COVID-19 patients

Healthcare continuity and remote care are among the key components for tackling disease-related effects using technological solutions. People recovering from home need high-quality of care and timely monitoring, resembling hospital care. This study proposes the use of a new device for person - machine interaction for home monitoring. The system takes advantage of automatic interaction initiated by the device on detecting patients' symptoms and providing remote care in order to improve technology engagement features. The feasibility of the proposed system was tested in COVID-19 patients as a definitive case of stay-at-home care where the treatment depends on the current state of health and the severity of the symptoms. The study shows promising results in terms of usability. The vast majority of the answers are perceiving the system as useful (90.9%) and easy to use (95.5%) and the overall System Usability Score (SUS) of the system is 65.25. The system usage adherence was also promising for the quarantine period (on average 7.2 days) but dropped after that. However, the results from the clinical team interviews showed that there is a need for sufficient allocated time for clinicians to get acquainted with the system and for ED staff to explain the device to patients.
Towards Humanoids Using Personal Transporters: Learning to Ride a Segway from Humans

Human bipedal locomotion is efficient, robust and versatile, but typically reserved to reach targets in the close vicinity. As soon as larger distances have to be covered, humans tend to rely on wheeled modes of transport in the form of cars, bikes, scooters etc. Having the flexibility to choose a personal transporter (PT) such as a Segway when needed, is also an interesting option for humanoids operating in the real world, but it requires the ability to control a device that has its own complex dynamics. In this paper, we synthesize controllers for the the humanoid robot REEM-C to ride a Segway in simulation, motivated by human Segway riding. We perform motion capture experiments of a human riding a Segway and identify human whole-body behavior as well as the Segway's internal controllers. We then show that the REEM-C can successfully generate translational, rotational and mixed motions of the Segway in simulation. The Segway is controlled by targeted motions of the REEM-C using an inverted pendulum based LQR controller for pitch control and an admittance controller for the LeanSteer to command a yaw-rate. After these promising simulation results, the next step will be implementation on a real Segway and the REEM-C humanoid.
Tailoring Fe∕Ag superparamagnetic composites by multilayer deposition

Fe∕Ag granular multilayers were examined by magnetization and Mossbauer spectroscopy measurements. Very-thin (0.2 nm) discontinuous Fe layers show superparamagnetic properties that can be tailored by the thickness of both the magnetic and the spacer layers. Novel heterostructures, superparamagnetic and ferromagnetic layers stacked in different sequences, were prepared and the specific contribution of the ferromagnetic layers to the low-field magnetic susceptibility was identified.
A System-on-Chip for IoT Applications with 16-bit Tiny Processor

In an Internet of Things (IoT) system, many embedded devices are deployed to gather massive amounts of information. These devices may collect indexes of natural substances (air, soil, water) or physiological parameters to provide data for later assessment on environmental conditions or improving healthcare. Gathering this information requires embedded processors to execute lightweight tasks involving sensing and communication through a wireless channel. This paper presents a low-area System on Chip (SoC) capable of performing sensing tasks for IoT applications. The SoC contains an accumulated architecture 16-bit processor, 512-Byte and 1-Kilobyte Random Access Memory (RAM) blocks for data and instruction, a General-Purpose In-Outs (GPIO), and a Serial Peripheral Interface (SPI) and a programmer. The SoC has been synthesized in 65-nm Silicon-On-Thin-Box (SOTB) technology and occupies 350×600-µm2. The processor area represents only 3.56% of the total SoC. The implementation of FPGA (Altera Cyclone IV EP4CE115) costs 373 LUTs, 202 Flip-flops, and 2 Block RAMs.
Smart IoT System for Automatic Detection and Protection from Indoor Hazards: An Experimental Study

Electrical short circuits and gas leakages are responsible for most of the fire occurrences. Considering this problem, Internet of Things (IoT)-based smart sensors and relays have been proposed in this paper. Besides, an automated indoor safety mechanism has been introduced which consisted of various sensors, built up with Arduino with the monitoring mechanism of ThingSpeak via IoT. The purpose of this proposed model is to save living creatures and goods from fire incidents. Additionally, a comprehensive device has been built to avert this situation by disconnecting the power source promptly and hence, save lives and property within a short time period. The main feature of this model is – (i) the monitoring device can operate from a remote area, and (ii) it collects real-life data and for the purpose of storage, the collected data would be send to the cloud. However, collected data can be analyzed from various locations using smart devices e.g., smart phones, laptops, computers, and hence, real-time decisions can be taken as well as execute. This experimental study ensures power safety as a result of fire or gas hazards and an IoT-based data monitoring system has been built which would continuously monitor the leakage of flammable gases e.g., Liquefied petroleum gas (LPG), Carbon dioxide (CO2), Ammonia (NH3) from the surroundings. The proposed system has been planned for online monitoring of indoor safety measurement, and the parameters can give important as well as helpful information about the atmospheric record and security issues of that place. The application of the developed model has been widely used in various settings, including - (i) residence, (ii) industries, (iii) gas stations, (iv) automobiles, (v) power plants, (vi) research centers, (vii) commercial areas (e.g., shopping malls), and (viii) hospitals.
A Low-Profile Wideband Monopole Antenna for WBAN Flexible Applications

This paper presents a wideband monopole antenna with a low-profile structure for wireless body area network applications. The antenna features a rectangular monopole patch with circular and rectangular cuts and operates at 11.92 GHz to 21.66 GHz with a 9.74 GHz bandwidth. The electrical length of the antenna is 1. 2λ. The antenna is simulated with an approximate four-layer human body tissue model. A slight frequency shift and gain attenuation are observed affected by the tissue model. SAR values are also simulated with different distances between the antenna and the tissue model, which indicates the safe distance must be at least 20 mm.
A Linear to Circular Polarization Converter Based on Split-Rectangular Ring Frequency Selective Surface

A transmission polarization converter is described that is based on frequency selective surface (FSS) to convert the incident linearly polarized wave to a circularly polarized one. This polarization converter consists of four layers and contains two different unit cells. The structure has helped in achieving desired nearly a high transparency to two orthogonal components of transmitted waves while providing nearly 90º phase difference, as required for the generation of circularly polarized fields. The polarization converter offers axial ratio of less than 3 dB over 76.8%, from 9.2 GHz to 20.7 GHz. In addition, its working frequency band can still cover the range of 9.7 GHz to 20.1 GHz when the incident angle increases to 20°. Theoretical analysis and results of numerical investigations related to the structure are presented.
The Development of Computer Assisted Intelligent Sports Teaching and Automatic Management System

The information system can be applied to all aspects of the education field, and the full use of technology as a tool can promote the smooth progress of education reform. The purpose of this paper is to study the development of school sports computer teaching and automatic management system. On the basis of countermeasure analysis of physical education management in the construction of informatization level at home and abroad, the questionnaire survey method and mathematical statistics method are used to research and discuss the informatization teaching management of the Physical Education College of Normal University. Sports computer teaching and automatic management system, the study found that the integration of information construction into sports teaching management has had a positive impact and role. Through questionnaire data analysis, 136 teachers were satisfied with the system, most physical education teachers were highly satisfied with the use of the system, and most physical education teachers held affirmative and supportive attitudes towards the construction.
Reachability of Chen-Fliess series: A Gradient Descent Approach*

Common procedures for maintaining systems in a safe region of operation are based on the computation of reachable sets. The objective is to find the tightest set containing all outputs of a system with respect to a set of inputs. Most methods for computing reachable sets require the knowledge of a state-space model, however, input-output approaches are more appealing due to model complexities and uncertainties. Additionally, these days abundance of data gives support to the development of data-driven approaches. In this paper, reachable sets are computed for nonlinear systems that can be described as Chen-Fliess series. This approach relies on representing systems as weighted sums of iterated integrals studied using noncommutative formal powers series. Specifically, a gradient descent algorithm based on the Chen-Fliess formalism is proposed for computing output reachable sets. This algorithm requires extending the notion of Gâteaux derivatives to this domain together with conditions for its existence. The gradient descent algorithm based on this derivative is then shown to be well-posed. Illustrative examples are provided in the last section. These are compared against reachable sets computed using the mixed-monotonicity methodology.
Data-Out Instruction-In (DOIN!): Leveraging Inclusive Caches to Attack Speculative Delay Schemes

Although the cache has been a known side-channel for years, it has gained renewed notoriety with the introduction of speculative side-channel attacks such as Spectre, which were able to use caches to not just observe a victim, but to leak secrets. Because the cache continues to be one of the most exploitable side channels, it is often the primary target to safeguard in secure speculative execution schemes. One of the simpler secure speculation approaches is to delay speculative accesses, whose effect can be observed, until they become non-speculative. Delay-on-Miss, for example, delays all observable speculative loads, i.e., loads that miss in the cache, and preserves the majority of the performance of the baseline (unsafe speculation) by executing speculative loads that hit in the cache, which were thought to be unobservable. However, previous works have failed to consider how instruction fetching can eject cache lines from the shared, lower-level caches, and thus from higher cache levels due to inclusivity. In this work, we show how cache conflicts between instruction fetch and data accesses can extend previous attacks, and we present the following new insights: 1) It is possible to use lowerlevel caches to perform Prime+Probe through conflicts resulting from instruction fetching. This is an extension to previous Prime+Probe attacks that potentially avoids other developed mitigation strategies. 2) Data-instruction conflicts can be used to perform a Spectre attack that breaks Delay-on-Miss. After acquiring a secret, secret-dependent instruction fetching can cause cache conflicts that result in evictions in the L1D cache, creating observable timing differences. Essentially, it is possible to leak a secret bit-by-bit through the cache, despite Delay-on-Miss defending against caches. We call our new attack Data-Out Instruction-In, DOIN! , and demonstrate it on a real commercial core, the AMD Ryzen 9. We demonstrate how DOIN! interacts with Delay-on-Miss and perform an analysis of noise and bandwidth. Furthermore, we propose a simple defense extension for Delay-on-Miss to maintain its security guarantees, at the cost of negligible performance degradation while executing the SPEC CPU2006 workloads.
Cuckoo Node Hashing on GPUs

The hash table finds numerous applications in many different domains, but its potential for non-coalesced memory accesses and execution divergence characteristics impose optimization challenges on GPUs. We propose a novel hash table design, referred to as Cuckoo Node Hashing, which aims to better exploit the massive data parallelism offered by GPUs. At the core of its design, we leverage Cuckoo Hashing, one of known hash table design schemes, in a closed-address manner, which, to our knowledge, is the first attempt on GPUs. We also propose an architecture-aware warp-cooperative reordering algorithm that improves the memory performance and thread divergence of Cuckoo Node Hashing and efficiently increases the likelihood of coalesced memory accesses in hash table operations. Our experiments show that Cuckoo Node Hashing outperforms and scales better than existing state-of-the-art GPU hash table designs such as DACHash and Slab Hash with a peak performance of 5.03 billion queries/second in static searching and 4.34 billion insertions/second in static building.
Designing Self-Adaptive Software Systems with Control Theory: An Overview

The complexity of modern software systems is continuously growing, as well as the amount of data that is produced on a daily basis. This calls for sound and scalable approaches that can be used to tame such an emerging complexity. This tutorial aims at introducing the basic concepts of control theory that can be used to design self-adaptive systems. The tutorial is divided into two main parts. The first part discusses the modeling of discrete-time systems, and the design of controllers in the discrete-time domain. The second part presents a number of successful examples of the application of control-based approaches for the design of self-adaptive software systems.
Research on intelligent inspection technologies of expressway maintenance based on edge computing

Smart inspections of expressway maintenance are collected on road data through vehicle cameras. The vehicle processing equipment combines edge computing to preprocess the collected image to reduce the pressure of data transmission background data operations. The back-end combination of image recognition technology and deep learning theory to filter the collected pavement information, and finally realize road disease recognition. This paper is based on technologies such as images, image enhancement, and data expansion. Through the research of the FASTER RCNN and YOLO-V5 algorithms, the UNET and YOLO-V5 are finally used for object segmentation and recognition.
A Multi-Body Model of an upper-limb prosthesis for grip force estimation and related object interaction application

Hand amputation greatly affects the ability of a person to perform activities of daily living (ADLs). For this reason, prosthetic hands should present grasping characteristics to allow the manipulation of objects of different shapes and dimensions. This is the case of the Hannes prosthetic hand, an under-actuated myoelectric prosthesis able to adapt the grasping configuration to the object shape using the actuation of a single motor and the differential mechanism that characterize this device. In this paper, we present the development of a multi-body and multi-domain model of the Hannes hand, which was experimentally validated using an external grasp force sensor to compare the model outcomes to the actual results. The model is used to investigate the correlation between available measurements from the prosthesis and the stiffness of the grasped objects, passing by the exploration of one of the most challenging aspects for tendon-driven under-actuated prosthetic devices: the friction experienced in the transmission mechanism. Therefore, the current analysis leads to the development of a novel control strategy through the use of an object stiffness classifier. The work provides an alternative model-based approach to overcome the absence of force sensors in under-actuated prosthetic hands for object recognition tasks. The obtained results are promising with contained percentage errors.
Clinical efficacy and neurobiological correlates of electroconvulsive therapy in patients with clozapine-resistant/intolerant schizophrenia: study protocol of multi-site parallel arm double-blind randomized sham-controlled study

Background: A substantial proportion of patients with treatment resistant schizophrenia do not respond well or partially to clozapine, with a subset that does not tolerate an adequate trial of clozapine. Electroconvulsive therapy (ECT) is regarded as one of the augmenting options, but there is a lack of high-quality evidence for this practice. This protocol describes a double-blind randomised sham-controlled modified-ECT trial to evaluate its efficacy in patients with clozapine resistant/intolerant schizophrenia. The study also involves multimodal investigations to identify the response predictors and the mechanistic basis of modified ECT in this population. Methods: One hundred consenting schizophrenia patients with resistance/intolerance to clozapine referred by clinicians for ECT would be randomly assigned to receive true ECT or sham ECT at three study centers. Sham ECT would mimic all the procedures of modified ECT including anaesthesia and muscle relaxation, except the electrical stimulation. After a blinded course, non-responders to sham ECT would be offered open-label true ECT. Clinical assessments, neurocognitive assessments and multimodal investigations (magnetic resonance imaging [MRI], electroencephalography, heart rate variability, investigative transcranial magnetic stimulation-transcranial direct current stimulation, gene polymorphism) would be conducted at baseline and repeated after the end of the trial, as well as open-label ECT course. The trial would evaluate the improvement in positive symptoms (scale for assessment of positive symptoms) of schizophrenia as the primary outcome measure with prediction of this change by resting-state functional-MRI based brain-connectivity as the second primary objective. Registration: Clinical Trial Registry of India (Reg no: CTRI/2021/05/033775) on 24 th May 2021.
Towards Technology-Enabled Learning of Relational Query Processing

The database systems course has gained increasing prominence in academic institutions due to the convergence of widespread usage of relational database management system ( RDBMS ) in the commercial world, the growth of Data Science, and the increasing importance of lifelong learning. A key learning goal of learners taking such a course is to learn how SQL queries are processed in an RDBMS in practice . Most database courses supplement traditional modes of teaching with technologies such as off-the-shelf RDBMS to provide hands-on opportunities to learn database concepts used in practice. Unfortunately, these systems are not designed for effective and efficient pedagogical support for the topic of relational query processing. In this vision paper, we identify novel problems and challenges that need to be addressed in order to provide effective and efficient technological supports for learning this topic. We also identify opportunities for data-driven education brought by any effective solutions to these problems. Lastly, we briefly report the TRUSS system that we are currently building to address these challenges.