Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Portable Lower Limb Exoskeleton for Gravity Compensation during Walking

This paper presents a novel portable passive lower limb exoskeleton for walking assistance. The exoskeleton is designed with built-in spring mechanisms at the hip and knee joints to realize gravity balancing of the human leg. A pair of mating gears is used to convert the tension force from the built-in springs into balancing torques at hip and knee joints for overcoming the influence of gravity. Such a design makes the exoskeleton has a compact layout with small protrusion, which improves its safety and user acceptance. In this paper, the design principle of gravity balancing is described. Simulation results show a significant reduction of driving torques at the limb joints. A prototype of single leg exoskeleton has been constructed and preliminary test results show the effectiveness of the exoskeleton.
Reasoning about When to Provide Explanation for Human-involved Self-Adaptive Systems

Many self-adaptive systems benefit from human involvement, where a human operator can provide expertise not available to the system and perform adaptations involving physical changes that cannot be automated. However, a lack of transparency and intelligibility of system goals and the autonomous behaviors enacted to achieve them may hinder a human operator’s effort to make such involvement effective. Explanation is sometimes helpful to allow the human to understand why the system is making certain decisions. However, explanations come with costs in terms of, e.g., delayed actions. Hence, it is not always obvious whether explanations will improve the satisfaction of system goals and, if so, when to provide them to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of their impact on a human operator’s ability to effectively engage in adaptive actions. We then present a decision-making approach for planning in self-adaptation that leverages a probabilistic reasoning tool to determine when the explanation should be used in an adaptation strategy in order to improve overall system utility. We illustrate our approach in a representative scenario for the application of an adaptive news website in the context of potential denial-of-service attacks.
Modeling and Analysis of SMA Actuator Embedded in Stretchable Coolant Vascular Pursuing Artificial Muscles

This paper proposes a muscle-like SMA (Shape Memory Alloy) actuator with an active cooling system for efficient response. An SMA coil spring is embedded into a stretchable coolant vascular for soften structure of robots. In order to design a flexible, lightweight, and fast-response soft actuator with the SMA coil spring and coolant circulation system, a modeling based approach has been conducted. Analysis of coolant effects has been conducted in aspects of heating speed, cooling speed, and energy consumption based on both theoretical and empirical studies. From thermomechanical and heat transfer model between SMA and coolant, the actuation times in the case of heating and cooling phase have been estimated. From experimental results, Mineral oil is selected as the optimal coolant, and the maximum actuation frequency was measured as 0.5Hz for 40% contraction lifting 1kg.
Model Predictive Power Control for Bidirectional Series-Resonant Isolated DC–DC Converters With Fast Dynamic Response in Locomotive Traction System

High-frequency bidirectional dual-bridge series-resonant dc–dc converters have been attracted more attention in power electronic transformer (PET) due to high power density and efficiency. In this article, a model predictive power control method with phase shift modulation is proposed, to improve the dynamic response and robustness of the converter without load current sensor. In addition, the proposed method is insensitive to the resonant parameters in the adopted converter and can keep excellent dynamic characteristics even if the resonant parameter deviates from the desired operating point, which indicates good robustness. Effectiveness of the proposed control is verified on a 400-W scale-down experimental prototype with resistance load and inverter load, respectively. Compared with traditional voltage loop control and model predictive voltage control schemes, the proposed scheme can achieve a faster dynamic response of the output voltage in load and input voltage step change.
Single-Phase Consensus-Based Control for Regulating Voltage and Sharing Unbalanced Currents in 3-Wire Isolated AC Microgrids

A distributed control strategy is proposed to share unbalanced currents in three-phase three-wire isolated AC Microgrids (MGs). It is based on a novel approach where, rather than analysing the MG as a three-phase system, it is analysed as three single-phase subsystems. The proposal uses a modified single-phase $Q-E$ droop scheme where two additional secondary control actions are introduced per phase. The first control action performs voltage regulation, while the second one achieves the sharing of negative sequence current components between the 3-legs power converters located in the MG. These secondary control actions are calculated online using a consensus-based distributed control scheme to share negative sequence current components, voltage regulation, and regulating the imbalance at the converters’ output voltage to meet the IEEE power quality standards. The proposed methodology has the following advantages over other distributed control solutions, such as those based on the symmetrical components or those based on the Conservative Power Theory: (i) it achieves sharing of unbalanced currents, inducing smaller imbalances in the converters’ output voltages than those of other methods, and (ii) the sharing of the unbalanced currents is simultaneously realised in both the sequence domain and the a-b-c domain. The latter is difficult to achieve using other solutions, as will be demonstrated in this work. Extensive experimental validation of the proposed distributed approach is provided using a laboratory-scale 3-wire MG.
Optimized KLD-Based Fault Detection Method for Complex System Under Multi-Operation Conditions

With the increasing development of industrial technology, fault detection technology has been widely applied in many complex systems. Aiming at the problems of multi-operation conditions and the data length needed for fault detection using Kullback- Leibler Divergence (KLD), a novel fault detection method for complex systems based on optimized KLD under multi-operation conditions is proposed in this paper. In the first place, based on the historical data of the system, the complex operation conditions of the system are divided into several simple mutually exclusive operation conditions, and the division standard is established. In the next place, the optimal length can be quickly determined by autocorrelation analysis and applied to various operation conditions. After the next, in the training stage, the KLD values of all training data are calculated with the benchmark that is the initial optimal data length of each operation condition. And the maximum value is taken as the threshold for fault detection. Afterward, in the test phase, it starts by judging the type of operation condition that the data belongs to, then the corresponding KLD value is calculated, which is compared with the corresponding threshold, so as to determine whether the fault occurs. Eventually, this method is applied to the suspension system of the maglev train and respectively compared with the fault detection methods based on Euclidean distance or Mahalanobis distance. The results show that the proposed method possesses a low false alarm rate and high sensitivity.
Trajectory Prediction Based on Planning Method Considering Collision Risk

Anticipating the trajectory of Autonomous Vehicles (AV) plays an important role in improving its driving safety. With the rapid development of learning-based method in recent years, the long short-term memory (LSTM) network for sequential data has achieved great success in trajectory forecasting. However, the previous LSTM only considered forward time cues and did not reason on motion intent of rational agents. In this paper, we use planning-based methods follow a sense-reason-predict scheme in which agents reason about intentions and possible ways to the goal. In addition, the collision risk is considered, and the most appropriate future trajectory will be selected with the current state of the agent. We have compared our method against two baselines in highD dataset. Our experimental results show that the planning-based method improves prediction accuracy compared with the baselines.
Wireless Charging of AIMDs-Compensation Circuits

Different types of active implantable medical devices (AIMDs), their life and size of the battery and in turn size of AIMDs and ways to overcome the problems are explained in this paper. Life of battery decides the life of AIMD. Recharging the battery wirelessly is the only solution to limit the surgical insertion of the device for the second time i.e. to replace the AIMD. As well as rechargeable battery may reduce the size of AIMD as half of the size of pacemaker is occupied by battery. This paper also explains about the different compensation techniques to implement WPT system to increase efficiency of power transfer.
Comparison Studies on Hand Grip Measurement Techniques using FBG Sensor and sEMG

An important factor for research in understanding the hand arm system is quantifying the hand grip strength (HGS). HGS is the prognosticator of various biochemical conditions like muscle strength, upper-limb strength, neuromuscular function, bone mass density etc., this paper highlights the demonstration of Fiber Bragg Grating Hand Grip Device (FBGHGD) and Electromyogram (EMG) for measuring the hand grip strength. The FBGHGD is useful in evaluating the muscle activity. It is calibrated using compression testing machine, which shows the relationship between the forces exerted on FBG based HGD and shift in Bragg wavelength. FBGHGD shows 184.067 pm shift in wavelength for a maximum applied weight. The demonstration shows the FBGHGD is more accurate and provides a real time data which is useful in accessing several vital parameters like grip force, muscle fatigue, stamina index etc., which in turn serves as a reference in understanding the functional capabilities of skeletal muscle.
Phase-Only Filter Design with Application in OFDM Systems

Signal processing algorithms are widely used in wireless communication systems where it is used to process natural as well as man-made signals. Among these, filtering is one of such algorithm which is used to either enhance or suppress certain features in the regime of interest. The signal processing research community has not paid due attention to phase-only filtering, which is the focus of this paper. In this paper, we introduce a phase-only filter design formally and highlight its advantage over other conventional techniques through illustrative examples. We propose a novel method to design an optimal phase-only filter using the least squares approach. Additionally, we demonstrate the advantage of this design in an orthogonal frequency division multiplexing (OFDM) receiver for reducing the group delay distortion introduced by analog/digital baseband filters with sharp cut-off frequency responses. In contrast to the conventional methods such as equalization, the phase-only filter achieves this at a lower computational complexity. Monte Carlo simulation results are provided to substantiate the theory.
Energy-Optimal Cooperative Manipulation via Provable Internal-Force Regulation

This paper considers the optimal cooperative robotic manipulation problem in terms of energy resources. In particular, we consider rigid cooperative manipulation systems, i.e., with rigid grasping contacts, and study energy-optimal conditions in the sense of minimization of the arising internal forces, which are inter-agent forces that do not contribute to object motion. Firstly, we use recent results to derive a closed form expression for the internal forces. Secondly, by using a standard inverse dynamics control protocol, we provide novel conditions on the force distribution to the robotic agents for provable internal force minimization. Moreover, we derive novel results on the provable achievement of a desired non-zero inter-agent internal force vector. Extensive simulation results in a realistic environment verify the theoretical analysis.
Performance Analysis Between RunC and Kata Container Runtime

Kata container unifies the security advantages of virtual machines with the speed and flexibility of containers. It is designed along the Open Container Initiative’s specification to perform like containers, enforcing the workload isolation. Since Docker containers share the kernel of the host operating system, it is exposed to potential security threats. Kata container mitigates these security issues by introducing a lightweight virtual machine with separate kernels to create an isolated environment for the container. This research work aims at providing a quantitative analysis between the Docker and Kata container runtimes. The performance of both the technologies has been evaluated based on boot tests, system utilizations, and benchmarks, showing a detailed analysis of the security and performance tradeoff between both the container technologies.
R-Min: a Fast Collaborative Underactuated Parallel Robot for Pick-and-Place Operations

This paper introduces an intrinsically safe parallel manipulator dedicated to fast pick-and-place operations, called R-Min. It has been designed to reduce the risk of injury during a collision with a human operator, while maintaining high speed and acceleration. The proposed architecture is based on a modification of the well-known planar five-bar mechanism, where additional passive joints are introduced to the distal links in order to create a planar seven-bar mechanism with two degrees of underactuation, so that it can passively reconfigure in case of collision. A supplementary passive leg, in which a tension spring is mounted, is added between the base and the end-effector in order to constrain the additional degrees of freedom.A prototype of this new collaborative parallel robot is designed and its equilibrium configurations under several types of loadings are analyzed. Its dynamics is also studied. We analyze the impact force occurring during a collision between our prototype and the head of an operator and compare these results with those that would have been obtained with a rigid five-bar mechanism. Simulation results of impact during a standard pick-and-place trajectory of duration 0.3 s show that a regular five-bar mechanism would injure a human, while our robot would avoid the trauma.
A 20 Gb/s Latency Optimized SerDes Transmitter for Data Centre Applications

The throughput and the latency for practically larger capacitive loads are the two key factors that needs to be considered in the HPC (High Performance Computing), AI (Artificial Intelligence), high frequency trading applications. In this paper we are addressing the latency which needs to be low for any such applications. For this implementation we have re-looked into the functionality of each constituents of the transceiver and implemented the circuits to reduce the overall latency of the system without affecting the Serializer/Deserializer (SerDes) throughput. The proposed architecture replaces some of the conventional blocks of transceivers to reduce the latency. In this paper we describe the implementation in detail with circuit simulated upto extracted netlist level. This design of a transceiver with 20gb/s is sent for fabrication in TSMC 28nm HPC+ technology node. This paper is limited to transmitter portion of the work, where we see a major reduction in the associated latency compared to conventional transmitters.
Adaptive Operation-Based ALU and FPU Clocking

The operating clock period in modern circuits is designated under worst-case operating conditions, in order to ensure error free functionality. To accomplish this goal, designers take into account the timing of the circuit critical path that provides an upper limit for the clock rate. However, that limit imposes heavy performance penalties on the design, since the critical path is not frequently excited during runtime. In contrast with the prevailing methodology, the better-thanworst-case (BTWC) paradigm treats the circuit timing requirements in a more flexible way, as it does not commit to serve the demands of the worst-case scenario. In this work we develop a novel timing analysis methodology inspired by the BTWC approach. Instead of performing the standard critical path analysis, we focus on analyzing the timing requirements of each operation separately. In the sequel we specify an adaptive clock period that is derived by the timing requirements of each supported circuit operation. As a result, we are guaranteed error-free circuit functionality, as no timing violations of individual operations are ever allowed to occur. To this end no error correction mechanisms are required. We verify the proposed methodology by applying it on four different post-layout implementations of ALUs and FPUs. The results we obtain display an average 2.05x throughput increase compared to the implementations operating under the worstcase clock period. We also demonstrate that the area requirements of our methodology are trivial as the overhead is less than 2% of the original design.
Automatic Floorplanning for AI SoCs

In recent years, artificial intelligence/deep learning field is growing rapidly. The AI system-on-chip designs are actively developed. These designs often have properties of many IP blocks/embedded memories and complicated logic interconnect. In this paper, we propose an automatic floorplanning algorithm by using dataflow information and design exploration techniques to obtain high quality mixed macro and cell placement to handle numerous macros and complicated logic interconnect in AI SoCs.
Fast Doubling Algorithm for the Solution of the Riccati Equation Using Cyclic Reduction Method

A new iterative doubling algorithm for the solution of the discrete time Riccati equation is proposed. The algorithm is based on the Cyclic Reduction Method (CRM). The proposed doubling algorithm does not require non-singularity of the transition matrix and is faster than the classical doubling algorithm. The method can be applied to infinite measurement noise case, where the Riccati equation takes the form of the Lyapunov equation. In this case, the classical doubling algorithm is faster.
Canonical Switching Cell (CSC) Converter-Based Power Factor-Corrected Battery Charger for E-Rickshaw

Modern days e-rickshaws are becoming exceedingly popular due to reduction of manual work, cost-effectiveness, and as an alternate to traditional petroleum fuel-based rickshaws. Low-cost battery chargers used to charge these e-rickshaws have a power rating of 500–700 W and are nonpower factor-corrected. Therefore, in this article, a canonical switching cell (CSC)-based power factor correction (PFC), off-board charger, is proposed. The CSC converter improves the power quality and output voltage ripple of traditional e-rickshaw battery charger. The two-stage battery charger uses a CSC converter for shaping the input current and an isolated dc–dc converter regulates the charging current and voltage of an e-rickshaw battery. The CSC converter operates under continuous inductor conduction mode to realize PFC and dc–dc converter stage is based on a half-bridge converter controlled by pulsewidth control. Moreover, modeling of CSC converter in state-space is presented and a 600-W, 10-A e-rickshaw battery charger prototype is developed and its performance is thoroughly evaluated.
A Cylindrical Conformal Leaky Wave Antenna with Omnidirectional Beam

In this paper, a cylindrical conformal leaky wave antenna is proposed, which can provide omnidirectional radio wave coverage in horizontal plane. After investigating the propagation characteristics of cylindrical conformal waveguide, radiation performances of the proposed antennas with three different radii are analyzed. A coupled feeding structure is proposed to excite the proposed conformal antenna efficiently. Simulation results of the proposed antenna are given, which show the efficiency of the design,
Decentralized Collaborative State Estimation for Aided Inertial Navigation

In this paper, we present a Quaternion-based Error-State Extended Kalman Filter (Q-ESEKF) based on IMU propagation with an extension for Collaborative State Estimation (CSE) and a communication complexity of $\mathcal{O}(1)$ (in terms of required communication links). Our approach combines a versatile filter formulation with the concept of CSE, allowing independent state estimation on each of the agents and at the same time leveraging and statistically maintaining interdependencies between agents, after joint measurements and communication (i.e. relative position measurements) occur. We discuss the development of the overall framework and the probabilistic (re-)initialization of the agent’s states upon initial or recurring joint observations. Our approach is evaluated in a simulation framework on two prominent benchmark datasets in 3D.
Deep-Learning-Based Multiple Beamforming for 5G UAV IoT Networks

This article develops a novel hierarchical 5G Internet of Things network with unmanned aerial vehicles (UAVs) in the sky. In the proposed system, the leader UAV plays a vital role in the communication with ground base stations and other UAVs. The leader UAV relies on multiple beamforming to establish and maintain reliable broadband connections, which requires the location and altitude information of the UAV. Therefore, we propose a novel deep learning algorithm based on gated recurrent units and autoencoder for trajectory prediction and pose estimation. Simulation results show that this algorithm greatly improves the performance of the entire system, and has obvious advantages compared to traditional methods.
Path Planning Under Malicious Injections and Removals of Perceived Obstacles: A Probabilistic Programming Approach

An autonomous mobile robot may encounter adversarial environments in which an attacker tries to influence its decisions. Through physical or software-level attacks, some of the robot's sensors might be compromised—a special concern for self-driving vehicles. Motivated by this scenario, this letter introduces and studies the problem of planning kinematically feasible (and possibly efficient) paths with bounded collision probability in adversarial settings where the obstacles perceived online by the robot display two layers of uncertainty. The first is the “usual” Gaussian uncertainty one would obtain from a standard object tracker (e.g., an Extended Kalman Filter); the second is an additional layer of uncertainty that captures possible sensor attacks and describes the actual existence of groups of obstacles in the environment. We study the complexity of the problem and propose a general sampling-based solution framework that uses the Sequential Probability Ratio Test (SPRT) to check collision probability constraints along the computed trajectory. We also show how probabilistic programming languages (PPLs) can simplify programming common algorithms (such as RRT and Hybrid A*) for mixed uncertainty. In addition to providing an easy-to-use programming framework, our approach is shown to plan safer paths compared to a Naive Monte Carlo baseline when both approaches are allowed to use at most the same given number of samples to perform collision checks.