Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference

Mobile edge computing systems help improve the performance of computational-intensive applications on mobile devices and have to resist jamming attacks and heavy interference. In this paper, we present a reinforcement learning based mobile offloading scheme for edge computing against jamming attacks and interference, which uses safe reinforcement learning to avoid choosing the risky offloading policy that fails to meet the computational latency requirements of the tasks. This scheme enables the mobile device to choose the edge device, the transmit power and the offloading rate to improve its utility including the sharing gain, the computational latency, the energy consumption and the signal-to-interference-plus-noise ratio of the offloading signals without knowing the task generation model, the edge computing model, and the jamming/interference model. We also design a deep reinforcement learning based mobile offloading for edge computing that uses an actor network to choose the offloading policy and a critic network to update the actor network weights to improve the computational performance. We discuss the computational complexity and provide the performance bound that consists of the computational latency and the energy consumption based on the Nash equilibrium of the mobile offloading game. Simulation results show that this scheme can reduce the computational latency and save energy consumption.
Wearable Vital Sign Sensing Radar Antenna with Moving Clutter Immunity

This work adopts a self-injection locked (SIL) bistatic radar capable of achieving the properties of high-sensitivity, small form factor, and low power consumption. This architecture is ideal for wearable vital sign sensor, the primary circuits are an SIL tag consisting of an omnidirectional monopole antenna and a frequency selective surface (FSS), and a mobile receiver consisting of a left-hand circularly polarized (LHCP) antenna and a delay discriminator. Worn on the subject's chest, the mobile tag can sense the fluctuations on skins caused by cardiopulmonary activities, and the polarization of the radiating electric field is then converted by the FSS to send an LHCP wave to the remote handheld receiver. Benefiting from polarization diversity, the SIL tag can reject moving clutter in the environment. In the experiments, the 2.4 GHz prototype was not affected by the movement of the handheld receiver or a walking person in the environment. The cardiopulmonary information is still reliable.
Incorporating Industry into the Curriculum: Applied Learning in Computer Science

Singapore Institute of Technology’s (SIT) Integrative Team Project (ITP) uses Applied Learning to expose student teams to industry’s cutting-edge projects and industry-ready evaluation, combined with academic supervision and assessment. Industry propose projects that fulfil real industrial needs, while SIT ensures projects meet students’ academic learning objectives. ITP affords students an integration of theory and practice coupled with the acquisition of specialist knowledge and development of professional skills in their respective industry. This paper serves as a case study, beginning with the pedagogical underpinnings, moving through the various assessments and evaluations, and finishing with a discussion of issues encountered with corresponding preventions and mitigations. The aim is to provide a blueprint for course organization and assessment that incorporates academic excellence with industrial relevance.
Open Source Software Evaluation, Selection, and Adoption: a Systematic Literature Review

Background. Open Source Software (OSS) is experiencing an increasing popularity both in industry and in academia. Aim. We investigated models for the selection, evaluation, and adoption of OSS, focusing on factors that affect most the evaluation of OSS. Method. We conducted a Systematic Literature Review of 262 studies published until the end of 2019, to understand whether OSS selection is still an interesting topic for researchers, and which factors are considered by stakeholders and are assessed by the available models. Result. We selected 60 primary studies: 20 surveys and 5 lessons learned studies elicited the motivations for OSS adoption; 35 papers proposed several OSS evaluation models focusing on different technical aspects. This Systematic Literature Review provides an overview of the available OSS evaluation methods, highlighting their limits and strengths, based on the wide range of technicalities and aspects explored by the selected primary studies. Conclusion. OSS producers can benefit from our results by checking if they are providing all the information commonly required by potential adopters. Users can learn how models work and which models cover the relevant characteristics of OSS they are most interested in.
Myoelectric model-based control of a bi-lateral robotic ankle exoskeleton during even ground locomotion *

Individuals with neuromuscular injuries may fully benefit from wearable robots if a new class of wearable technologies is devised to assist complex movements seamlessly in everyday tasks. Among the most important tasks are locomotion activities. Current human-machine interfaces (HMI) are challenged in enabling assistance across wide ranges of locomoting tasks. Electromyography (EMG) and computational modelling can be used to establish an interface with the neuromuscular system. We propose an HMI based on EMG-driven musculoskeletal modelling that estimates biological joint torques in real-time and uses a percentage of these to dynamically control exoskeleton-generated torques in a task-independent manner, i.e. no need to classify locomotion modes. Proof of principle results on one subject showed that this approach could reduce EMGs during exoskeleton-assisted even ground locomotion compared to transparent mode (i.e. zero impedance). Importantly, results showed that a substantial portion of the biological ankle joint torque needed to walk was transferred from the human to the exoskeleton. That is, while the total human-exoskeleton ankle joint was always similar between assisted and zero-impedance modes, the ratio between exoskeleton-generated and human-generated torque varied substantially, with human-generated torques being dynamically compensated by the exoskeleton during assisted mode. This is a first step towards natural, continuous assistance in a large variety of movements.
QORAl: Q Learning based Delivery Optimization for Pharmacies

Home delivery is getting extremely popular since the rise of the smartphone generation and is being implemented in almost all sectors where consumer convenience is top priority. One such important sector is the pharmacy industry. The critical aspects of delivery systems targeted towards this sector is the correctness of the order and speed of delivery. Furthermore, optimisation of the delivery routes in a multi-source, multi-destination scenario increases the complexity of the problem considerably. This paper provides a Q-Learning based approach for optimising routes with minimum computation time, in the form of the QORAl algorithm. In addition, certain complications with respect to the application of e-commerce in healthcare have also been addressed with the PharmaQuick ecosystem.
Software Logs for Machine Learning in a DevOps Environment

System logs perform a critical function in software-intensive systems as logs record the state of the system and significant events in the system at important points in time. Unfortunately, log entries are typically created in an ad-hoc, unstructured and uncoordinated fashion, limiting their usefulness for analytics and machine learning. In a DevOps environment, especially, unmanaged evolution in log data structure causes frequent disruption of operations in automated data pipelines, dashboards and analytics. In this paper, we present the main challenges of contemporary approaches to generating, storing and managing the evolution of system logs data for large, complex, software-intensive systems based on an in-depth case study at a world-leading telecommunications company. Second, we present an approach for generating and managing the evolution of log data in a DevOps environment that does not suffer from the aforementioned challenges and is optimized for use in machine learning. Third, we provide validation of the approach based on expert interviews that confirm that the approach addresses the identified challenges and problems.
A Redundancy Eliminated Flip-Flop in 28 nm for Low-Voltage Low-Power Applications

A redundancy eliminated flip-flop (REFF) is presented in 28-nm LP process, targeting wide-range voltage scalability (1–0.3 V). The REFF removes redundant clock transitions to reduce dynamic power consumption and further eliminates redundant transistors with topological and logical methods while keeping it fully static and contention free. The measured power is reduced by 70%/59% with 0%/10% activity at 1-V compared to the TGFF, and 100 dies from five corners were functional down to 0.28 V.
Dual-Band Microstrip Resonant Sensor for Dielectric Measurement of Liquid Materials

A novel compact microstrip resonant dielectric permittivity sensor with dual-band operation is presented in this paper. The circuit makes use of a single stepped-impedance-resonator as the sensing element, working at the 2.45-GHz and 5.8-GHz ISM bands, under differential and common modes, respectively. Sensitivity of the circuit and resonator coupling at each band can be configured independently. Moreover, testing of the sample is done by using a single sensing region without the need of duplicating the sample location. Experimental measurements are carried out using a very small amount of liquids and the results are presented validating the theory. This work delivers a compact, reusable, label-free and non-destructive microwave device and paves a way for sensing dielectric properties of chemicals with accuracy due to the dual-band performance.
Flexion-Extension Wrist Impedance Estimation Using a Novel Portable Wrist Exoskeleton: a Pilot Study

Although the wrist plays a crucial role in performing interactive tasks, the wrist impedance has not been deeply studied so far. Moreover, all studies that aimed at estimating wrist impedance cannot be carried out in unstructured environments, thus limiting the gamut of possible research in this field. The major reasons underlying such limitations are the encumbrance and the non-portability of the robots used for this kind of applications. Within this work we presented the validation of a novel portable wrist exoskeleton to estimate the passive impedance of the wrist in Flexion-Extension (FE). To this aim, we enrolled 9 subjects whose wrist was passively moved around FE of ± 20deg by the device, characterized by 1 active degree of freedom. During the experimental sessions, we measured the joint torque and joint displacement, and we derived the angular velocities through numerical differentiation. Considering the wrist as a linear time-invariant second order mechanical system, we estimated impedance evaluating stiffness and damping coefficients, neglecting the contribution of the inertia due to the low speed of the movement applied by the robot. To this aim, we run a linear regression which allowed us to estimate stiffness and damping values. Passive stiffness estimated was equal to 1. $794\pm 0.514\mathrm{N}\mathrm{m}/\mathrm{r}\mathrm{a}\mathrm{d}$ for extension and $1.418\pm 0.445\mathrm{N}\mathrm{m}/\mathrm{r}\mathrm{a}\mathrm{d}$ for flexion, confirming also the wellknown difference between extension and flexion (p=0.027). Passive damping was found equal to $2.054\pm 1.202\mathrm{N}\mathrm{m}\mathrm{s}/\mathrm{r}\mathrm{a}\mathrm{d}$ for extension and 1. $403\pm 1.196\mathrm{N}\mathrm{m}\mathrm{s}/\mathrm{r}\mathrm{a}\mathrm{d}$ for flexion. These results are consistent with previous studies reported in literature, demonstrating the efficacy of our fully portable wearable wrist robot in estimating the wrist impedance around FE.
Continuous Pulse Rate Monitoring from Facial Video Using rPPG

Cardiovascular activity causes some imperceptible changes in different regions of human body. These changes set the stage for isolated systolic hypertension, diastolic dysfunction and heart failure, atrioventricular conduction defects and aorta valve calcification. Activity of heart, which is an indicator of physical health and lifestyle are required to be measured remotely and continuously. In this paper a system is proposed, which is able to estimate pulse rate of a subject remotely without any biological effects on it. Estimation is done by utilizing facial video of a subject. Our proposed framework utilize SLIC superpixels for ROI detection and adaptive least mean square method is adapted to suppress the effect of illumination variation. For performance verification we have tested our system on publicly available large database MAHNOB-HCI and shows that our method shows significant improvement than existing works.
Low Voltage Ride-through Strategy of Photovoltaic Power Generation Based on State-dependent Riccati Equation Technique

In current photovoltaic transmission systems, low voltage ride-though is a main challenge that restricts the development of photovoltaic power generation. In this study, a power system with large photovoltaic (PV) power penetration is considered, and nonlinear control strategies for PV power stations are designed to guarantee the operation of PV power system and provide enough reactive power for the grid. First, the dynamic model of PV stations is constructed, and the control objective is to enhance the robustness of the system against grid faults. Second, the state-dependent Riccati equation (SDRE) technique is used to solve the robust control problem. The SDRE technique reconstructs a nonlinear system to a linear-like structure, and controls the system through solving an algebraic Riccati equation (ARE)-like approach with state-dependent coefficient (SDC) matrices. Comparisons with conventional PI controllers are performed, and better control performance is achieved through the proposed strategy.
Person Following Mobile Robot using Pedestrian Dead-Reckoning with Inertial data of Smartphones

For a person following mobile robot, identifying the specific host to follow is challenging in presence of other people with similar features in its camera frame or field of view. In this paper we propose the use of Pedestrian Dead-Reckoning using the Inertial Measurement Unit (IMU) data from smartphone of host or user, which is used to localize the user in the map frame of the mobile robot. The movement and orientation of the user is used to calculate the path of the user and used as global targets for the mobile robot to follow. The mobile robot which is doing Simultaneous Localization and Mapping (SLAM), running on the Robot Operating System (ROS) framework follows the user as the user moves while it also implements obstacle avoidance locally. Since the smartphone is uniquely paired with the mobile robot, the ambiguity of identifying the host is also solved.
SREKF Based State of Health Estimation for Lithium-Ion Battery

State of health (SOH) estimation plays an important role in battery management system. This problem is addressed by introducing a second-order RC equivalent circuit model (ECM) and square root extended kalman filter (SREKF) in this work. According to the ECM, the ohmic resistance (R0) which represents the SOH is molded as a state vector, and lithium battery SOH is obtained by using the internal relationship between ohm resistance and SOH. Then the state of the resulting nonlinear dynamic system is estimated in real time by the SREKF which can guarantee the symmetry and non-negative characteristics of the state variance matrix by the square root method recursively. The verification results show that the SREKF algorithm can estimate the SOH parameter more accurately and reduce the maximum error of the ohmic resistance by 6% under 1C-rate constant current test compared with the EKF algorithm.
Human Arm Posture Optimisation in Bilateral Teleoperation Through Interface Reconfiguration

In this paper, we propose a method for improving the human operator’s arm posture during bilateral teleoperation. The method is based on a musculoskeletal model that considers human operator’s arm dynamics and the feedback force from the haptic interface (master), which is used to control a robotic arm (slave) in a remote environment. We perform an online optimisation to find the optimal configuration that has the longest endurance time with respect to muscle fatigue. Next, a trajectory is generated on the haptic interface in order to guide the human arm into the optimal configuration. The teleoperation is temporarily suspended by decoupling the master from the slave robot when the haptic device is being reconfigured. Afterwards, the loop is coupled again and the slave robot is controlled from the position where it stopped after the haptic interface guided the operator’s arm to the optimised configuration. The main advantage of the proposed method is that the human operator can perform the task with less effort, which increases the endurance time. To validate our approach, we performed proof-of-concept experiments on a teleoperation system composed of two Franka Emika robots, where one was serving as master and the other as slave.
Haptic Rendering Modulates Task Performance, Physical Effort and Movement Strategy during Robot-Assisted Training

Research on neurorehabilitation has emphasized that somatosensory information about the interaction with the environment during physical training is crucial to provoke brain plasticity. Despite this, only a small number of robotic devices provide haptic rendering of the virtual environment during neurorehabilitation exercises, the majority with simple structures. However, to provide realistic haptic rendering while supporting neurological patients to perform motor tasks, a transparent robot with several degrees of freedom is needed. In this study, we employed Disturbance Observers to achieve high transparency and fine haptic capabilities on the six DoF exoskeleton ARMin. We incorporated arm weight compensation to reduce the excessive physical effort required to move against gravity, promoting movement performance and directing the participants’ effort to the interaction with the environment. The effect of haptic rendering and its interaction with arm weight compensation were evaluated with six healthy participants. The task consisted of inverting a virtual pendulum and keeping it inverted. We found that haptic rendering of the pendulum dynamics affects the movement strategy the participants follow, i.e., they covered a significantly larger workspace with the end-effector at a significantly higher speed, and required moderate physical effort. The inclusion of arm weight support increased task performance and reduced participants’ effort, while it did not change the movement strategy. Our results suggest that haptic rendering, together with arm weight support, are potential interventions to enhance neurorehabilitation due to the added somatosensory information during motor training.
BS-UE Association and Power Allocation in Heterogeneous Massive MIMO Systems

We consider a downlink Heterogeneous Massive Multiple-Input Multiple-Output (HM-MIMO) system with the macro-base station (BS) having hundreds of antennas and each of the micro-BSs having tens of antennas. Two lower bounds and one approximation of the achievable per-user equipment (UE) rate in closed forms under this system are derived and compared in terms of network utility. Using the derived approximation, three BS-UE association approaches are proposed. Firstly, a simple, sub-optimal, and heuristic multiple BS-UE association approach is designed, which achieves around 25% utility performance improvement compared with the conventional maximum received signal strength (Max-RSS) approach. Secondly, based on the results given by this heuristic approach, a learning approach for BS-UE association using convolutional neural network (CNN) is introduced. After being fully trained, the CNN can take any new BS-UE configuration as input and provide a sub-optimal BS-UE association for that configuration directly. It has only a small performance degradation compared with the proposed heuristic approach. Thirdly, realizing that the BS-UE connection probability in the proposed CNN architecture can be considered as a power allocation ratio, a combined power allocation and association approach is proposed. Its performance achieves as high as 60% utility improvement compared with the Max-RSS association and is also comparable to that achieved by the max-min power allocation approach which requires more than $10000\times $ running time. It is remarkable that by using the gradients of the derived achievable per-UE rate approximation with respect to the power control coefficients, accurate target data is in fact not required for training in this approach.
Detection and Prevention Mechanisms for DDoS Attack in Cloud Computing Environment

For optimal use of cloud resources and to reduce the latency of cloud users, the cloud computing model extends the services such as networking facilities, computational capabilities and storage facilities based on demand. Due to the dynamic behavior, distributed paradigm and heterogeneity present among the processing elements, devices and service oriented pay per use policies; the cloud computing environment is having its availability, security and privacy issues. Among these various issues one of the important issues in cloud computing paradigm is DDoS attack. This paper put in plain words the DDoS attack, its detection as well as prevention mechanisms in cloud computing environment. The inclusive study also explains about the effects of DDoS attack on cloud platform and the related defense mechanisms required to be considered.
Capacitive Sensing-Based Continuous Gait Phase Estimation in Robotic Transtibial Prostheses

Gait phase plays an important role in controlling wearable robot. This paper focuses on the continuous gait phase estimation by using capacitive sensing method for one transtibial amputee wearing robotic prosthesis. First, the capacitive sensing signals are used to record muscles’ contraction and relaxation to detect two gait events (heel strike and toe off) as the start points (reset 0 rad) of one gait cycle. Secondly, continuous gait phase estimation is conducted based on the detected gait events (heel strike and toe off, respectively) with adaptive oscillators. In this study, the heel strike and toe off in each gait cycle can be detected with 100% accuracy based on linear discriminant analysis algorithm. The delay ratios for heel strike and toe off are less than 1% within one gait cycle. The root-mean-square errors of continuous gait phase estimation based on the different reset timings (heel strike and toe off) are 0.15 rad and 0.19 rad. The results show the feasibility to estimate the gait phase for amputee based on the residual muscles’ capacitive signals.