Miracle-dz/UniFAR-FTUs huggingface dataset:
Framework of virtual reality game on dual cognitive task for stroke rehabilitation

Dual cognitive task is a task that requires both motor and cognitive skills simultaneously. Due to the brain pathology, a stroke patient may have difficulties in performing dual cognitive task. In the field of serious games for rehabilitation one of the technique is the use of virtual reality. However, through our literature search, we found no virtual reality games designed specifically for dual cognitive task. Dual cognitive task rehabilitation requires a game design that is suitable for problems that are related to dual cognitive task and must also be suitable for stroke patient. Therefore, this paper will discuss the proposed framework design of a virtual reality game for dual cognitive task challenge for stroke rehabilitation. In order to design the framework, some known literature has been summarized. The focus of the study are performing dual cognitive task in stroke patients, virtual reality game and its challenges, game design element, game genre, virtual reality environment and also game theory for dual cognitive task rehabilitation. The expected result is the framework of virtual reality game on dual cognitive task for stroke rehabilitation.
Towards incentive compatible auction mechanism for electric vehicles bidding in microgrids

In this paper, we address the issue of Electric Vehicles (EVs) bidding at charging stations in Microgrids. EVs bidding issues have attracted growing attentions recently, as the increasing integration of EVs and the shortage of energy supply. Auction mechanisms have great potential to facilitate energy trading among EVs and charging station in an efficient and fair manner. Nevertheless, two essential problems are challenging when designing auction mechanisms in EVs charging market, i) a feasible auction mechanism should satisfy the property of incentive compatibility which ensure that the bidders could achieve highest utility if and only if submitting truthful bids and asks; ii) a robust auction mechanism should also prevent the auction results from being manipulated by the adversaries false-name bidding attacks, where the adversaries create multiple identities to compromise the auction results. To address the above challenges, we investigate a multi-round incentive compatible (MuRIC) auction mechanism to enable energy trading among EVs owners (buyers) and charging station (seller). Through theoretical analysis, we prove that MuRIC achieves properties of individual rationality, incentive compatibility and false-name proofness. The simulation results show that MuRIC achieves good performance regarding to buyers/seller satisfaction ratio and the winners distributions.
Experiment and controller design for two-wheeled robot with nonlinear damping and road disturbance

This paper address the problem of control an underactuated two-wheel robot to follow a desired tracking path under unknown road disturbance. Based on the robot practical kinematics and dynamics model, the unknown road damping force should be nonlinear and related with wheels' velocity. The solutions include a Lyapunov-based trajectory tracking control law and disturbance estimator. In mathematics aspect, the control algorithm is solving the position, orientation and velocity error's boundedness and convergence problem. In general, all errors should be approaching to equilibrium states. According to real robot model, the controller performance includes following aspects: i) working against unknown road disturbance, i) considering the saturation problem of position error, iii) computing the nonlinear pa­rameters in robot model. Finally, in order to validate the controller, an experiment system was desined which is based on Matlab software on host computer and Inverted Pendulum Two-Wheeled Robot with Arduino MCU (Microprogrammed Control Unit) and DC (Direct Current) motor as lower computer.
Evaluation of BAQ on Tiangong-2 interferometric imaging radar altimeter data compression

Tiangong-2 Interferometric imaging radar altimeter (InIRA) applies the technology of small incident angle and high precision interferometric phase measurement, which has the characteristics of wide swath, imaging and high accuracy height measuring. Compared with traditional altimeter, InIRA can realize much wider swath and can obtain SAR image of observed area, so there is a high requirement of onboard memory and downlink speed for InIRA. As a result, we need to investigate data compression technique for InIRA to reduce the data rate. As an efficient algorithm for SAR echoes data compression, Block Adaptive Quantization (BAQ) is widely used to spaceborne SAR system for raw data compression. The motivation of this work is to evaluate the influence of BAQ algorithm on interferometric coherence and sea surface height error for InIRA.
Retinal vessel segmentation using convolutional neural networks

Retinal vessel segmentation and extracting features such as tortuosity, width, length related to those vessels can be used in diagnosis, treatment and screening of many diseases such as retinopathy of prematurity, hypertension and diabetes. Therefore, automatic segmentation of vessels by computers will make the analysis of those diseases easier and will help during the screening, diagnosis and treatment processes. In this study, a solution based on convolutional neural networks (CNN) is proposed for automatic segmentation of retinal vessels. The proposed CNN model is tested on DRIVE dataset and a better performance than literature is achieved.
Estimation of attraction domains for nonlinear systems of special form

For a nonlinear system that can be represented as an aggregate of interacting linear subsystems, the problem of finding an estimate of the attraction domain of an equilibrium state is posed. The estimate is sought as a Cartesian product of invariant ellipsoids of the subsystems composing the system. The proposed method relies on results of absolute stability theory and reduces construction of the estimate to solving an LMI system.
Timing analysis for Diffie Hellman Key Exchange In U-BOOT using Raspberry pi

In Diffie-Hellman Key Exchange (DHKE), two parties need to communicate to each other by sharing their secret key (cipher text) over an unsecure communication channel. An adversary or cryptanalyst can easily get their secret keys but cannot get the information (plaintext). Brute force is one the common tools used to obtain the secret key, but when the key is too large (etc. 1024 bits and 2048 bits) this tool is no longer suitable. Thus timing attacks have become more attractive in the new cryptographic era where networked embedded systems security present several vulnerabilities such as lower processing power and high deployment scale. Experiments on timing attacks are useful in helping cryptographers make security schemes more resistant. In this work, we timed the computations of the Discrete Log Hard Problem of the Diffie Hellman Key Exchange (DHKE) protocol implemented on an embedded system network and analyzed the timing patterns of 1024-bit and 2048-bit keys that was obtained during the attacks. We have chosen to implement the protocol on the Raspberry-pi board over U-BOOT Bare Metal and we used the GMP bignum library to compute numbers greater than 64 bits on the embedded system.
Fluidical bending actuator designed for soft octopus robot tentacle

Soft actuation, due to its mechanical properties offer a complex motion that is not achievable for traditional mechanism. Thanks to that property it is often considered for bio-mimicking devices as many leaving creatures move by complex and distributed deformation. In this paper we propose a novel soft fluidical actuator designed to be used as a biomimicking tentacle in swimming octopus robot. The actuator has two degrees of freedom that enables bending in some range of directions and inherits the actuation effectiveness of single degree of freedom actuators while still able to control two of them. The actuator has been tested in terms of generated forces and motion capabilities and shows significant improvement regarding the state of the art actuators capable of completing the assumed task. The actuator enables the octopus robot to advance forward, change swimming directions and rotate around its primary axis. The paper presents the design, fabrication process and experimental verification of the proposed solution.
X band patch array antenna design for marine radar application

Horizontally polarized patch antenna array design is proposed for frequency modulated continuous wave (FMCW) marine radar applications. The array consists of series-parallel fed 96 elements printed on microwave substrate. The array configuration is optimized for minimum line loss and maximum aperture efficiency. The target frequency band is 9.3–9.4 GHz. Antenna parameters such as return loss, peak gain, half-power beam width and side-lobe levels are presented for marine radar applications.
Lithium-ion battery SoC estimation based on online support vector regression

Lithium-ion battery is a typical dynamic and nonlinear electrochemical system, and common battery model can't accurately describe its characteristics of dynamic changes, nonlinear and strong coupling. The online support vector regression machine can update the model online in real time under the limited sample, and it has the global optimal and good generalization ability. The working voltage and temperature are selected as input variables, and the state of charge is used as the output variable to train the algorithm model. The simulation results show that the online support vector regression can accurately predict the state of charge of the battery compared with the BP neural network, and has higher SoC prediction accuracy and stability.
Sliding Mode Based Dynamic State Estimation for Synchronous Generators in Power Systems

This letter deals with the design of a robust sliding mode observer for dynamic state estimation applied to synchronous generators in power systems. Assuming only the frequency deviation of the generator is measured via phasor measurement units, we use a robust sliding mode estimation technique to dynamically reconstruct the rotor angle and the transient voltage. The adopted estimation technique is insensitive to matched bounded uncertainties affecting the dynamics of the synchronous generator. A stability analysis and tuning rules for the observer are also provided. Numerical simulations confirm the validity of the approach.
Trading virtual machines to stabilize revenue in public clouds

By obtaining virtual machines (VMs) from infrastructure providers (InPs) according to the demand in public cloud services, service providers (SPs) can elastically provide network services to users. As the charging methods of VMs, reserved instance (RI) and on-demand instance (ODI) are widely used. For InPs, RI is more desirable than ODI thanks to easiness of estimating long-term revenue, risk aversion of occurring idle VM resources, and reduction of charging cost. In this paper, to improve the ratio of RI in VMs prepared by an InP, we propose VM trading methods in which idle RI of SPs with VM demand falling below the amount of contracted RI are applied to SPs with VM demand exceeding the amount of contracted RI. As the VM trading mechanisms, we investigate two approaches: RI with self-help effort (RISE) and RI with mutual aid (RIMA). Through numerical evaluation using the demand pattern of commercial VoD service, we show that the proposed VM trading methods decrease the number of VMs required for ODI by about 50% to 100% and increase the ratio of RI by about 10% to 70%.
DeepSource: Point Source Detection using Deep Learning

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - a deep learning solution - that uses convolutional neural networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio (SNR) of the original map and then uses dynamic blob detection to detect sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT images with a total of 300,000 sources, DeepSource is essentially perfect in both purity and completeness down to SNR = 4 and outperforms PyBDSF in all metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For natural-weighting we find a smaller improvement of ~40% in the PC score at SNR = 3. If instead we ask where either of the purity or completeness first drop to 90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the 4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration. Our results show that deep learning is a promising approach to point source detection in astronomical images.
Random finite set based data assimilation algorithm for dynamic data driven simulation

Computer simulation has long been used for studying and predicting behaviors of complex systems. With the recent advances in sensor and network technologies, the availability and fidelity of real time measurements have been greatly increased. This makes the new simulation paradigm of dynamic data driven simulation more and more popular. It can assimilate the real time measurements for much better analysis and prediction of complex systems. Data assimilation techniques are the foundation of the dynamic data driven simulation, but the traditional particle filter based data assimilation algorithms can't meet the actual application requirements. In this paper, we study how to utilize the real time measurements for the dynamic data driven simulation. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the standard data assimilation algorithms. The random finite set based measurement model and simulation model that are used in the data assimilation process are introduced. The detailed implementation of the random finite set based data assimilation algorithm is presented. The study case with anti-piracy is used to practically illustrate the proposed data assimilation algorithm. The effectiveness and accuracy of the algorithm are checked by experiments.
Application of Gauss process regression modeling based on NN-MIV for marine enzyme fermentation process

To overcome the problems of variable redundancy, long training time and low prediction accuracy in the soft sensing model for marine enzyme fermentation, a Gauss process regression (GPR) model based on NN-MIV is presented, which is named as GPR-NNMIV soft sensing model. Firstly, the NN-MIV variable selection method, combining neural network (NN) and mean impact value (MIV), takes into account both the internal contribution rate and the external contribution rate to get the most suitable input variables with the highest contribution rate, and reduces the number of variables and simplifies soft sensing model. Secondly, based on the NN-MIV method, a new Gauss process regression model is proposed, which does not only give out the soft sensing results but also gives the corresponding uncertainty simultaneously. Results show that the proposed GPR-NNMIV soft sensing model has higher accuracy of results and small confidence intervals compared with single Gauss process model.
Fair multi-resource allocation with external resource for mobile edge computing

We consider the problem of fair multi-resource allocation for mobile edge computing (MEC). In MEC, to execute tasks with demands on multiple types of computing resources in the edge servers, the users must upload their tasks over a single dedicated wireless communication link that exists outside the servers. For this environment, we design a multi-resource allocation mechanism that extends the notion of dominant resource fairness (DRF) to accommodate an external resource, called DRF-ER. It provides several highly desirable properties. First, DRF-ER is envy-free, as no user prefers the allocation of another user. Second, DRF-ER allocations are Pareto optimal, as no one can improve its allocation without decreasing that of the others. Finally, DRF-ER is strategy-proof, as no user has an incentive to lie about its resource demand. Large-scale simulation driven by Google cluster traces further shows that DRF-ER significantly outperforms a naive extension of DRFH, which is a well-known variant of DRF for multiple servers, leading to higher resource utilization.
Speed and heading control of unmanned surface vehicle based on IDA-PBC and L2 gain disturbance attenuation approach

An Interconnection and damping assignment passivity based control(IDA-PBC) with L2 gain disturbance attenuation control law is presented and simulation for speed and heading angle tracking of unmanned surface vehicles. Using interconnection and damping assignment passivity based control, the desired PCH structure is assigned to the closed-loop USV control system by energy shaping theory. Moreover, the L2 gain disturbance attenuation approach is applied to the PCH control of USV system. The simulation results show that the proposed approach can quickly arrive at a given signal and effectively restrain the disturbance. So the proposed approach has a certain practical value.
Downlink Training Overhead Reduction Technique for FDD Massive MIMO Systems

In this letter, a novel minimum mean square error (MSE) based channel estimation framework is proposed to reduce the downlink channel training overhead in frequency division duplexing massive multiple-input multiple-output systems, where the overhead reduction is achieved through training only a subset of antennas and by exploiting the spatial correlation between the antennas at the base station. Closed-form expressions of the analytical MSE and the asymptotic MSE of the system are obtained. Furthermore, a perfect match between theoretical and simulation results is observed, where the channel training overhead can be reduced by half with an acceptable performance.
H∞ containment control for multi-agent systems over Markovian switching topologies

Improving the robustness performance of networking agents against external disturbance is quite necessary for the design of control protocols. This paper considers the robust containment control problem for a group of agents with linear dynamics over Markovian switching communication topologies. Considering the random switching of the communication graphs, this paper adopts the assumption that a continuous-time Markov process governs the switching of the communication topologies. To achieve containment, a class of distributed state-feedback controllers based solely on the relative information between neighboring agents is proposed. By using tools from graph theory and matrix theory, the containment control problem for a group of agents under the proposed controller is converted into the robust stability problem for a high-dimensional stochastic system. Sufficient conditions in the form of linear matrix inequalities are then given to ensure the existence of the state-feedback containment controller. It is worth mentioning that the proposed controller enables the networking agents to fulfill the containment control task when the external disturbance is absent, and to maintain good H∞ robustness against disturbance when the external disturbance exists. A numerical simulation is finally performed on a group of helicopters to verify the theoretical results.
CoMID: Context-Based Multiinvariant Detection for Monitoring Cyber-Physical Software

Cyber-physical software delivers context-aware services through continually interacting with its physical environment and adapting to the changing surroundings. However, when the software's assumptions on the environment no longer hold, the interactions can introduce errors for leading to unexpected behaviors and even system failures. One promising solution to this problem is to conduct runtime monitoring of invariants. Violated invariants reflect latent erroneous states (i.e., abnormal states that could lead to failures). In turn, monitoring when program executions violate the invariants can allow the software to take alternative measures to avoid danger. In this article, we present context-based Multiinvariant detection (CoMID), an approach that automatically infers invariants and detects abnormal states for cyber-physical programs. CoMID consists of two novel techniques, namely context-based trace grouping and multiinvariant detection. The former infers contexts to distinguish different effective scopes for CoMID's derived invariants, and the latter conducts ensemble evaluation of multiple invariants to detect abnormal states during runtime monitoring. We evaluate CoMID on real-world cyber-physical software. The results show that CoMID achieves a 5.7–28.2% higher true-positive rate and a 6.8–37.6% lower false-positive rate in detecting abnormal states, as compared with the existing approaches. When deployed in field tests, CoMID's runtime monitoring improves the success rate of cyber-physical software in its task executions by 15.3–31.7%.
Preliminary experimental study on variable stiffness structures based on fiber jamming for soft robots

Soft robotics opened a new set of technological challenges in using soft materials to build robots. The ability of change stiffness is among the most important, because it enables the possibility to tune forces exchanged with the environment. Granular and layer jamming transition have already demonstrated to be a promising approach for developing variable stiffness structures, but no studies have been dedicated to specifically fiber jamming so far. In this paper, we report a preliminary comparative study on fiber jamming applied to cylindrical structures. Fibers of different materials have been used as filler and tested under bending conditions. The results show that up to 380% of stiffness increase can be achieved, in line with data collected on similar devices that use granular jamming. The role of the main mechanical and geometrical parameters have been discussed: elastic properties are fundamental, but optimal performance can be achieved only with ordered arrangements of the fibers; geometrical features seem secondary, but surface roughness has an important role in preventing sliding. This is a preliminary study, but it already defines a first set of guidelines that can help and promote future works on the development and integration of the fiber jamming in soft systems.
Mining ABAC Rules from Sparse Logs

Different methods have been proposed to mine attribute-based access control (ABAC) rules from logs. In practice, these logs are sparse in that they contain only a fraction of all possible requests. However, for sparse logs, existing methods mine and validate overly permissive rules, enabling privilege abuse. We define a novel measure, reliability, that quantifies how overly permissive a rule is and we show why other standard measures like confidence and entropy fail in quantifying overpermissiveness. We build upon state-of-the-art subgroup discovery algorithms and our new reliability measure to design Rhapsody, the first ABAC mining algorithm with correctness guarantees: Rhapsody mines a rule if and only if the rule covers a significant number of requests, its reliability is above a given threshold, and there is no equivalent shorter rule. We evaluate Rhapsody on different real-world scenarios using logs from Amazon and a computer lab at ETH Zurich. Our results show that Rhapsody generalizes better and produces substantially smaller rules than competing approaches.