Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay-Dependent LMI Conditions for Stability of Stochastic Systems with Delay Term in the Form of Stieltjes Integral

Stochastic LTI system is considered with a delay term described by Stieitjes integral. This includes systems with discrete or distributed delays. Two Lyapunov-based methods for the asymptotic mean square stability are presented that lead to sufficient conditions in the form of linear matrix inequalities (LMIs). The first one employs neutral type model transformation and augmented Lyapunov functionals. Differently from the existing LMI stability conditions based on neutral type transformation, the proposed conditions do not require the stability of the corresponding integral equations. Moreover, it is shown that in the existing LMI stability conditions based on simple (non-augmented) Lyapunov functionals, the stability analysis of the integral equation can be omitted. The second method is based on a stochastic extension of simple Lyapunov functionals depending on the state derivative. Numerical examples give comparison of results via different methods.
Deep Reinforcement Learning Based Self-Configuring Integral Sliding Mode Control Scheme for Robot Manipulators

This paper deals with the design of an intelligent self-configuring control scheme for robot manipulators. The scheme features two control structures: one of centralized type, implementing the inverse dynamics approach, the other of decentralized type. In both control structures, the controller is based on Integral Sliding Mode (ISM), so that matched disturbances and uncertain terms, due to unmodeled dynamics or couplings effects, are suitably compensated. The use of the ISM control also enables the exploitation of its capability of acting as a “perturbation estimator” which, in the considered case, allows us to design a Deep Reinforcement Learning (DRL) based decision making mechanism. It implements a switching rule, based on an appropriate reward function, in order to choose one of the two control structures present in the scheme, depending on the requested robot performances. The proposed scheme can accommodate a variety of velocity and acceleration requirements, in contrast with the genuine decentralized or centralized control structures taken individually. The assessment of our proposal has been carried out relying on a model of the industrial robot manipulator COMAU SMART3-S2, identified on the basis of real data and with realistic sensor noise.
Hardware acceleration of support vector machine based on high level synthesis

Hardware accelerator design of machine learning algorithms is of great significance since huge amount of data are generated continuously and the computation load keeps increasing fast. Traditional solutions which rely on general purpose processors like X86 CPUs and ARM embedded processors cannot achieve high computation performance and energy efficiency due to lack of adaptability to specific algorithms. Domain-specific hardware accelerator is a promising alternative solution since the hardware is specifically designed to deal with one type of problems, the computation performance and energy efficiency can outperform traditional solutions by more than one order of magnitude. In this paper, we have proposed a hardware accelerator for support vector machine based on high level synthesis. Through the proposed loop dependence analysis and batch processing method, the speedup can reach about 153x on Xilinx ZC706 with unroll factor of 160, which is very close to its limit. This hardware implementation can be used in data centers as a coprocessor to accelerate the data processing speed in many classification applications.
Multi-agent structured optimization over message-passing architectures with bounded communication delays

We consider the problem of solving structured convex optimization problems over a network of agents with communication delays. It is assumed that each agent performs its local updates using possibly outdated information from its neighbors under the assumption that the delay with respect to each neighbor is bounded but otherwise arbitrary. The private objective of each agent is represented by the sum of two possibly nonsmooth functions one of which is composed with a linear mapping. The global optimization problem consists of the aggregate of the local cost functions and a common Lipschitz-differentiable function. In the case when the coupling between agents is represented only through the common function, we employ the primal-dual algorithm proposed by Vü and Condat. In the case when the linear maps introduce additional coupling between agents a new algorithm is developed. In both cases convergence is obtained under a strong convexity assumption. To the best of our knowledge, this is the first time that this form of delay is analyzed for a primal-dual algorithm in a message-passing local-memory model.
Biomedical Named Entity Recognition Based on Hybrid Multistage CNN-RNN Learner

This current research presents an inventive multilevel named entity recognition scheme for explaining the confrontation with biomedical entity recognition which based on divergent algorithms. The presented scheme contains multilevels, which enables Biomedical entity recognition tasks to extract and identify important biomedical concept: DNA, RNA, CELL-LINE, CELL-TYPE, PROTEIN, and O classes with ease. The BioNLP/NLPBPA 2004 challenge datasets have been used and evaluated, resulted in promising outcomes in terms of biomedical recognition model performance.
SoundMark: Accurate Indoor Localization via Peer-Assisted Dead Reckoning

Pedestrian dead reckoning enables pervasive indoor localization without a site survey on fingerprints or an intensive deployment of infrastructures. But accumulated errors in dead reckoning limit the spread of pervasive indoor location-based services. Existing landmark-based approaches mostly rely on resetting the user’s position with the landmark position only when the user is detected while on arrival at a landmark. However, such methods are still restricted by the specific movement patterns and sparse landmark distributions so that the opportunity for position calibration is limited. In this paper, an accurate peer-assisted localization system (called SoundMark) on a smartphone with no prior infrastructure or fingerprinting is proposed. It calibrates mobile user’s dead reckoning position by leveraging the location constraints between another stationary user who arrives at a landmark. To detect whether a user arrives at a landmark, motion pattern is extracted by fusing the multiple sensors. Then, user activity in the landmark is decomposed to determine whether the user is stationary for performing audio ranging. Besides, SoundMark also applies a mobility-induced time-difference-of-arrival-based audio ranging to extract the location constraints between the peers for localization. SoundMark is implemented on the Android platform for evaluations. The results show that the accuracy of proposed peer-assisted localization is within 2.1 m at the percentage of 80%.
Distributed Linear Quadratic Regulator for the Synthesis of a Separable Terminal Cost for Distributed Model Predictive Control

In distributed model predictive control the global optimization problem is split into simpler problems through the use of decomposition methods. To enable such a decomposition, the problem is required to be separable, a property which is in general fulfilled when dealing with networks of dynamically coupled systems. The standard linear quadratic regulator used to provide stability through a terminal cost function, however, destroys the problem's inherent structure and can therefore not be directly employed. In this paper an alternative suboptimal design providing both a stabilizing terminal cost function which preserves the network structure and a nominal distributed control law for the unconstrained system is presented. The proposed iterative algorithm is based on the standard Riccati iteration and on semidefinite programming. The global synthesis problem is itself amenable to distributed optimization and can hence be executed online. The controllers can therefore easily adapt to general network changes enabling full Plug&Play capabilities. The implemented method is tested numerically on a chain of inverted penduli network.
Design of Adaptive Sliding Diagonal Recurrent Cerebellar Model Articulation Controller for Direct Torque Control Systems of an Induction Motor

A novel adaptive sliding diagonal recurrent fuzzy cerebellar model articulation controller (ASDRC) is proposed by this study. ASDRC includes the inputs by a sliding surface into a diagonal recurrent fuzzy cerebellar model articulation controller (DRCMAC). The ASDRC enables cerebellar model articulation controller (CMAC) to exhibit both static and dynamic characteristics, indicating that the use of DRCMAC improves the disadvantage of conventional CMAC while exhibiting the advantages of fuzzy CMAC (FCMAC). Regarding the proposed ASDRC, the adaptive update law determining the memory weights, means of Gaussian functions, and standard deviations of Gaussian functions is yielded by the Lyapunov stability theory; moreover, the gradient descent method is applied to yield the recurrent weight update law. Using the adaptive update law and recurrent weight update law of the ASDRC to implement online adjustment ensures system stability. To demonstrate the performance of the proposed ASDRC, this study applies it to the direct torque control (DTC) systems of an induction motor to perform experiments. The root-mean-square error is used as an assessment indicator to compare the results of the proposed controller with those of the FCMAC. The experimental results prove that the ASDRC has more excellent response, and its performance is superior to that of the FCMAC.
Adaptive State-Space Neuro Fuzzy Control

This paper proposes a new general adaptive state- space Neuro-Fuzzy control framework. It combines a eight-layered neuro-fuzzy architecture with a state feedback quadratic stabilising controller. Both the model and controller are updated online within a constrained unscented Kalman filter. Results from a benchmark Multi-Input and Multi-Output system demonstrate the effectiveness of the proposed approach.
Composable Template Attacks Using Templates for Individual Architectural Components

With embedded systems and IoT devices being widely deployed nowadays, their security becomes a major concern. Among all possible attacks, side channel attacks (SCA) represent a major source of threats. For power side channels, template attacks have been proven to be efficient and widely applicable. Traditional template attacks require physical access to an identical target device for extensive profiling to construct the attack template. In this paper, we present a composable template attack that relaxes this requirement by constructing the attack template as a composition of templates from individual architectural components, including processor, caches, and memories. The proposed approach enables an attacker to construct a template using only information of a system's components and device models thereof.
Bank Fraud Detection Using Support Vector Machine

With the significant development of communications and computing, bank fraud is growing in its forms and amounts. In this paper, we analyze the various forms of fraud to which are exposed banks d data mining tools allowing its early detection data already accumulated in a bank. We use supervised learning methods Support Vector Machines with Spark (SVM-S) to build models representing normal and abnormal customer behavior and then use it to evaluate validity of new transactions. The results obtained from databases of credit card transactions show that these techniques are effective in the fight against banking fraud in big data. Experiment result from the study show that SVM-S have better prediction performance than Back Propagation Networks (BPN). Besides the average prediction, accuracy reaches a maximum when training the data ratio arrives at 0.8.
MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection

Moving object detection (foreground and background) is an important problem in computer vision. Most of the works in this problem are based on background subtraction. However, these approaches are not able to handle scenarios with infrequent motion of object, illumination changes, shadow, camouflage etc. To overcome these, here a two stage robust and compact method for moving object detection (MOD) is proposed. In first stage, to generate the saliency map, background image is estimated using a temporal histogram technique with the help of several input frames. In the second stage, multiscale encoder-decoder network is used to learn multiscale semantic feature of estimated saliency for foreground extraction. The encoder is used to extract multi-scale features from multi-scale saliency map. The decoder part is designed to learn the mapping of low resolution multi-scale features into high resolution output frame. To observe the efficacy of proposed MsEDNet, experiments are conducted on two benchmark datasets (change detection (CDnet-2014) [1] and Wallflower [2]) for MOD. The precision, recall and F-measure are used as performance parameter for comparison with the existing state-of-the-art methods. Experimental results show a significant improvement in detection accuracy and decrement in execution time as compared to the state-of-the-art methods for MOD.
Automatic Identification of Abnormalities in 12-Lead ECGs Using Expert Features and Convolutional Neural Networks

Automatic identification of the rhythm/morphology abnormalities in ECGs has gained growing attention in various areas and remains a challenge. We propose an algorithm to classify 12-lead ECGs into 9 categories. We extracted expert features including generic features and specific features with statistics and physiology significance. Then a 17-layer Convolutional Neural Network (CNN) was proposed to detect deep features in ECGs. With these features we trained ensemble classifiers to predict labels. Experiment on the training set (5-fold cross-validation) reports 0.81 accuracy score.
Characterization and understanding of slow traps in GeOx-based n-Ge MOS interfaces

The properties of slow electron traps in n-Ge MOS interfaces over a wide range of electrical field across gate oxides $(E_{ox})$ are systematically investigated. It is found through careful examination of the CV hysteresis that slow trapping under low $E_{ox}$ conditions is attributed only to electron trapping into existing slow traps. Under large $E_{ox}$ conditions, on the other hand, generation of slow electron traps and hole trapping are found to additionally affect the slow trapping characteristics. We propose a new measurement scheme to discriminate existing and generated slow electron traps and apply this method to the three different GeOx-based MOS interfaces in order to clarify the nature of slow traps. It is revealed from this analysis that a pre-plasma oxidation process reduces existing slow electron traps and improves slow trapping in low $E_{ox}$. On the other hand, ultrathin Y2O3 insertion reduces generation of slow electron traps and improves slow trapping in high $E_{ox}$.
A Flexible Compound Sensing System for Tactile Detection in Prosthetic Hands*

Tactile perception feedback is very critical for grasp safety of prosthetic hands and realization of human-machine interaction. However, current tactile sensing methods are usually limited in practical applications due to either the structure or the material performance of sensors. In this work, we designed and fabricated a compound sensing system for tactile detection in prosthetic hands based on piezoelectret and force-sensing-resistor (FSR) sensors, where the piezoelectrets are highly sensitive to dynamic forces and the FSRs are stable for static forces. The results show that the proposed sensing system could simultaneously detect the normal pressure and tangential force on prosthetic hand surfaces. By improving the resolution of slipping signal with algorithm, the slipping signals could be recorded within 0.1 s. This preliminary work may provide a new approach for fabricate artificial skin of prosthetic hands.
Research on Visualization Systems for DDoS Attack Detection

Computer networks have been widely used and the network security is becoming a critical issue due to the frequent attacks. Among these attacks, the DDoS attack is a destructive attack method, which can cause enormous loss and is difficult to detect. The network administrators work on large volume of traffic data to monitor the DDoS attack. The information visualization method is then introduced to the domain of network security and DDoS attack detection to better comprehend the information behind the multi-dimensional data. Various visualization methods have been proposed to detect the DDoS attack. However, little research has been carried out to give an overview of the existing methods. In this work, we surveyed the literature from 2004 to 2017 concerning network security visualization. We have selected the methods designed for or that can be applied to DDoS attack detection. We have classified these methods and given the advantages and shortcomings of each kind of method. We then proposed the future work in this area, which is aimed at helping the researchers in this area to have a comprehensive view of DDoS visualization.
Finite-Time Non-Fragile Control of a Class of Uncertain Linear Positive Systems

The finite-time non-fragile control scheme has received great interest because of its robustness to the controller gain errors. In this paper, we intend to study the finite-time non-fragile control problems for a class of linear positive systems with uncertainties. It devises an appropriate non-fragile control law, such that the closed-loop system is positive and stabilizable and satisfies the given <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance in a specified time interval. The main issue is to give a sufficient condition for the solution of the designed finite-time non-fragile <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> controller associated with the several control techniques applied to the positive system. The design result is described as an optimization problem that can be expressed through a couple of linear matrix inequalities. In the end, we use a practical RL circuit model to evaluate the performance of the proposed controller.
Reader Text Highlighter based on Gaze Tracking and Finite State Machine

Camera-based gaze tracking incorporates the visual characteristics of the eye for applications in human-computer interaction (HCI). However, precision tasks such as highlighting text on a computer screen line by line while reading is limited by constraints such as low vertical accuracy of gaze tracking and low camera resolution. In this study, we developed an interactive read highlighter for text-heavy documents which employs the built-in webcam of laptop, by limbus tracking combined with finite state machine (FSM) for a smooth reading experience. The system also tracks the eye-blinking to alert the user of drowsiness while reading. Eye-tracking was performed using Haar cascade classifier followed by iris detection using image processing techniques. Finite state machine was implemented to alleviate for the insufficiency of vertical accuracy with the underlying assumption that the user reads the text without skipping back and forth across lines. The robustness of the text highlighter was assessed and obtained for proven results the approach. The performance of the highlighter was dependent on ambient lighting conditions, larger head movements and also limited by camera resolution.
Consensus Approach to Secure State Estimation for a Class of Remotely Sensed Networked Systems

Remote sensing is often a crucial part of cyber-physical and networked systems. Measurements taken at remote locations can both serve as monitoring tools as well as to inform control decisions. Over the past few years, a number of cyberattacks were mounted against such systems with severe socio-economic consequences. In this paper we explore how physical measurements can be used in combination with a consensus algorithm, to ensure secure state estimation for a class of tree-like networked systems.
Improving RNN Based Recommendation by Embedding-Weight Tying

Many researchers recently paid attention to applying deep learning to collaborative recommendation. Especially, RNN(Recurrent Neural Network)-based recommender system was shown to learn users' interest and preference from temporal sequences of users' movie consumption records, and they could make better recommendation compared to conventional collaborative recommendation. In this work, we present an embedding-weight tying approach to RNN-based recommendation in order to improve the performance of movie recommender system more. In experiments, our approach outperforms existing RNN-based recommendation as well as currently popular collaborative recommendation in terms of short-term prediction success(sps) and recall.
An accurate SOC estimation system considering the observation noise of the electric current

with the advent of the large-scale popularization of Lithium-ion storage batteries, the accuracy as well as low cost should be indispensable for measurement systems. The State of Charge (SOC) estimation is essential for capturing the state of Lithium-ion secondary batteries. SOC estimation using Kalman filter proved to be an effective SOC prediction method. However, Kalman filtering does not consider current noise (input noise). In battery management system (BMS), both voltage and current measurements contain noise. Therefore, SOC estimation consider both voltage and current measurements noise. In this paper, we discuss the state-of-charge (SOC) estimation system for lithium-ion batteries using the extended Kalman filter (EKF) considering the observation noise of the electric current.
E-HOSA: Extended-Hybrid Optical Switch Architecture for Cloud Computing Data Centers

Cloud computing has recently gained significant attention because of the emergence of new internet-based applications which utilize cloud computing infrastructure in the data centers (DCs). Therefore, Internet traffic and traffic generated within a data center has also increased exponentially. Traditional data center networks, which are based on the electrical switches, are not adequate to handle increasing bandwidth demands of these applications. They are also not power efficient. A new optical switch architecture, based on optical switches, for data centers is presented in this paper. Optical switches are more power efficient and they can support larger bandwidths. The proposed architecture, namely, extended-hybrid optical switch architecture (E-HOSA), relies on software defined network (SDN) networking paradigm. Similar to SDN, E-HOSA has a separate control plane and a data plane. We perform analytical modelling of E-HOSA to evaluate its cost and power consumption. The comparative analysis of E-HOSA is performed with other famous electrical / optical networks for data centers. The results have shown significant improvement in power consumption as compared to other networks on the cost of increased capital expenditures (CAPEX). However, this increased CAPEX is mitigated to some extent due to its increased power efficiency. We also perform scalability analysis of E-HOSA. The analysis shows that E-HOSA is scalable to thousands of servers which is suitable for a very large scale data center.