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This work proposes different methods to calculate the sensitivity of system parameters based on graphic analysis. From percentages variations in base values of the parameters, one-at-a-time measures are taken to the construction of a graph that expresses the impact on the output caused by variations in the inputs. Two case studies are proposed, with simulated and experimental data, in which the proposed...
The performance of contactless biometric identification system is greatly affected by the image quality. In order to acquire palm vein image with high-quality, an image quality assessment algorithm for palm vein is presented based on clarity and brightness uniformity. We propose two criterions and fuse them to make a more reasonable assessment of palm image quality. Moreover, a large number of experiments...
Standard hexarotors are often mistakenly considered ‘by definition’ fail-safe multi-rotor platforms because of the two additional propellers when compared to quadrotors. However this is not true, in fact, a standard hexarotor cannot statically hover with ‘only’ five propellers. In this paper we provide a set of new general algebraic conditions to ensure static hover for any multi-rotor platform with...
This paper present results of on-line control loop performance assessment using non-Gaussian statistical and fractal measures. Research shows importance of loop quality indexes that are not biased with Gaussian assumption about signal characteristics. Industrial data show frequent fat-tail properties and thus relevant indexes are proposed, like non-Gaussian statistical factors or persistence fractal...
Detecting anomalies in large-scale, streaming datasets has wide applicability in a myriad of domains like network intrusion detection for cyber-security, fraud detection for credit cards, system health monitoring, and fault detection in safety critical systems. Due to its wide applicability, the problem of anomaly detection has been well-studied by industry and academia alike, and many algorithms...
A derivative-free method for state estimation-based control of a robot/prosthesis system is presented. The system is the combination of a test robot that emulates human hip and thigh motion, and a powered transfemoral prosthetic leg. The robot/prosthesis combination is modeled as a three degree-of-freedom (DOF) robot: vertical hip displacement, thigh angle, and knee angle. We develop a derivative-free...
In combination with Control Lyapunov and Control Barrier Functions (CLF and CBF), Quadratic Programming (QP) has been considered for control design for nonlinear system and has been successfully applied to robotic and automotive systems. This approach could be considered an extension of the CLF based point-wise minimum norm controller to constrained nonlinear systems. In this paper we modify the original...
A fundamental but often overlooked component in the z-axis feedback loop of the atomic force microscope (AFM) operated in dynamic mode is the demodulator. Its purpose is to obtain a preferably fast and low-noise estimate of amplitude and phase of the cantilever deflection signal in the presence of sensor noise and additional distinct frequency components. In this paper, we implement both traditional...
In this paper, a robust tracking control approach is proposed for a class of multivariable nonlinear systems with input quantization. Based on the standard backstepping approach and the hyperbolic tangent function property, a robust quantized controller is constructed. This effort proves that with the proposed controller, all the signals of closed-loop system are globally uniformly bounded and the...
Digital signal processing for Global Navigation Satellite System (GNSS) receivers is mostly based on the assumption that the noise at the receiver input is Gaussian. This assumption leads to a non-linear Least Squares (LS) problem where GNSS signal parameters are estimated by minimizing a quadratic cost function. The receiver performance can be however significantly degraded by non-Gaussian phenomena...
Software deobfuscation is a crucial activity in security analysis and especially in malware analysis. While standard static and dynamic approaches suffer from well-known shortcomings, Dynamic Symbolic Execution (DSE) has recently been proposed as an interesting alternative, more robust than staticanalysis and more complete than dynamic analysis. Yet, DSE addresses only certain kinds of questions encountered...
The detection of cells and nuclei is a crucial step for the automatic analysis of digital pathology slides and as such for the quantification of the phenotypic information contained in tissue sections. This task is however challenging because of high variability in size, shape and textural appearance of the objects to be detected and of the high variability of tissue appearance. In this work, we propose...
Integrated circuit (IC) packages can delaminate under thermal cyclic loading because of elastic properties mismatch between the components. On the other hand, delamination has no pattern which points out the effect of variations of the design parameters on the response. Hence, a robustness estimation should be done to have more stable IC packages which show no important variations in the response...
In recent years, several encryption schemes have been proposed to protect data from unauthorized access. It is not suitable to use traditional encryption algorithms for image encryption which were proposed for textual data. The encryption schemes used for images are computationally expensive and power hungry, hence not suitable for mobile phone devices. In this paper, an image encryption scheme is...
The paper presents a robust algorithm for detecting of features, the effectiveness of the robust detector of bright and dark features analyzed in the processing of natural scene images. Images in computer vision systems are often exposed to noise, such action causes detector performance degradation and increases of rate false alarms. Research shows that the effectiveness of the features detection...
We study the problem of low-rank and sparse decomposition from possibly noisy observations. We propose a novel objective function with nuclear norm on the low-rank term and ℓ0-‘norm’ on the sparse term, as well as ℓ1-norm on the additive noise term. When there is no dense inlier noise, the proposed method shares the same theoretical guarantee as the Principal Component Pursuit (PCP), i.e., it can...
Linear discriminant analysis (LDA) is typically carried out using Fisher's method, which relies heavily on the estimation of sample mean vectors and covariance matrices. However, Fisher LDA is vulnerable to outliers as it happens to other multivariate statistical methods. In this paper, we analyzed the optimal discriminant design based on the criterion of minimizing total misclassification rate, assuming...
This paper focuses on the low rank plus sparse matrix decomposition problem in big data settings. Conventional algorithms solve high-dimensional optimization problems that scale with the data dimension, which limits their scalability. In addition, existing randomized approaches mostly rely on blind random sampling. In this paper, the drawbacks of random sampling from coherent/structured data matrices...
With a growing system complexity in the IoT framework, many networked cyber-physical systems work in a hierarchical fashion. Layers of information outputs and command inputs are available. An active area of research is in optimizing the design of policies and control command that influence information flow for such multi-layered systems. Our focus in current research is to first formulate the control...
In this paper, we develop a new approach called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the novel inception region proposal network (Inception-RPN), which slides an inception network with multi-scale windows over the top of convolutional feature maps and associates a set of text characteristic...
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