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Blind Source Separation (BSS) of underdetermined mixture has acquired a huge attention in signal processing environment, even though it is very much difficult to separate the underlying sources. The difficulty in source separation arise due to the mixing of large number of source signals in time and frequency, and propagation of it to one or more sensors through air. The objective in BSS is to identify...
This paper proposed a simple, efficient and easy to hardware implementation iteratively reweighted algorithm to improve the recovery performance for sparse reconstruction from compressive sensing (CS). The algorithm focusses on solving the weighted non-convex penalty minimization problem using homotopy and dichotomous coordinate descent (DCD) algorithm compared with the traditional l1 norm and weighted...
In multiple-input multiple-output (MIMO) radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, maximum-likelihood (ML) estimation yields the best performance. For this problem, the ML estimation requires the joint estimation of spatial location and Doppler shift, which is a two dimensional search problem. Therefore, the computational complexity of ML estimation...
Passive radar imaging using illuminators has attracted much attention in recent years, and space passive radar imaging has been the hotspot of nowadays' research because it has more advantages compared with other ground-based radar imaging. This paper presents a fast and robust passive radar imaging reconstruction algorithm (Harctan-Oracle) based on digital video broadcasting-satellites (DVB-S). In...
The Finite Control Set Model Predictive Control (FCS-MPC) technique is worldwide recognized due to its adaptability and capacity to take into account the discrete characteristics of control systems. However, it shows some limitations when directly used to control power converters, as its variable frequency spectrum, its limited current quality and its low-voltage precision for a given sample time...
Particle swarm optimization constitutes currently one of the most important nature-inspired metaheuristics, used successfully for both combinatorial and continuous problems. Its popularity has stimulated the emergence of various variants of swarm-inspired techniques, based in part on the concept of pairwise communication of numerous swarm members solving optimization problem in hand. This paper overviews...
The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm “Simultaneous On-line Discovery and Improvement of Robotic Skills” (SODIRS)...
We present an approach for 2D sensor network localization when only bearing measurements are available and no global coordinate frame is known. Our work builds off of the linear constraint given in Kennedy et al. (2012) for sets of nodes that form triangles. We extend that constraint to general networks and present methods for locally optimizing the resulting cost function. We also show how these...
This paper describes an algorithm for robotic motion planning that is capable of optimising several cost functions simultaneously to provide optimised, feasible and collision-free paths. The algorithm is based on the best-first graph search algorithm using a Pareto frontier to evaluate costs at each node. Additionally, we include a calculation of the distribution of robot trajectories when the path...
In adaptive feedback cancellation the convergence speed and the computational complexity depend on the number of adaptive param-eters used to model the acoustic feedback path. To improve the convergence speed and reduce the computational complexity, it has been proposed to model the acoustic feedback path as the convolution of a time-invariant common pole-zero part and a time-varying variable part...
Conventional single-vector-based model predictive torque control (MPTC) has been widely studied due to its intuitive concept and quick response. To improve the steady state performance, recently the concept of duty cycle control was introduced in MPTC by inserting a null vector along with an active vector during one control period. However, this still fails to reduce the torque error to a minimal...
This article describes universal tracking control method. It is based on model linearization in every point of trajectory. Tested device is an inertial wheel pendulum (IWP). This is an underactuated nonlinear object — two degrees of freedom (angle from vertical and angle of rotation of electric motor) and one actuator (current). The linear quadratic regulator (LQR) and the model of the object are...
This paper presents a finite control set model predictive strategy for a three-phase, three-level Neutral-Point-Clamped (NPC) inverters with resistive-inductive load (RL-Load). This strategy allows for fast load current control while keeping the balance of the DC-link capacitor voltages. The system performance with a prediction horizon of one sample time and the dynamic response of the system with...
In the paper a multiple use of the fractional-order differential calculus theory in the model predictive control is proposed. First, the principle of the integer-order linear predictive control and theoretical foundations of the fractional-order differential calculus are reminded. Using the presented theoretical foundations attention is focused further on the possibility of developing the fractional-order...
Model Predictive Control (MPC) offers many advantages over more traditional control techniques, such as the ability to avoid cascaded control loops. Unfortunately, as a result of the lack of presence of a modulation strategy, this approach produces spread spectrum harmonics which are difficult to filter effectively. Furthermore, high switching frequencies may be needed because of the limited number...
The planning of collision-free paths of a team of mobile robots involves many degrees of freedom and therefore the use of sampling-based methods is a good alternative. Among them, the RRT∗ planner has been proposed to cope with optimization problems, and has been proven to be asymptotically optimal. Any optimization function can be defined, although optimization has been usually focused on the traveled...
Dynamic Time-division duplex (TDD) can provide efficient and flexible splitting of the common wireless cellular resources between uplink (UL) and downlink (DL) users. In this paper, the UL/DL optimization problem is formulated as a noncooperative game among the small cell base stations (SCBSs) in which each base station aims at minimizing its total UL and DL flow delays. To solve this game, a self-organizing...
This paper presents a model predictive strategy for a three-phase, two-level Voltage Source Inverter (VSI) and three-level Diode-Clamped Converters (DCCs) with AC filter for Renewable Energy Systems (RES) applications. The renewable energy systems model is used in this paper to investigate the system performance when power is supplied to Resistive-Inductive load (RL-load). This strategy allows for...
Localisation or position determination is one of the most important applications for wireless sensor networks since the locations of the sensor nodes are critical to both network operations and most application level tasks. Numerous techniques for localisation of sensor nodes have been proposed that make use the Received Signal Strength Indicator (RSSI) from sensor nodes due to its simplicity and...
With the advent of personal computers, humans have always wanted to communicate with them in either their natural language or by using gestures. This gave birth to the field of Human Computer Interaction and its subfield Automatic Sign Language Recognition. This paper proposes the method of automatic feature extraction of the images of hand. These extracted features are then used to train the Softmax...
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