Search

Despite the importance of cyber-security for networked control systems, no suitable cryptosystem exists for networked control systems that guarantees stability and has low computational complexity. This study proposes a novel dynamic ElGamal cryptosystem for encrypted control systems. The proposed cryptosystem is a multiplicative homomorphic cryptosystem, and it updates key pairs and ciphertexts by simple updating rules with modulo operations at every sampling period. Furthermore, the authors modify the proposed cryptosystem by using a dynamic encoder and decoder so that the asymptotic stability of the encrypted control systems is guaranteed. Numerical simulations demonstrate that the encrypted controller with the proposed cryptosystem achieves asymptotic stability while randomly updating key pairs and ciphertexts. The feasibility of the proposed encrypted control system is evaluated through regulation control with a positioning table testbed. The processing time of the proposed encrypted control system is on the order of milliseconds, indicating that the system achieves real-time control.

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.

Here, a simplified algorithm to calculate log-likelihood ratio (LLR) for m-QAM demodulation is proposed. Traditional method calculating LLR for m-QAM demodulation is supposed to do distance computing between any two constellation points so that the high complexity of the hardware implementation is not acceptable. This simplified method is different from the traditional ways that the distance calculation between any two points in a plane can be omitted, indicating that there is an advantage in the amount of computation in this simplified method. Finally, the simplified method is compared with traditional methods from the perspective of complexity and system performance. The simulation results show that the simplified method can greatly reduce the amount of calculation and have little effect on the performance of the system.

Fractionally spaced super-exponential (FSSE) algorithm has the disadvantage of computational complexity since it exploits high-order statistics explicitly. The authors propose a dimension degradation technique for FSSE when it is applied to the equalisation of a sparse channel in accordance with the relationship between the coefficients of the cross fourth-order cumulant (CFOC) in FSSE and the channel impulse response. They implement partial updating on the prominent coefficients of the CFOC with all the small coefficients remaining unchanged. The computational complexity of the modified FSSE reduces significantly with an acceptable performance loss, and its performance is validated via numerical simulations.

In this study, a 2D adaptive beamforming algorithm for sparse array is proposed. Firstly, a signal model based on matrix completion theory for adaptive beamforming in sparse array is established, which is proved to satisfy null space property. Secondly, in order to enhance the performance of reconstructing complete received signal matrix, genetic algorithm is used to optimise the sparse sampling array. Thirdly, the accelerated proximal gradient algorithm is adopted to reconstruct the complete received signal matrix. Finally, the adaptive beamforming weight is provided directly to form beam patterns, which can be obtained as a result of reconstructing complete received signal matrix. The proposed method could improve the utilisation rate of the sparse array elements and reduce the computational complexity in interference suppression. Simulation results show the effectiveness of the method.

With precise angle measurement, traditional monopulse techniques for forward-looking imaging can acquire exact angle information of one single target within a radar beam. However, when multiple targets exist in a beam, it is difficult to resolve them. To address this problem, a novel monopulse forward-looking imaging algorithm based on Levenberg–Marquardt (LM) optimisation is proposed. The core idea of this algorithm is to solve maximum likelihood estimation problem based on LM optimisation (ML-LM) to obtain the direction of arrival (DOA) estimation of unresolved targets. First, the echo model of two targets within a forward-looking radar beam is established, then the imaging framework of the proposed algorithm is introduced. Finally, the advantages of ML-LM are illustrated based on a series of evaluation criterion, including angle measurement error for various values of signal-to-noise ratio (SNR), angle interval adaptability and computational efficiency. The simulation results show that two targets within a forward-looking radar beam can be resolved and relocated accurately utilising the proposed algorithm. Meanwhile, the comparison with other algorithms shows it has higher DOA estimation accuracy, less computational complexity and a wider range of angle interval adaptability.

The problem of DOA estimation for sub-array multiple-input multiple-output radar is concerned. Employing compressive sampling concept and the minimum mean-square error (MMSE) technology, the proposed algorithm alternates between updating sample covariance matrix and the MMSE filter bank values until convergence. Simplified array manifolds are considered to decrease the computational complexity. The core idea of the algorithm is to determine the DOA by calculating the spatial distribution of signal power adaptively. Simulation results show that the new algorithm performs well both in a wide SNR range and limited samples, compared with the MUSIC, PIAA-APES, and OGSBI algorithms. The most outstanding advantage of the new algorithm is that it can maintain high estimation accuracy under limited samples without knowing the number of targets.

Matrix constant false alarm rate (M-CFAR) detector based on information geometry provides a novel scheme to the detection of target in sea clutter environment for pulse Doppler radar with small bunch of pulses. The detection performance of the existing M-CFAR detectors is greatly influenced by the geometric measures on the matrix manifold. Different geometrical measures lead to different detection performance, some of which are not satisfactory. Motivated by this, the authors utilise Root-Euclidean, Power-Euclidean, Cholesky-Euclidean and new Riemannian measure to design several kinds of detection schemes, which improve the detection performance. Finally, simulation results verify that the designed detectors achieve a better detection performance with a relatively lower computational complexity.

This article designs waveform in fast-time domain to optimise high-frequency radar performances embedded in the co-channel interference and coloured noise. Considering the whitening filter is applied for fast-time processing, a novel objective function accounting for both signal to interference plus noise ratio (SINR) and integrated range sidelobe level (IRSL) is introduced and the constant modulus constraint is forced on the probing waveform. An iterative algorithm based on the alternating direction method of multipliers is presented, resolving such non-convex quartic optimisation problem with a polynomial-time computational complexity. In each iteration, two quadratic problems are presented, with the closed-form solutions given. Finally, the authors evaluate the effectiveness of the designed waveform in terms of the range-Doppler processing result, SINR, IRSL and power spectrum.

Target localisation using frequency diverse array (FDA) has drawn much attention owing to the additional degree of freedom in the range dimension. However, the Doppler effect was often ignored in previous FDA works because of the stationary target assumption, and in contrast, even stationary target localisation could be of quite a high computational complexity. In this study, a method is proposed that can jointly estimate range, angle, and Doppler for FDA multiple-input-multiple-output radar with low computational complexity. First, Doppler is estimated independently by utilising unstructured maximum likelihood method. Next, traditional two-dimensional (2D) MUSIC is divided into multiple one-dimensional (1D) MUSIC to estimate the angle. Then substituting the estimated angle into the former 2D MUSIC results in a 1D searching over range dimension to get the range estimation. The advantage of the proposed method was verified by simulation results.

This study introduces a two-level strategy for efficient execution of multiple sequence alignment (MSA) of complex heterogeneous sequences. The two levels of the proposed technique are comprised of: designing the discrete firefly algorithm (DFFA) for the formation and implementation of makespan minimisation on parallel machines, followed by performing Ctrie-based caching for pairwise alignment to reduce the load on the data servers for handling multiple queries. The proposed strategy addresses a multi-client problem that aims to acquire the full advantage of the computational power of parallel connected machines. Further, it is shown that the inclusion of Ctrie as caching mechanism successively improves the performance of the system with accretion in several sequences. Performance of proposed DFFA is also compared with discrete versions of four swarm intelligence based algorithms at the criteria of makespan minimisation and the rate of convergence on two kinds of complex and diverse datasets. The work is unique in this sense: it is the first swarm-intelligence-based implementation for the addressed problem; it is so far the first approach for Ctrie based caching of the MSA on the scheduled parallel machines; hybridisation of DFFA with Ctrie for caching the MSA results is also a novel implementation.

In this study, a three-dimensional model with multi-parameter order reduction is applied to the thermal modelling of power electronics modules with complex geometries. Finite element or finite difference method can be used to establish accurate mathematical models for thermal analyses. Unfortunately, the resulting computational complexity hinders the analysis in parametric studies. This study proposes a parametric order reduction technique that can significantly increase simulation efficiency without significant penalty in the prediction accuracy. The method, based on the block Arnoldi method, is illustrated with reference to a multi-chip SiC power module mounted on a forced air-cooled finned heat sink with a variable mass flow rate.

The modular multilevel converter (MMC) with large-scale sub-modules has the advantage of simpler modulation, lower switching frequency, and lower harmonic component, thus would be very promising in voltage source converter (VSC)–high voltage direct current transmission systems. Conventional capacitor voltage balancing algorithm suffers from insufficient grouping and sorting techniques. Therefore, it might result in excessive computation and high switching frequency of power electronic devices. To address the problem, a modified capacitor voltage balancing sorting algorithm is proposed in this paper. The proposed algorithm could avoid sorting all the module capacitor voltages by selecting only a certain number of the largest or smallest module capacitor voltages, and thus reduces time complexity greatly without losing control precision. Furthermore, the proposed algorithm focuses on the sub-modules whose capacitor voltage exceeds the limits, while the switching states of the other sub-modules are maintained to some degrees by employing the maintaining factor. Therefore, the switching frequency of the power electronic devices is further reduced. The performance of the proposed algorithm is evaluated through a time-domain MMC–HVDC simulation in PSCAD/EMTDC. Results show that the proposed algorithm is able to balance the module voltage with lower computation and reduce the switching frequency of power devices significantly, without noticeably increasing the capacitor voltage ripples.

The characteristics of the pole-to-pole fault in the tree-structured DC system are complicated, and the transient analytic expressions of high-order current and voltage can make the calculation difficulty increase significantly. In response to this phenomenon, on the one hand, when the parameters are the same, the impact of the changes of the number about the terminals on the waveform is considered. On the other hand, when the number of terminals is fixed, the influence of the change of the parameter on the waveform is studied. Taking the interaction between different ports into account, correct the short-circuit current and voltage expression of single-ended DC system and the calculation method of short-circuit current and voltage expression for multi-ended DC system is proposed. This method can reduce the computational complexity of transient expression for multi-ended DC system. The calculation results are verified by MATLAB/Simulink simulation tool. The calculated transient expressions of voltage and current are in good agreement with the results of simulation, which provide the theoretical basis for the fault location and calculation of setting value.

With the growth of complex multiprocessor system-on-chip design and the evolution of process technology, it is crucial to address thermal issues owing to the high-power density of chips. Electronic system level (ESL) design is an acknowledged effective methodology to explore system design by virtual platforms (VPs) at the early stages of integrated circuit (IC) design. Therefore, power- and thermal-aware (PTA) VPs at the ESL are required to explore the thermal issues at the early stages of IC design. Current VPs at the ESL are less discussed in the thermal issues. Therefore, the authors propose a methodology to easily develop VPs from power awareness to power and thermal awareness. In the methodology, a refined thermal engine that has lower computational complexity is proposed to analyse the heat of ICs, and it has high compatibility for integration with power-aware (PA) VPs. In addition, the thermal analysis in the methodology considers the thermal effects of leakage power consumption to reflect the physical temperature of ICs. Thus, system designers can utilise the proposed methodology to easily develop their PA VPs into PTA VPs.

For positioning a moving ultrasonic transmitter, most of the existing ultrasonic positioning systems require the use of a bank of correlators to estimate the Doppler shift associated with its movement which require high computational complexity. In this paper, for positioning a moving transmitter, a computationally efficient a Doppler shift estimation and compensation technique is proposed. As the proposed approach has the ability to measure the Doppler shift directly from the received signal, it does not require to use a bank of correlators to estimate the Doppler shift associated with its movement of the transmitter.

Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Because of the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. This paper explores three important factors that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm while the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.

A minimal-difference differential coefficients method is presented for low power and high-speed realisation of differential-coefficients-based finite impulse response filters. The conventional differential coefficients method (DCM) uses the difference between adjacent coefficients whereas we identify the coefficients that have the least difference between their magnitude values and use these minimal difference values to encode the differential coefficients. Our minimal-difference differential coefficients can be coded using fewer bits, which in turn reduces the number of full additions required for coefficient multiplication. By employing a differential-coefficient partitioning algorithm and a pseudofloating-point representation, we show that the number of full adders and the net memory needed to implement the coefficient multipliers can be significantly reduced. The proposed method is combined with common subexpression elimination for further reduction of complexity. Experimental results show the average reductions of full adder, memory and energy dissipated achieved by our method over the DCM are 40, 35 and 50, respectively. � The Institution of Engineering and Technology 2007.

It is well known that most wavelet transform algorithms compute sampled coefficients of the continuous wavelet transform using the filter bank structure of the discrete wavelet transform (DWT). Although this method is efficient, noticeable computational savings have been obtained through an FFT-based implementation. The authors present a fast Hartley transform (FHT)-based implementation of the filter bank and show that noticeable overall computational savings can be obtained.