Search

This study proposes an improved interferogram filtering method with mathematical morphology. The algorithm firstly converts the interferogram in cosine and sine parts to preserve the continuity of phases, then adopts the improved adaptive line structure elements to run reconstruction process on two parts based on grey scale morphological opening and closing operations. In addition combine the two filtered parts to get the wrapped phases. Simulated and real-data experiments show that the proposed algorithm is more robust in filtering compared with other algorithms referred in this study. [ABSTRACT FROM AUTHOR]

This paper examines the resource gain that can be obtained from the creation of clusters of nodes in densely populated areas. A single node within each such cluster is designated as a "hotspot"; all other nodes then communicate with a destination node, such as a base station, through such hotspots. We propose a semi-distributed algorithm, referred to as coordinated cognitive tethering (CCT), which clusters all nodes and coordinates hotspots to tether over locally available white spaces. CCT performs the following these steps: (a) groups nodes based on a modified k-means clustering algorithm; (b) assigns white-space spectrum to each cluster based on a distributed graph-coloring approach to maximize spectrum reuse, and (c) allocates physical-layer resources to individual users based on local channel information. Unlike small cells (for example, femtocells and WiFi), this approach does not require any additions to existing infrastructure. In addition to providing parallel service to more users than conventional direct communication in cellular networks, simulation results show that CCT can increase the average battery life of devices by 30%, on average. [ABSTRACT FROM AUTHOR]

Smart metering systems have become widespread around the world. RF mesh communication systems have contributed to the creation of smarter and more reliable power systems. This paper presents an algorithm for positioning GPRS concentrators to attain delay constraints for a ZigBee-based mesh network. The proposed algorithm determines the number and placement of concentrators using integer linear programming and a queueing model for the given mesh network. The solutions given by the proposed algorithm are validated by verifying the communication network performance through simulations. [ABSTRACT FROM AUTHOR]

It is very important to provide a parameterized quality of service (QoS) using traffic specification (TSPEC), such as mean data rate, maximum burst size, and peak data rate, when packets from the application layer need to be transmitted with guaranteed services in a high-rate wireless personal area network (HR-WPAN). As medium resources are limited, the optimal medium time required for each device needs to be estimated to share the resources efficiently among devices. This paper proposes a variable-service interval-based resource allocation algorithm to efficiently make a reservation of medium resources based on a parameterized QoS. In other words, the proposed algorithm calculates the number of medium access slots (MASs) based on TSPEC, local resources, and local conditions and determines suitable locations for the MASs within a superframe to accommodate more devices. The simulation results show that the proposed algorithm can accommodate more devices and has greater than 10% resource allocation efficiency in an HR-WPAN compared to existing schemes. [ABSTRACT FROM AUTHOR]

Co-channel interference between macro-femtocell networks is an unresolved problem, due to the frequency reuse phenomenon. To mitigate such interference, a secondary femtocell must acquire channel-state knowledge about a co-channel macrocell user and accordingly condition the maximum transmit power of femtocell user. This paper proposes a pilot-based spectrum sensing (PSS) algorithm for overlaid femtocell networks to sense the presence of a macrocell user over a channel of interest. The PSS algorithm senses the pilot tones in the received signal through the power level and the correlation metric comparisons between the received signal and the local reference pilots. On ensuring the existence of a co-channel macrocell user, the maximum transmit power of the corresponding femtocell user is optimized so as to avoid interference. Time and frequency offsets are carefully handled in our proposal. Simulation results show that the PSS algorithm outperforms existing sensing techniques, even at poor received signal quality. It requires less sensing time and provides better detection probability over existing techniques. [ABSTRACT FROM AUTHOR]

A dimensionality-reduction-augmented non-linear state-space representation has been proposed to reduce the optimisation space for maximum-likelihood estimation. Based on the above representation, an expectation-maximisation algorithm has been derived to realise joint estimation of states and parameters. During the expectation step, the system state was estimated via the use of a fifth-order cubature Kalman filter and Rauch-Tung-Striebel smoother based on the stateaugmented method. During the maximisation step, unknown parameters within iterations were estimated using the Newton method. Subsequently, two joint-estimation methods - one containing all measurements and the other involving a sliding window - were developed to estimate the invariants and step parameters, respectively. An example concerning manoeuvringtarget tracking has been discussed to demonstrate the performance of proposed algorithms. [ABSTRACT FROM AUTHOR]

To investigate highly squinted spotlight synthetic aperture radar (SAR) images of an electrically large ship target over a rough sea surface, this work focuses on the simulation analysis of SAR images from such a composite scene. For this problem, there are two key issues need to be considered, namely the simulation and the processing of SAR echoes. Considering the first issue, an efficient facet scattering model based on capillary wave modification facet scattering model and geometrical optics and physical optics hybrid method is applied to calculate the electromagnetic (EM) scattering characteristics from a real ship-ocean scene, based on which SAR echoes can be obtained. For the second issue, a non-linear frequency scaling algorithm (NFSA) is employed to efficiently process the highly squinted SAR echoes. Compared with the traditional frequency scaling algorithm, the NFSA extends the frequency scaling operation to the cubic order and makes a more accurate secondary range compression. With the solutions to the two issues, SAR images of a complicated ship-ocean scene under different incident and squint angles are presented and analysed. The reasonable results demonstrate the validity of the simulation approach and the practicability of the model for highly squinted spotlight SAR images. [ABSTRACT FROM AUTHOR]

Advanced in unpredictability, ergodicity and sensitivity to initial conditions and parameters, chaotic maps are widely used in modern image encryption algorithms. In this study, the authors propose a novel image block encryption algorithm based on several widely used chaotic maps. In their algorithm, the image blocking method is variable, and both shuffling and substitution algorithms are adopted based on different chaotic maps. Several simulations are provided to evaluate the performances of this encryption scheme. The results demonstrate that the proposed algorithm is with high security level and fast encryption speed, which can be competitive with some other recently proposed image encryption algorithms. [ABSTRACT FROM AUTHOR]

In the field of radar reconnaissance, unsupervised recognition of radar signals is a particularly important method for classifying different signals and estimating signal parameters. For untagged signals, a new unsupervised recognition method based on the time‐frequency (TF) analysis and Multi‐view Low‐Rank Sparse Subspace Clustering (MLRSSC) is proposed. Specifically, the authors use image wavelet decomposition to create four views for TF images (TFIs), which introduces the concept of multi‐view subspace clustering into radar signal recognition. Compared with other advanced clustering algorithms, the authors' algorithm pays more attention to combining the inherent properties of TFIs. Aiming at the unique ridgeline distribution phenomenon of TFI, a multi‐block technology capable of robustly distributing TFIs subspaces is proposed. Then, Multi‐block and MLRSSC are combined to propose the Multi‐block Joint Multi‐view Subspace Clustering (M‐MLRSSC). The simulation shows that in the TFIs data set with a certain signal‐to‐noise ratio, M‐MLRSSC has obtained superior results, which is better than a variety of advanced subspace clustering algorithms. [ABSTRACT FROM AUTHOR]

The post‐silicon validation and debug is the most important task in the contemporary integrated circuit design methodology. The vital problem prevailing in this system is that it has limited observability and controllability due to the minimum number of storage space in the trace buffer. This tends to select the signals prudently in order to maximize state reconstruction. In the reported works, to select and to restore the signals efficiently it is categorized into two types like low simulation with high‐quality technique and high simulation with low‐quality technique. In this work, a node‐based combinational gate signal selection algorithm is proposed based on machine learning method that maximizes the state restoration capability. A significant improvement (80%) has made to achieve adequate simulation time with the high‐quality associated with the state‐of‐the‐art of supplementary methods. [ABSTRACT FROM AUTHOR]

For the scenario where the overall layout is known and the obstacle distribution information is unknown, a dynamic path planning algorithm combining the A* algorithm and the proximal policy optimization (PPO) algorithm is proposed. Simulation experiments show that in all six test environments, the proposed algorithm finds paths that are on average about 2.04% to 5.86% shorter compared to the state‐of‐the‐art algorithms in the literature, and reduces the number of training epochs before stabilization from tens of thousands to about 4000. [ABSTRACT FROM AUTHOR]

Through-the-wall radar has been used in rescue missions, behind-the-wall targets detection, surveillance and reconnaissance etc. However, through-the-wall radar encounters two major problems in complex environment. One is that the refraction of the unknown wall would distort the through-the-wall image. The other is that the multi-reflection between wall and walls as well as wall and targets would lead to a lot of fake targets. To solve these problems, a multi-path suppression algorithm is proposed in this study. The simulation results show that proposed algorithm can automatically estimate the thickness and dielectric parameter of the unknown wall and give an undistort image, and remove the fake targets due to the multi-path effect. [ABSTRACT FROM AUTHOR]

Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomised algorithms have been proposed in the literature. They commonly perform poorly when input traffic does not uniformly select output ports. Here, the authors propose two randomised algorithms that outperform the well-known formerly proposed solutions. They exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a new algorithm for the flocking problem of multi-agent systems with the dynamic virtual leader under the sampled-data frameworks. A necessary time-varying condition is derived on the sampling period for preserving connectivity of the network and avoiding collision among agents. Then, defining a new energy function, it is proved that using a connectivity-preserving potential function and under the sampling period which satisfies the necessary condition, an asymptotic flocking motion is achievable. Under the proposed sampled-data flocking algorithm, the connectivity preservation of the network and collision avoidance among agents are guaranteed as well as the velocity convergence of all agents to that of the virtual leader is ensured. Moreover, employing the upper bound of the energy function, they specify an upper bound of the sampling period which could satisfy the time-varying necessary condition. Finally, they present a numerical simulation to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Efficient transient simulation of large power grid is of great importance for system operation and dispatching, fault repetition, operator training etc. Online stability estimation and testing on system control and protection require hard real-time performance of transient simulation. The conventional real-time simulation, which splits an entire grid into sub-grids and solves them in parallel, lacks generality for various case systems. This study proposes a new efficiency enhancement method without grid partitioning. Three key techniques are involved. First, parallel solving format is proposed to distribute two-layer computational burdens into central processing unit cores in balance. Second, to calculate the fault changing the grid structure, the inverse current compensation is employed instead of re-forming impedance matrix and repeating lower-upper (LU) decomposition. Third, dual-loop forward and backward substitutions to solve grid equations are modified to a single-loop iteration, which further improves the computational efficiency. At last, the validity and effectiveness of the proposed method are verified by tests. [ABSTRACT FROM AUTHOR]

This study presents an observer-based robust adaptive type-2 (T2) fuzzy tracking control scheme for the longitudinal dynamics of flexible air-breathing hypersonic vehicles. The control scheme is composed of three parts, namely an uncertain dynamics approximator, a reduced-order adaptive state observer, and command filter-based backstepping altitude and velocity controllers. First, the approximator is implemented by utilising interval T2 fuzzy logic systems for approximating unknown vehicle dynamics in the control scheme. Then, the reduced-order adaptive state observer is designed by the combination of the approximator and output state feedback techniques, which guarantee the estimation of unmeasurable states as well as uncertain dynamics identification. By using the linear matrix inequalities technique, a new condition for obtaining the gains of the state observer is presented. Next, on the basis of the designed approximator and state observer, the altitude and velocity tracking controllers are constructed by command-filtered backstepping control approach which avoids the explosion of complexity in conventional backstepping design. Thorough stability analysis of the closed-loop systems is conducted. Nominal and comparison simulation studies considering parametric uncertainty, incomplete state measurement, and measurement noises are carried out, illustrating the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Four improved chirp scaling algorithms (CSAs) are proposed to reconstruct images from synthetic aperture radar (SAR) data received at high squint angles, in which a larger synthetic aperture is required to receive sufficient amount of data for image reconstruction, and the range migration also becomes more serious, demanding more computational load and larger memory size. The proposed methods reconstruct better SAR images with less computational load and memory than the conventional CSA, which are verified by simulations. [ABSTRACT FROM AUTHOR]

Solar energy applications and research are becoming increasingly popular, and photovoltaics (PVs) are among the most significant solar energy applications. To simulate and optimise PV system performance, the optimal parameters of the solar cell models should be estimated exactly. In this study, improved simplified swarm optimisation (iSSO), a recently introduced soft computing method based on simplified swarm optimisation, is proposed to minimise the least square error between the extracted and the measured data for the solar cell models parameter estimation of the single- and double-diode model problems. Based on the new all-variable difference update mechanism and survival of the fittest policy, the proposed algorithm is able to find an improved approximation for estimating the parameters of single- and double-diode solar cell models. As evidence of the utility of the proposed iSSO, the authors present extensive computational results for two benchmark problems. The comparison of the computational results supports the proposed iSSO algorithm outperforms the previously developed algorithms for all of the experiments in the literature. [ABSTRACT FROM AUTHOR]

Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2. [ABSTRACT FROM AUTHOR]

Demand-side resources have considerable potential, which can provide efficient load shedding operations so as to maintain the supply-demand balance together with generating units. This study is concerned with active power dispatch and control problems by considering both generating units and demand-side resources, where some controllable loads in demand-side are aggregated under each bus load agent. A multi-level dispatch control architecture is proposed, which can handle the optimisation and control problems in different time scales. Specifically, an upper layer performs calculation of system's look-ahead dispatch for generating units and bus load agents, while a middle level computes control actions for multiple load clusters in each bus load agent via a distributed consensus estimation algorithm, and multiple energy management units in the lower layer respond the control instruction issued from the load cluster based on a distributed pinning control algorithm. For the middle and lower layers, participants can communicate with its neighbours via sparse communication networks for the sake of distributed calculation or distributed control, which is more robust than the centralised strategies in terms of dealing with massive amounts of participation units. The simulation results on a modified IEEE 9 bus system show the applicability of the proposed architecture. [ABSTRACT FROM AUTHOR]

Integration of intermittent renewable resources and mass electrification of heat and transport into the existing electricity network, with limited network asset reinforcement requires incorporation of intelligence in the form of active management of flexible resources within different sections of the distribution network. A hierarchical multi-level control framework is proposed for this purpose which incorporates the appropriate optimisation and control strategies at different levels. In particular, a novel deterministic control algorithm for controlling power flows at the community cell level has been developed and presented in this study. This algorithm incorporates robustness to communication and device failure and is easily expandable to an arbitrary number of devices. The simulation results presented in this study show that the effectiveness of the proposed control technique depends on distributed energy resources flexibility and storage capacity. [ABSTRACT FROM AUTHOR]

A key problem with IEEE 802.11 technology is adaptation of the transmission rates to the changing channel conditions, which is more challenging in vehicular networks. Although rate adaptation problem has been extensively studied for static residential and enterprise network scenarios, there is little work dedicated to the IEEE 802.11 rate adaptation in vehicular networks. Here, the authors are motivated to study the IEEE 802.11 rate adaptation problem in infrastructure-based vehicular networks. First of all, the performances of several existing rate adaptation algorithms under vehicle network scenarios, which have been widely used for static network scenarios, are evaluated. Then, a new rate adaptation algorithm is proposed to improve the network performance. In the new rate adaptation algorithm, the technique of sampling candidate transmission modes is used, and the effective throughput associated with a transmission mode is the metric used to choose among the possible transmission modes. The proposed algorithm is compared to several existing rate adaptation algorithms by simulations, which shows significant performance improvement under various system and channel configurations. An ideal signal-to-noise ratio (SNR)-based rate adaptation algorithm in which accurate channel SNR is assumed to be always available is also implemented for benchmark performance comparison. [ABSTRACT FROM AUTHOR]