We describe a fault-tolerant distributed storage system for local area networks. Our system implements Persistent, Associative, Shared Object (PASO) memory. A PASO memory stores a set of data objects that can be acces...
详细信息
Rotorcraft vibration suppression has traditionally relied on passive devices, such as vibration isolation systems and dynamic absorbers that are effective at a single rotor speed. However, modern rotorcraft designs ar...
详细信息
In this study, we propose a least mean square-partial parallel interference cancellation (LMS-PPIC) method named parallel LMS-PPIC (PLMS-PPIC) in which the normalised least mean square (NLMS) adaptive algorithm with o...
详细信息
In this study, we propose a least mean square-partial parallel interference cancellation (LMS-PPIC) method named parallel LMS-PPIC (PLMS-PPIC) in which the normalised least mean square (NLMS) adaptive algorithm with optimised chip time-varying step-size is engaged to obtain the cancellation weights. The former LMS-PPIC method is based on fixed not optimised step-size, which causes propagation of error from one stage to the next one and increases the bit error rate (BER). The unit magnitude of the cancellation weights is the principal property in our step-size optimisation. To avoid computational complexity a small set of NLMS algorithms with different step-sizes are executed. In each iteration the parameter estimate of that NLMS algorithm which the elements magnitudes of its cancellation weight estimate have the best match with unit is chosen. Magnificent decrease in BER is achieved by executing the proposed method. Moreover PLMS-PPIC like former LMS-PPIC method comes to practice only when the channel phases are known. When they are unknown, having only their quarters in (0, 2 pi), we propose modified versions of LMS-PPIC and PLMS-PPIC to find the channel phases and the cancellation weights simultaneously. Simulation scenarios are given to compare the performance of our methods with that of LMS-PPIC in two cases: balanced channel and unbalanced channel. The results show that in both cases the proposed method outperforms LMS-PPIC, especially for high processing gains.
Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysi...
详细信息
Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysis, and the amount of these data together with the scarceness of human resources devoted to Energy Management activities who could maintain and update the analyses' output are often the main barriers to its diffusion in companies. Advanced tools such as software based on machine learning techniques are therefore the key to overcome these barriers and allow an easy but accurate control. This type of systems is able to solve complex problems obtaining reliable results over time, but not to understand when the reliability of the results is declining (a common situation considering energy using systems, often undergoing structural changes) and to automatically adapt itself using a limited amount of training data, so that a completely automatic application is not yet available and the automatic energy consumption control using intelligent systems is still a challenge. This paper presents a whole new approach to energy consumption control, proposing a methodology based on Artificial Neural Networks (ANNs) and aimed at creating an automatic energy consumption control system. First of all, three different structures of neural networks are proposed and trained using a huge amount of data. Three different performance indicators are then used to identify the most suitable structure, which is implemented to create an energy consumption control tool. In addition, considering that huge amount of data are not always available in practice, a method to identify the minimum period of data collection to obtain reliable results and the maximum period of usability is described. The general purpose of the work is to allow the automatic utilization of this kind of tools, so a method to identify a lack of accuracy in the model and two different retraining methods are proposed a
The specific features of detecting signals and estimating their parameters in the far-field zone of an antenna in the presence of intense interferences in the near-field zone are considered using modern adaptive algor...
详细信息
The specific features of detecting signals and estimating their parameters in the far-field zone of an antenna in the presence of intense interferences in the near-field zone are considered using modern adaptive algorithms. An imitative simulation shows the possibilities of the adaptive methods for localizing sources in the far- and near-field antenna zones.
First-order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iteratively the learning rate for each coor...
详细信息
First-order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iteratively the learning rate for each coordinate. Despite great practical success in deep learning, their behavior and performance on more general loss functions are not well understood. In this paper, we derive a non-autonomous system of differential equations, which is the continuous time limit of adaptive optimization methods. We study the convergence of its trajectories and give conditions under which the differential system, underlying all adaptive algorithms, is suitable for optimization. We discuss convergence to a critical point in the non-convex case and give conditions for the dynamics to avoid saddle points and local maxima. For convex loss function, we introduce a suitable Lyapunov functional which allows us to study its rate of convergence. Several other properties of both the continuous and discrete systems are briefly discussed. The differential system studied in the paper is general enough to encompass many other classical algorithms (such as Heavy Ball and Nesterov's accelerated method) and allow us to recover several known results for these algorithms.
This paper proposes a method of adaptive metaheuristics that is based on a class of neighborhood search, such as the local search, the simulated annealing and the tabu search. This method uses an adaptive memory, whic...
详细信息
This paper proposes a method of adaptive metaheuristics that is based on a class of neighborhood search, such as the local search, the simulated annealing and the tabu search. This method uses an adaptive memory, which reflects search history. Its construction is simple so that the method realizes an easy coding of an optimization problem. This method is applied to a class of optimal allocation problems of allocating irregular shapes onto a sheet so that the waste of the sheet is minimized. This problem is known to NP-complete and therefore is difficult to solve. The experimental results show the effectiveness of the use of this memory in order to find an optimal solution.
In this paper, we propose new adaptive algorithms for the extraction and tracking of the least (minor) or eventually, principal eigenvectors of a positive Hermitian covariance matrix. The main advantage of our propose...
详细信息
In this paper, we propose new adaptive algorithms for the extraction and tracking of the least (minor) or eventually, principal eigenvectors of a positive Hermitian covariance matrix. The main advantage of our proposed algorithms is their low computational complexity and numerical stability even in the minor component analysis case. The proposed algorithms are considered fast in the sense that their computational cost is O (np) flops per iteration where n is the size of the observation vector and p < n is the number of eigenvectors to estimate. We consider OJA-type minor component algorithms based on the constraint and non-constraint stochastic gradient technique. Using appropriate fast orthogonalization procedures, we introduce new fast algorithms that extract the minor (or principal) eigenvectors and guarantee good numerical stability as well as the orthogonality of their weight matrix at each iteration. In order to have a faster convergence rate, we propose a normalized version of these algorithms by seeking the optimal step-size. Our algorithms behave similarly or even better than other existing algorithms of higher complexity as illustrated by our simulation results. (C) 2011 Elsevier Inc. All rights reserved.
Coherent detection of transmitted signals in wireless communication systems necessitates the need for accurate estimation of Channel State Information (CSI) at the receiver. This is also true for the newly introduced ...
详细信息
Coherent detection of transmitted signals in wireless communication systems necessitates the need for accurate estimation of Channel State Information (CSI) at the receiver. This is also true for the newly introduced multi-user Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) based wireless communication systems. Just like the other wireless communication systems, most of the initial CSI estimation schemes for this system have been based, predominantly, on the use of known training (pilot) signals. This is with the assumption that the wireless communication channel presents rich multipath phenomena to the transmitted signals. However, the assumption of rich multipath in wireless communications systems violated most physical systems in which the wireless channel actually exhibits sparse multipath features. This paper therefore presents a detailed overview of the various channel estimation schemes that have exploited the inherent sparsity in the OFDM channel with the aim of enhancing the estimation of the CSI in the OFDM-IDMA systems. Corresponding comparative results and computational complexities for all these channel estimation schemes are presented and discussed in this paper. Finally, the open research questions for future investigation are also itemized. (C) 2018 Elsevier Inc. All rights reserved.
In view of recent interest in applications of adaptive filtering of multidimensional (m-D) signals in practical problems such as video compression and image enhancement, implementation of a class of m-D infinite impul...
详细信息
In view of recent interest in applications of adaptive filtering of multidimensional (m-D) signals in practical problems such as video compression and image enhancement, implementation of a class of m-D infinite impulse response, adaptive, hyperstable filters is undertaken, While the theoretical results on convergence of such schemes have been made available in our recent work On the topic, the present paper reports our experiments with enhancement of noise-corrupted images for the first time, Aside from implementing the previously introduced m-D HARF algorithm, we also introduce and implement other variants of the algorithm that are conceptually more transparent, computationally less expensive, or converge faster, The new algorithms emerging from this study, namely, the modified m-D HARF and the m-D SHARF are compared with the earlier m-D HARF algorithm by;explicitly deriving measures of computational complexities for both sequential and for parallel implementation, whenever appropriate, Performances of these nem algorithms are also studied both theoretically and experimentally.
暂无评论