This paper derives theoretical performance of adaptive arrays based on minimum BER algorithms which achieve superior performance to that on MMSE based algorithms such as LMS (least mean square) in the presence of lots...
详细信息
ISBN:
(纸本)9780863418426
This paper derives theoretical performance of adaptive arrays based on minimum BER algorithms which achieve superior performance to that on MMSE based algorithms such as LMS (least mean square) in the presence of lots of interference. Especially, LBER (least BER) algorithm which has been proposed as one of minimum BER algorithms is applied for the derivation in this paper. It is shown that the LBER algorithm can be regarded as a variable gain adaptive algorithm where the variable stepsize is a function of eigenvalues of the channel matrix and transmission signal states. The theoretical performance is confirmed to fit that of computer simulation.
This paper describes the adaptive approach of the Population-based Incremental Learning (PBIL) algorithm, and proposes several Learning Rules aimed to improve its performance. The assessment of such alternatives was m...
详细信息
ISBN:
(纸本)1601322186
This paper describes the adaptive approach of the Population-based Incremental Learning (PBIL) algorithm, and proposes several Learning Rules aimed to improve its performance. The assessment of such alternatives was made in terms of both the convergence time and of the quality of the achieved solutions. Two classical optimization problems were used for the tests: The Job Shop Scheduling problem and the Traveling Salesman problem. The obtained results are very promising and suggest that some of the proposed learning rules have a superior performance, without degrading drastically the quality of the solutions.
In this paper fast adaptive iterative image restoration algorithms are proposed. These algorithms are based on a class of nonadaptive restoration algorithms which exhibit a first or higher order of convergence and som...
详细信息
On-line algorithms are usually analyzed using competitive analysis, in which the performance of on-line algorithm on a sequence is normalized by the performance of the optimal off-line algorithm on that sequence. In t...
详细信息
A cellular nonlinear network (CNN) technology based vision system and computing platform for UAVs is described. It is also demonstrated that how multi-channel visual flow analysis and a classifier driven visual attent...
详细信息
ISBN:
(纸本)156347719X
A cellular nonlinear network (CNN) technology based vision system and computing platform for UAVs is described. It is also demonstrated that how multi-channel visual flow analysis and a classifier driven visual attention-selection mechanism can be efficiently supported by an analogic architecture. The proposed algorithmic framework makes the acquisition of a spatially and temporally consistent image flow possible even in a case of extreme variations in the environment. The proposed spatio-temporal adaptation relies on a feature based optical flow estimation that can be efficiently calculated on available CNN chips.
In this paper we present a pair of QR-RLS adaptive algorithms for second-order Volterra filtering. The algorithms are based solely on Given's rotations. Hence both the algorithms are numerically stable and highly ...
详细信息
The family of matching pursuit algorithms are greedy heuristic methods for basis selection problem. In this paper, we propose an adaptive structure for matching pursuit algorithms that uses a progressive refinement st...
详细信息
ISBN:
(纸本)1604238216
The family of matching pursuit algorithms are greedy heuristic methods for basis selection problem. In this paper, we propose an adaptive structure for matching pursuit algorithms that uses a progressive refinement structure. The proposed adaptive algorithm allows the detector to adjust the tradeoff between the computational complexity and the resolution performance. The novel algorithm is applied to direction of arrival detection problem and it is shown that the algorithm results in a lower computational complexity than the direct implementation of matching pursuit algorithms.
We compare the convergence rates of adaptive algorithms of four kinds: correlation automatic cancellers of interference;quasi-Newton algorithms based on maximum likelihood (ML) estimates of interference correlation ma...
详细信息
Cache-adaptive algorithms are a class of algorithms that achieve optimal utilization of dynamically changing memory. These memory fluctuations are the norm in today's multi-threaded shared-memory machines and time...
详细信息
ISBN:
(纸本)9783959772471
Cache-adaptive algorithms are a class of algorithms that achieve optimal utilization of dynamically changing memory. These memory fluctuations are the norm in today's multi-threaded shared-memory machines and time-sharing caches. Bender et al. [8] proved that many cache-oblivious algorithms are optimally cache-adaptive, but that some cache-oblivious algorithms can be relatively far from optimally cache-adaptive on worstcase memory fluctuations. This worst-case gap between cache obliviousness and cache adaptivity depends on a highly-structured, adversarial memory profile. Existing cache-adaptive analysis does not predict the relative performance of cache-oblivious and cache-adaptive algorithms on non-adversarial profiles. Does the worst-case gap appear in practice, or is it an artifact of an unrealistically powerful adversary? This paper sheds light on the question of whether cache-oblivious algorithms can effectively adapt to realistically fluctuating memory sizes;the paper focuses on matrix multiplication and sorting. The two matrix-multiplication algorithms in this paper are canonical examples of "(a, b, c)-regular" cache-oblivious algorithms, which underlie much of the existing theory on cache-adaptivity. Both algorithms have the same asymptotic I/O performance when the memory size remains fixed, but one is optimally cache-adaptive, and the other is not. In our experiments, we generate both adversarial and non-adversarial memory workloads. The performance gap between the algorithms for matrix multiplication grows with problem size (up to 3.8×) on the adversarial profiles, but the gap does not grow with problem size (stays at 2×) on non-adversarial profiles. The sorting algorithms in this paper are not "(a, b, c)-regular," but they have been well-studied in the classical external-memory model when the memory size does not fluctuate. The relative performance of a non-oblivious (cache-aware) sorting algorithm degrades with the problem size: It incurs up to 6× the nu
The main emphasis in this paper lies on evaluation of adaptive algorithms which are predominantly used. They are LMS and NLMS algorithms respectively. Initially these algorithms are applied on adaptive equalizers. The...
详细信息
暂无评论