The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of possibly noisy tests, and is relevant in applications such as medical testing, communica...
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The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of possibly noisy tests, and is relevant in applications such as medical testing, communication protocols, pattern matching, and many more. One of the defining features of the group testing problem is the distinction between the non-adaptive and adaptive settings. In the non-adaptive case, all tests must be designed in advance, whereas in the adaptive case, each test can be designed based on the previous outcomes. While tight information-theoretic limits and near-optimal practical algorithms are known for the adaptive setting in the absence of noise, surprisingly little is known in the noisy adaptive setting. In this paper, we address this gap by providing information-theoretic achievability and converse bounds under various noise models, as well as a slightly weaker achievability bound for a computationally efficient variant. These bounds are shown to be tight or near-tight in a broad range of scaling regimes, particularly at low noise levels. The algorithms used for the achievability results have the notable feature of only using two or three stages of adaptivity.
The noncooperative game with private constraints is studied, where heterogeneous players communicate over a weight-unbalanced digraph. A novel distributed Nash equilibrium (NE) seeking algorithm is adapted for this pr...
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Representative passive adaptive algorithms have been developed with a wide variety of applications. However, to the best of our knowledge, the attempt to unify or to compare them has not been clearly established in li...
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Representative passive adaptive algorithms have been developed with a wide variety of applications. However, to the best of our knowledge, the attempt to unify or to compare them has not been clearly established in literature. In this article, we provide a passive adaptive framework which encompasses all those algorithms including the recently developed proportional-integral (PI) adaptive scheme. A comparative analysis among performances of the passive algorithms is carried out by means of simulations considering the problem of passivity-based adaptive tracking control of a simple pendulum. In addition, passivity property for PI algorithm is rigorously shown in case of linear parametrization. Copyright (C) 2013 John Wiley & Sons, Ltd.
A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least ...
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A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least squares (RLS)-type adaptive algorithms, which achieve an enhanced convergence and tracking performance with low computational cost, as compared with existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.
Currently, monitoring and controlling a bioreactor is crucial for the improvement of any bioprocess behavior. Hence, it is necessary to know the system dynamics, such as reaction rates, which are challenging to descri...
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This article presents a comprehensive overview of the applications of bio-inspired algorithms in planar circuit design. Drawing inspiration from the adaptive and self-organising behaviours observed in biological syste...
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Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. adaptive selection of suitable operators to the characteristics of different optimization stages is an important task...
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The adaptive array which can distinguish the interference from the signal of interest (SoI) by the adaptive algorithm is crucial for radar anti-interference. However, it has a resolution blind area (RBA) that results ...
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For a mechanical antenna system with a perfect parabolic reflector surface of diameter D the antenna gain is proportional to (D/lambda(c))2, where lambda(c) denotes the received carrier wavelength. However, for a prac...
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For a mechanical antenna system with a perfect parabolic reflector surface of diameter D the antenna gain is proportional to (D/lambda(c))2, where lambda(c) denotes the received carrier wavelength. However, for a practical system an increase in D/lambda(c) is also associated with a decrease in antenna efficiency due to the imperfect reflector surface. Since the antenna efficiency is a very sensitive function of the RMS surface deviation or effectively of D/lambda(c), the loss in efficiency can more than offset the increase in directivity. The loss in efficiency results owing to the dispersion of the signal in the focal plane, with the result that only a fraction of the available signal power is collected by the focal feed. A configuration wherein several feeds are placed in the focal plane of the antenna is considered so as to capture all the available power. However, the signal phase and amplitude at the outputs of the various feeds vary randomly with time owing to the time-varying deformation of the reflector surface induced, for example, by vibrational modes set up by gravitational or thermal fields or wind. Coherent signal combining techniques based on an adaptive least-squares algorithm are investigated for nearly optimally and adaptively combining the outputs of these feeds. The performances of the two proposed versions of the least-squares algorithm are evaluated by simulations. It is shown for the example considered that both the adaptive least-squares algorithms are capable of offsetting most of the loss in the antenna gain incurred owing to reflector surface deformations.
In this paper, we introduce and evaluate novel adaptive schemes for neighbor discovery in Bluetooth-enabled ad-hoe networks. In an ad-hoc peer-to-peer setting, neighbor search is a continuous, hence battery draining p...
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In this paper, we introduce and evaluate novel adaptive schemes for neighbor discovery in Bluetooth-enabled ad-hoe networks. In an ad-hoc peer-to-peer setting, neighbor search is a continuous, hence battery draining process. In order to save energy when the device is unlikely to encounter a neighbor, we adaptively choose parameter settings depending on a mobility context to decrease the expected power consumption of Bluetooth-enabled devices. For this purpose, we first determine the mean discovery time and power consumption values for different Bluetooth parameter settings through a comprehensive exploration of the parameter space by means of simulation validated by experiments on real. devices. The fastest average discovery time obtained is 0.2 s, while at an average discovery time of 1 s the power consumption is just 1.5 times that of the idle mode on our devices. We then introduce two adaptive algorithms for dynamically adjusting the Bluetooth parameters based on past perceived activity in the ad-hoc network. Both adaptive schemes for selecting the discovery mode are based only on locally-available information. We evaluate these algorithms in a node mobility simulation. Our adaptive algorithms reduce energy consumption by 50% and have up to 8% better performance over a static power-conserving scheme.
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