On the Average Case Communication Complexity for Detection in Sensor Networks

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space viewpoint is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces $\mathcal{S_I}$ and $\mathcal{S_C}$ and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating $\mathcal{S_I}$ and $\mathcal{S_C}$ is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. The average case CC of the relevant greater-than (GT) function is characterized within two bits. In the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm.

Performance analysis of a suprathreshold, stochastic resonance based nonlinear detector

In this paper we introduce a nonlinear detector based on the phenomenon of suprathreshold stochastic resonance (SSR). We first present a model (an array of 1-bit quantizers) that demonstrates the SSR phenomenon. We then use this as a pre-processor to the conventional matched filter. We employ the Neyman-Pearson(NP) detection strategy and compare the performances of the matched filter, the SSR-based detector and the optimal detector. Although the proposed detector is non-optimal, for non-Gaussian noises with heavy tails (leptokurtic) it shows better performance than the matched filter. In situations where the noise is known to be leptokurtic without the availability of the exact knowledge of its distribution, the proposed detector turns out to be a better choice than the matched filter.

Distributed intrusion detection in the presence of correlated sensor readings: Signal-space and communication-complexity view-point

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space view-point is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces f(s) and f(g) and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating f(s) and f(g) is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication-complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. Extensions to the multi-party case is straightforward and is briefly discussed. The average case CC of the relevant greaterthan (CT) function is characterized within two bits. Under the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm. 2010 Elsevier B.V. All rights reserved.

Signal Detection in Generalized Gaussian Noise by Nonlinear Wavelet Denoising

In this paper, a nonlinear suboptimal detector whose performance in heavy-tailed noise is significantly better than that of the matched filter is proposed. The detector consists of a nonlinear wavelet denoising filter to enhance the signal-to-noise ratio, followed by a replica correlator. Performance of the detector is investigated through an asymptotic theoretical analysis as well as Monte Carlo simulations. The proposed detector offers the following advantages over the optimal (in the Neyman-Pearson sense) detector: it is easier to implement, and it is more robust with respect to error in modeling the probability distribution of noise.

一种高效覆盖的水下传感器网络部署策略

传感器节点的部署直接关系到水下传感器网络的成本和性能。考虑到传感器节点间具有很强的协同能力,该文提出一种基于检测融合的部署策略。采用Neyman-Pearson准则融合单元网格内所有传感器节点的检测信息,实现正方形和正三角形两种单元网格的高效覆盖,进而分别给出针对两种单元网格的监测区域网格划分方法,从而确定监测区域需要的传感器节点数量以及放置的具体位置。通过仿真实验验证了该部署策略的有效性。结果表明,与不采用检测融合时相比,降低了传感器节点冗余度。使用相同数量的传感器节点,新的部署策略能够在保证一定感知质量的基础之上获得更大的覆盖范围。

Permutation tests for nonparametric detection

In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman?Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the permutation and rank tests (detectors) against the parametric ones in radar applications. First, a brief review of some contributions on nonparametric tests is realized. Then, the optimum permutation and rank tests are derived. Finally, a performance analysis is realized by Monte-Carlo simulations for the corresponding detectors, and the results are shown in curves of detection probability versus signal-to-noise ratio

Location-aided distributed primary user identification in a cognitive radio scenario

We address a cognitive radio scenario, where a number of secondary users performs identification of which primary user, if any, is trans- mitting, in a distributed way and using limited location information. We propose two fully distributed algorithms: the first is a direct iden- tification scheme, and in the other a distributed sub-optimal detection based on a simplified Neyman-Pearson energy detector precedes the identification scheme. Both algorithms are studied analytically in a realistic transmission scenario, and the advantage obtained by detec- tion pre-processing is also verified via simulation. Finally, we give details of their fully distributed implementation via consensus aver- aging algorithms.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

Colin Marsh (2008) Becoming a Teacher: Knowledge, Skills and Issues. 4th edition, Frenchs Forrest: Pearson Education Australia, 460 pages

Becoming a Teacher is structured in five very readable sections. The introductory section addresses the nature of teaching and the importance of developing a sense of purpose for teaching in a 21st century classroom. It also introduces some key concepts that are explored throughout the volume according to the particular chapter focus of each part. For example, the chapters in Part 2 explore aspects of student learning and the learning environment and focus on how students develop and learn, learner motivation, developing self esteem and learning environments. The concepts developed in this section, such as human development, stages of learning, motivation, and self-concept are contextualised in terms of theories of cognitive development and theories of social, emotional and moral development. The author, Colin Marsh, draws on his extensive experience as an educator to structure the narrative of chapters in this part via checklists for observation, summary tables, sample strategies for teaching at specific stages of student development, and questions under the heading ‘your turn’. Case studies such as ‘How I use Piaget in my teaching’ make that essential link between theory and practice, something which pre-service teachers struggle with in the early phases of their university course. I was pleased to see that Marsh also explores the contentious and debated aspects of these theoretical frameworks to demonstrate that pre-service teachers must engage with and critique the ways in which theories about teaching and learning are applied. Marsh weaves in key quotations and important references into each chapter’s narrative and concludes every chapter with summary comments, reflection activities, lists of important references and useful web sources. As one would expect of a book published in 2008, Becoming a Teacher is informed by the most recent reports of classroom practice, current policy initiatives and research.

Colin Marsh (2010) Becoming a Teacher : Knowledge, Skills and Issues. 5th edition, Frenchs Forrest : Pearson Education Australia, 459 pages. [Book Review]

In the preface to the fifth edition of Becoming a Teacher, Colin Marsh reminds us that teachers need to have passion, energy and a commitment to enhance students’ learning. This most recent edition certainly provides examples of the author’s wide ranging knowledge and depth of insights that reflect his own commitment to inspirational and dedicated teaching practice. The fifth edition shares those characteristics which made previous editions so worthwhile. Most notable is the subtle but significant dual theme of Marsh’s narrative. That is, first, teaching is a vehicle for increasing the life opportunities of students, and second, teaching is profession that requires continual commitment and critical reflection. These are very important messages for any course that develops teaching methodology. Becoming a Teacher continues to be structured in five readable sections, however the 2010 edition has some exciting new features that warrant the attention of teacher educators and their pre-service students.

Molecular camouflage : making use of protecting groups to control the self-assembly of inorganic janus particles onto metal–chalcogenide nanotubes by Pearson Hardness

Hard and soft: Binding of inorganic Pt@Fe3O4 Janus particles to WS2 nanotubes through their Pt or Fe3O4 domains is governed by the difference in Pearson hardness: the soft Pt block has a higher sulfur affinity than the harder magnetite face; thus the binding proceeds preferentially through the Pt face. This binding preference can be reversed by masking the Pt face with an organic protecting group.

Book Review: "Deviance Conformity and Social Control (4th Edn)" Sharyn L. Roach Anleu Sydney: Pearson, 2006

If the sociology of deviance ‘died’ a few years back, as some have claimed, the continuing significance of deviance for sociologists, in both research and teaching, might be explained in terms of a ‘resurrection’. Sharyn Roach Anleu has been spreading both the good and bad news about deviance for some years now, this being not merely the second coming of her text, but its fourth. In terms of Australian tertiary publishing, this is no small accomplishment and gives further weight to the durability of sociological concern with the sub-field of deviance...