http://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/SensorNetworksSurvey.pdf

Wireless sensor networks: a survey

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.

Neural Networks for Pattern Recognition

Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

In this paper, we describe the cognitive radios sharing the spectrum with licensed users and its effects on operational coexistence with unlicensed users. Due to the unlicensed spectrum band growing needs and usage by many IEEE 802.11 protocols, normal wireless radio operation sees high interference leading to high error rates on operational environments. We study the licensed bands and the characteristics of the unlicensed bands, as it is know that the licensed bands have a maximum limit of FCC interference for a licensed set of frequencies. The cognitive algorithm for this probabilistic model for the unlicensed users, uses a model which takes into account the threshold variable ratio Eb/No and also calculates the lower-bound of the combined value of secondary user interference for overlapping frequencies with the primary user. By using simulation, we detect the primary user when the radio frequencies are known a priori and compare it when the frequencies are unknown and needs to cognitively detected. In our analysis we exploit the similarity measure seen at each sub-channel frequencies, which are due to multiple paths of the same reflected signal by maximizing the correlated information of the correlation matrix. For the general case the covariance matrix for blind source separation, we use ICA de-correlation methods and show that cognitive radios can efficiently identify users in complex situations.

/pdf/cognitive-model-selections-in-co-existing-operation-of-36vdlbya6e.pdf

Cognitive Model Selections in Co-existing Operation of Wireless Sensor Networks

Errors in surface code have typically been decoded by Minimum Weight Perfect Matching (MWPM) based method. Recently, neural-network-based Machine Learning (ML) techniques have been employed for this purpose. Here we propose a two-level (low and high) ML-based decoding scheme, where the first level corrects errors on physical qubits and the second one corrects any existing logical errors, for different noise models. Our results show that our proposed decoding method achieves $\sim10 \times$ and $\sim2 \times$ higher values of pseudo-threshold and threshold respectively, than for MWPM. We show that usage of more sophisticated ML models with higher training/testing time, do not provide significant improvement in the decoder performance. Finally, data generation for training the ML decoder requires significant overhead hence lower volume of training data is desirable. We have shown that our decoder maintains a good performance with the train-test-ratio as low as $40:60$.

Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

A computer model for hydrodynamic shearing of DNA — Further investigation on distribution of break lengths: Part III

Betweenness centrality is a classic network measure used to determine prominent nodes in a network G(V, E), where the edges capture a type of flow through the network (like information, material or money) Betweenness being a global centrality measure requires the entire network information to compute the centrality of even a single vertex We consider the setting where the global network structure is not present centrally with a single individual Rather, the data is distributed among many individuals, each having only a partial view of the network Furthermore, confidentiality constraints prevent the individual parties from disclosing their share of the data, thus inhibiting the aggregation of the entire network for analysis The current paper proposes a secure multiparty protocol to compute the betweenness centrality measure, in a privacy preserving manner, for the considered setting Employing various optimizations, including oblivious data structures and oblivious RAM, we present a secure variant of the Brandes algorithm for computing betweenness centrality in unweighted networks The protocol is designed in the semi-honest adversarial model under the two-party setting We evaluate the performance of the designed protocol by implementing them in the Obliv-C framework for secure computation We are the first to provide a benchmark for the implementations using the state of the art ORAM schemes and help identify the best schemes for input data of different sizes Employing the Circuit ORAM and the Square-Root ORAM schemes, we report the complexity of the proposed protocol as $\mathcal {O}(|V||E|\log ^3|E|)$ and $\mathcal {O}(|V||E|^{15}\log ^{15}|E|)$ primitive operations respectively The asymptotic complexity of Circuit ORAM is found to be the least, with an overhead of only $\mathcal {O}(\log ^3|E|)$ compared to the traditional non-oblivious Brandes algorithm with complexity $\mathcal {O}(|V||E|)$

S. Sitharama Iyengar

Papers

Cognitive Model Selections in Co-existing Operation of Wireless Sensor Networks

Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing

A computer model for hydrodynamic shearing of DNA — Further investigation on distribution of break lengths: Part III

Computing Betweenness Centrality: An Efficient Privacy-Preserving Approach

Signal processing for mining information