Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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

https://www.cs.montana.edu/courses/csci566/Ref/CR-Survey.pdf

NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey

Social Network Analysis

We call methods for achieving high-resolution enlargements of pixel-based images super-resolution algorithms. Many applications in graphics or image processing could benefit from such resolution independence, including image-based rendering (IBR), texture mapping, enlarging consumer photographs, and converting NTSC video content to high-definition television. We built on another training-based super-resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution. Our algorithm requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data. This one-pass super-resolution algorithm is a step toward achieving resolution independence in image-based representations. We don't expect perfect resolution independence-even the polygon representation doesn't have that-but increasing the resolution independence of pixel-based representations is an important task for IBR.

/pdf/example-based-super-resolution-3nf17iliy7.pdf

Example-based super-resolution

In this correspondence, a generalization of rateless codes is proposed. The proposed codes provide unequal error protection (UEP). The asymptotic properties of these codes under the iterative decoding are investigated. Moreover, upper and lower bounds on maximum-likelihood (ML) decoding error probabilities of finite-length LT and Raptor codes for both equal and unequal error protection schemes are derived. Further, our work is verified with simulations. Simulation results indicate that the proposed codes provide desirable UEP. We also note that the UEP property does not impose a considerable drawback on the overall performance of the codes. Moreover, we discuss that the proposed codes can provide unequal recovery time (URT). This means that given a target bit error rate, different parts of information bits can be decoded after receiving different amounts of encoded bits. This implies that the information bits can be recovered in a progressive manner. This URT property may be used for sequential data recovery in video/audio streaming

Rateless Codes With Unequal Error Protection Property

This correspondence introduces a framework to design and analyze low-density parity-check (LDPC) codes over nonuniform channels. We study LDPC codes for channels with nonuniform noise distributions, rate-adaptive coding, and unequal error protection. First, we propose a technique to design LDPC codes for volume holographic memory (VHM) systems for which the noise distribution is nonuniform. We show that the proposed coding scheme has an easy design procedure and results in efficient codes for holographic memories. An important property of the proposed technique is the design of the codes that have a low error floor and low variable node degrees, while maintaining performance close to the Shannon limit. We then show that punctured LDPC codes can be studied as a special case of our design methodology for nonuniform channels. Finally, we propose a method to generate LDPC codes that can provide unequal error protection in addition to having a good overall performance. Moreover, the highly protected bits can be decoded without requiring the entire word to be decoded.

Nonuniform error correction using low-density parity-check codes

This paper investigates decoding of low-density parity-check (LDPC) codes over the binary erasure channel (BEC). We study the iterative and maximum-likelihood (ML) decoding of LDPC codes on this channel. We derive bounds on the ML decoding of LDPC codes on the BEC. We then present an improved decoding algorithm. The proposed algorithm has almost the same complexity as the standard iterative decoding. However, it has better performance. Simulations show that we can decrease the error rate by several orders of magnitude using the proposed algorithm. We also provide some graph-theoretic properties of different decoding algorithms of LDPC codes over the BEC which we think are useful to better understand the LDPC decoding methods, in particular, for finite-length codes.

On decoding of low-density parity-check codes over the binary erasure channel

This article presents a branch of research where the use of molecules to encode and transmit information among nanoscale devices (nanomachines) is investigated as a bio-inspired viable solution to realize nano-communication networks. Unlike traditional technologies, molecular communication is a radically new paradigm, which demands novel solutions, including the identification of naturally existing molecular communication mechanisms, the establishment of the foundations of a molecular information theory, or the development of architectures and networking protocols for nanomachines. The tight connection of this cutting edge engineering research field with biology will ultimately enable both the bio-inspired study of molecular nanonetwork architectures and their realization with tools already available in nature. The testbed described in this article, which is based on a microfluidic device hosting intercommunicating populations of genetically engineered bacteria, is a clear example of this research direction.

Monaco: fundamentals of molecular nano-communication networks

Over the last decade, the tremendous growth in the mobile Internet user population has been accompanied by an equally exciting evolution in wireless data networks. However, quite understandably, the evolution has been distinctly characterized by an increasing degree of heterogeneity along several dimensions such as the access technology, network model, device, and application requirements. This heterogeneity, in turn, imposes a significant challenge on the design of the network protocol stack, and leads to the question: how can the protocol stack at a mobile host cater effectively to the heterogeneous characteristics of the operating environment? In this article we provide an overview of AdaptNet, an adaptive protocol suite for next-generation wireless data networks. AdaptNet consists of protocol solutions at different layers of the protocol stack addressing several problems, including rate adaptation, congestion control, mobility support, and coding. A common underlying theme in the design of the protocols in the AdaptNet suite is adaptiveness to the operating environment. Through high-level discussions, preliminary results, and pointers to relevant related work, we show how AdaptNet achieves the goal of effectively addressing heterogeneity in next-generation wireless data networks.

Faramarz Fekri

Papers

Rateless Codes With Unequal Error Protection Property

Nonuniform error correction using low-density parity-check codes

On decoding of low-density parity-check codes over the binary erasure channel

Monaco: fundamentals of molecular nano-communication networks

AdaptNet: an adaptive protocol suite for the next-generation wireless Internet