In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.

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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Zinc oxide nanoparticles were synthesized using a simple precipitation method with zinc sulfate and sodium hydroxide as starting materials. The synthesized sample was calcined at different temperatures for 2 h. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and proton-induced X-ray emission (PIXE) analysis. SEM images show various morphological changes of ZnO obtained by the above method. The average crystallite sizes of the samples were calculated from the full width at half maximum of XRD peaks by using Debye-Scherrer's formula and were found to be in the nanorange. EDS shows that the above route produced highly pure ZnO nanostructures. PIXE technique was used for trace elemental analysis of ZnO. The optical band gaps of various ZnO powders were calculated from UV-visible diffuse reflectance spectroscopic studies.

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Synthesis, characterization and optical properties of zinc oxide nanoparticles

Structural, FTIR and photoluminescence studies of Cu doped ZnO nanopowders by co-precipitation method

Renewable and sustainable biobased materials: An assessment on biofibers, biofilms, biopolymers and biocomposites

https://run.unl.pt/bitstream/10362/11253/3/New%20natural%20hydraulic%20lime%20mortars_J.Grilo%20et%20al_accepted_20140315.pdf

New natural hydraulic lime mortars – Physical and microstructural properties in different curing conditions

Unlike usual VLSI approaches necessary for the computation of intensive Low-Density Parity-Check (LDPC) code decoders, this paper presents flexible software-based LDPC decoders. Algorithms and data structures suitable for parallel computing are proposed in this paper to perform LDPC decoding on multicore architectures. To evaluate the efficiency of the proposed parallel algorithms, LDPC decoders were developed on recent multicores, such as off-the-shelf general-purpose x86 processors, Graphics Processing Units (GPUs), and the CELL Broadband Engine (CELL/B.E.). Challenging restrictions, such as memory access conflicts, latency, coalescence, or unknown behavior of thread and block schedulers, were unraveled and worked out. Experimental results for different code lengths show throughputs in the order of 1 ~ 2 Mbps on the general-purpose multicores, and ranging from 40 Mbps on the GPU to nearly 70 Mbps on the CELL/B.E. The analysis of the obtained results allows to conclude that the CELL/B.E. performs better for short to medium length codes, while the GPU achieves superior throughputs with larger codes. They achieve throughputs that in some cases approach very well those obtained with VLSI decoders. From the analysis of the results, we can predict a throughput increase with the rise of the number of cores.

Massively LDPC Decoding on Multicore Architectures

Due to huge computational requirements, powerful Low-Density Parity-Check (LDPC) error correcting codes, discovered in the early 1960s, have only recently been adopted by emerging communication standards. LDPC decoders are supported by VLSI technology, which delivers good parallel computational power with excellent throughputs, but at the expense of significant costs.In this work, we propose an alternative flexible LDPC decoder that exploits data-parallelism for simultaneous multicodeword decoding, supported by multithreading on CUDA-based graphics processing units (GPUs). The ratio of arithmetic operations per memory access is low for the efficient min-sum LDPC decoding algorithm proposed, which causes a bottleneck due to memory latency and data collisions. We propose runtime data realignment to allow coalesced parallel memory accesses to be performed by distinct threads inside the same warp. The memory access patterns of LDPC codes are random, which does not admit the simultaneous use of coalescence in both read and write operations of the decoding process. To overcome this problem we have developed a data mapping transformation which allows new addresses to be contiguously accessed for one of the mentioned memory access types. Our implementation shows throughputs above 100Mbps and BER curves that compare well with ASIC solutions.

How GPUs can outperform ASICs for fast LDPC decoding

Low-Density Parity-Check (LDPC) codes are powerful error correcting codes (ECC). They have recently been adopted by several data communication standards such as DVB-S2 and WiMax. LDPCs are represented by bipartite graphs, also called Tanner graphs, and their decoding demands very intensive computation. For that reason, VLSI dedicated architectures have been investigated and developed over the last few years. This paper proposes a new approach for LDPC decoding on graphics processing units (GPUs). Efficient data structures and an new algorithm are proposed to represent the Tanner graph and to perform LDPC decoding according to the stream-based computing model. GPUs were programmed to efficiently implement the proposed algorithms by applying data-parallel intensive computing. Experimental results show that GPUs perform LDPC decoding nearly three orders of magnitude faster than modern CPUs. Moreover, they lead to the conclusion that GPUs with their tremendous processing power can be considered as a consistent alternative to state-of-the-art hardware LDPC decoders.

Massive parallel LDPC decoding on GPU

This article proposes to address, in a tutorial style, the benefits of using Open Computing Language [1] (OpenCL) as a quick way to allow programmers to express and exploit parallelism in signal processing algorithms, such as those used in error-correcting code systems. In particular, we will show how multiplatform kernels can be developed straightforwardly using OpenCL to perform computationally intensive low-density parity-check (LDPC) decoding, targeting them to run on a large set of worldwide disseminated multicore architectures, such as x86 general purpose multicore central processing units (CPUs) and graphics processing units (GPUs). Moreover, devices with different architectures can be orchestrated to cooperatively execute these signal processing applications programmed in OpenCL. Experimental evaluation of the parallel kernels programmed with the OpenCL framework shows that high-performance can be achieved for distinct parallel computing architectures with low programming effort.

Vitor Silva

Papers

New natural hydraulic lime mortars – Physical and microstructural properties in different curing conditions

Massively LDPC Decoding on Multicore Architectures

How GPUs can outperform ASICs for fast LDPC decoding

Massive parallel LDPC decoding on GPU

Portable LDPC Decoding on Multicores Using OpenCL [Applications Corner]