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Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature representations are effectively enhanced. The results are further improved by the iterative refinement. Moreover, our method is quite efficient by taking advantage of a fully convolutional network, which is formulated as an end-to-end learning framework of detection and segmentation. Extensive experimental results on a large-scale database corroborated that our method achieved a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.

This article is from BMC Proceedings , volume 5 . Abstract We develop statistical methods for detecting rare variants that are associated with quantitative traits. We propose two strategies and their combination for this purpose: the iterative regression strategy and the extreme values strategy. In the iterative regression strategy, we use iterative regression on residuals and a multimarker association test to identify a group of significant variants. In the extreme values strategy, we use individuals with extreme trait values to select candidate genes and then test only these candidate genes. These two strategies are integrated into a hybrid approach through a weighting technology. We apply the proposed methods to analyze the Genetic Analysis Workshop 17 data set. The results show that the hybrid approach is the most powerful approach. Using the hybrid approach, the average power to detect causal genes for Q1 is about 40% and the powers to detect FLT1 and KDR are 100% and 68% for Q1, respectively. The powers to detect VNN3 and BCHE are 34% and 30% for Q2, respectively.

This article is from PLoS ONE , volume 9 . Abstract We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis.

The objectives of the proposed MPL component of a SWAMSI continuation will develop bistatic applications of the iterative time reversal techniques to focus along the ocean bottom. Iterative time-reversal provides a simple solution for self-adaptive focusing on strong reflectors (i.e. scattering targets) located on the ocean bottom without relying on predictive or modeling capabilities of the environment and of the target of interest. These robust focusing properties are crucial for mapping large and uncharted area with little signal processing effort involved. With the experiments planned by Schmidt et al, there will be enough data to understand the ultimate limitations of the proposed self adaptive methods.

We propose an Iterative Detection and Decoding (IDD) scheme with Low Density Parity Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems for block-fading $F = 2$ and fast fading Rayleigh channels. An IDD receiver with soft information processing that exploits the code structure and the behaviour of the log likelihood ratios (LLR)'s is developed. Minimum Mean Square Error (MMSE) with Successive Interference Cancellation (SIC) and with Parallel Interference Cancellation (PIC) schemes are considered. The soft \textit{a posteriori} output of the decoder in a block-fading channel with Root-Check LDPC codes has allowed us to create a new strategy to improve the Bit Error Rate (BER) of a MIMO IDD scheme. Our proposed strategy in some scenarios has resulted in up to 3dB of gain in terms of BER for block-fading channels and up to 1dB in fast fading channels.

We propose an Iterative Detection and Decoding (IDD) scheme with Low Density Parity Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems for block-fading $F = 2$ and fast fading Rayleigh channels. An IDD receiver with soft information processing that exploits the code structure and the behaviour of the log likelihood ratios (LLR)'s is developed. Minimum Mean Square Error (MMSE) with Successive Interference Cancellation (SIC) and with Parallel Interference Cancellation (PIC) schemes are considered. The soft \textit{a posteriori} output of the decoder in a block-fading channel with Root-Check LDPC codes has allowed us to create a new strategy to improve the Bit Error Rate (BER) of a MIMO IDD scheme. Our proposed strategy in some scenarios has resulted in up to 3dB of gain in terms of BER for block-fading channels and up to 1dB in fast fading channels.

This paper considers a Iterative Linear Minimum Mean Square Error (LMMSE) detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO) systems with Non-Orthogonal Multiple Access (NOMA), in which all the users interfere with each other both in the time domain and frequency domain. It is well known that the Iterative LMMSE detection greatly reduces the system computational complexity by departing the overall processing into many low-complexity distributed calculations that can be executed in parallel. However, it is generally considered to be suboptimal and achieves relatively poor performance due to its sub-optimal detector. In this paper, we firstly present the matching conditions and area theorems for the iterative detection of the MIMO-NOMA systems. Based on the matching conditions and area theorems, the achievable rate region of the Iterative LMMSE detection is analysed. Interestingly, we prove that by properly design the Iterative LMMSE detection, it can achieve (i) the optimal capacity of symmetric MIMO-NOMA system, (ii) the optimal sum capacity of asymmetric MIMO-NOMA system, (iii) all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA system, (iv) the whole capacity region of two-user and three-user asymmetric MIMO-NOMA systems, in a distributed manner for all cases. Finally, a practical Iterative LMMSE detection design is also proposed for the general asymmetric MIMO-NOMA systems.

Spatial coupling is utilized to improve the performance of iterative channel estimation, multiuser detection, and decoding for multiple-input multiple-input (MIMO) bit-interleaved coded modulation (BICM). Coupling is applied to both coding and BICM---the encoder uses a protograph-based spatially-coupled low-density parity-check (SC LDPC) code. Spatially and temporally coupled (STC) BICM is proposed to enable iterative channel estimation via coupling. Linear minimum mean-squared error (LMMSE) estimation is applied for both channel estimation and detection to reduce the complexity. Tractable density evolution (DE) equations are derived to analyze the convergence property of iterative receivers in the large-system limit, via a tool developed in statistical physics---replica method. The DE analysis implies that the STC BICM can improve the performance of iterative channel estimation especially for higher-order modulation. Numerical simulations show that the STC BICM can provide a significant gain of the performance at high signal-to-noise ratios for 64 quadrature amplitude modulation (QAM), as well as an improvement in the decoding threshold, compared to conventional BICM.

We consider compressive sensing as a source coding method for signal transmission. We concatenate a convolutional coding system with 1-bit compressive sensing to obtain a serial concatenated system model for sparse signal transmission over an AWGN channel. The proposed source/channel decoder, which we refer to as turbo CS, is robust against channel noise and its signal reconstruction performance at the receiver increases considerably through iterations. We show 12 dB improvement with six turbo CS iterations compared to a non-iterative concatenated source/channel decoder.

This paper examines the performance of decision feedback based iterative channel estimation and multiuser detection in channel coded aperiodic DS-CDMA systems operating over multipath fading channels. First, explicit expressions describing the performance of channel estimation and parallel interference cancellation based multiuser detection are developed. These results are then combined to characterize the evolution of the performance of a system that iterates among channel estimation, multiuser detection and channel decoding. Sufficient conditions for convergence of this system to a unique fixed point are developed.

Recovering a large matrix from limited measurements is a challenging task arising in many real applications, such as image inpainting, compressive sensing and medical imaging, and this kind of problems are mostly formulated as low-rank matrix approximation problems. Due to the rank operator being non-convex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation and the low-rank matrix recovery problem is solved through minimization of the nuclear norm regularized problem. However, a major limitation of nuclear norm minimization is that all the singular values are simultaneously minimized and the rank may not be well approximated \cite{hu2012fast}. Correspondingly, in this paper, we propose a new multi-stage algorithm, which makes use of the concept of Truncated Nuclear Norm Regularization (TNNR) proposed in \citep{hu2012fast} and Iterative Support Detection (ISD) proposed in \citep{wang2010sparse} to overcome the above limitation. Besides matrix completion problems considered in \citep{hu2012fast}, the proposed method can be also extended to the general low-rank matrix recovery problems. Extensive experiments well validate the superiority of our new algorithms over other state-of-the-art methods.

We present a comparison of four methods of filtering solar-like variability to increase the efficiency of detection of Earth-like planetary transits by means of box-shaped transit finder algorithms. Two of these filtering methods are the harmonic fitting method and the iterative non-linear filter that, coupled respectively with the Box Least-Square (BLS) and Box Maximum-Likelihood algorithms, demonstrated the best performance during the first detection blind test organized inside the CoRoT consortium. The third method, the 3-spot model, is a simplified physical model of Sun-like variability and the fourth is a simple sliding boxcar filter. We apply a Monte Carlo approach by simulating a large number of 150-day light curves (as for CoRoT long runs) for different planetary radii, orbital periods, epochs of the first transit and standard deviations of the photon shot noise. Stellar variability is given by the Total Solar Irradiance variations as observed close to the maximum of solar cycle 23. After filtering solar variability, transits are searched for by means of the BLS algorithm. We find that the iterative non-linear filter is the best method to filter light curves of solar-like stars when a suitable window can be chosen. As the performance of this filter depends critically on the length of its window, we point out that the window must be as long as possible, according to the magnetic activity level of the star. We show an automatic method to choose the extension of the filter window from the power spectrum of the light curves. The iterative non-linear filter, when used with a suitable choice of its window, has a better performance than more complicated and computationally intensive methods of fitting solar-like variability, like the 200-harmonic fitting or the 3-spot model.

This article is from Korean Journal of Radiology , volume 13 . Abstract Objective: To investigate whether the low-tube-voltage (80-kVp), intermediate-tube-current (340-mAs) MDCT using the Iterative Reconstruction in Image Space (IRIS) algorithm improves lesion-to-liver contrast at reduced radiation dosage while maintaining acceptable image noise in the detection of hepatocellular carcinomas (HCC) in thin (mean body mass index, 24 ± 0.4 kg/m2) adults. Subjects and Methods: A phantom simulating the liver with HCC was scanned at 50-400 mAs for 80, 100, 120 and 140-kVp. In addition, fifty patients with HCC who underwent multiphasic liver CT using dual-energy (80-kVp and 140-kVp) arterial scans were enrolled. Virtual 120-kVP scans (protocol A) and 80-kVp scans (protocol B) of the late arterial phase were reconstructed with filtered back-projection (FBP), while corresponding 80-kVp scans were reconstructed with IRIS (protocol C). Contrast-to-noise ratio (CNR) of HCCs and abdominal organs were assessed quantitatively, whereas lesion conspicuity, image noise, and overall image quality were assessed qualitatively. Results: IRIS effectively reduced image noise, and yielded 29% higher CNR than the FBP at equivalent tube voltage and current in the phantom study. In the quantitative patient study, protocol C helped improve CNR by 51% and 172% than protocols A and B (p < 0.001), respectively, at equivalent radiation dosage. In the qualitative study, protocol C acquired the highest score for lesion conspicuity albeit with an inferior score to protocol A for overall image quality (p < 0.001). Mean effective dose was 2.63-mSv with protocol A and 1.12-mSv with protocols B and C. Conclusion: CT using the low-tube-voltage, intermediate-tube-current and IRIS help improve lesion-to-liver CNR of HCC in thin adults during the arterial phase at a lower radiation dose when compared with the standard technique using 120-kVp and FBP.

Chemicals released in the air can be extremely dangerous for human beings and the environment. Hyperspectral images can be used to identify chemical plumes, however the task can be extremely challenging. Assuming we know a priori that some chemical plume, with a known frequency spectrum, has been photographed using a hyperspectral sensor, we can use standard techniques like the so called matched filter or adaptive cosine estimator, plus a properly chosen threshold value, to identify the position of the chemical plume. However, due to noise and sensors fault, the accurate identification of chemical pixels is not easy even in this apparently simple situation. In this paper we present a post-processing tool that, in a completely adaptive and data driven fashion, allows to improve the performance of any classification methods in identifying the boundaries of a plume. This is done using the Multidimensional Iterative Filtering (MIF) algorithm (arXiv:1411.6051, arXiv:1507.07173), which is a non-stationary signal decomposition method like the pioneering Empirical Mode Decomposition (EMD) method. Moreover, based on the MIF technique, we propose also a pre-processing method that allows to decorrelate and mean-center a hyperspectral dataset. The Cosine Similarity measure, which often fails in practice, appears to become a successful and outperforming classifier when equipped with such pre-processing method. We show some examples of the proposed methods when applied to real life problems.

In this work we aim to discover high quality speech features and linguistic units directly from unlabeled speech data in a zero resource scenario. The results are evaluated using the metrics and corpora proposed in the Zero Resource Speech Challenge organized at Interspeech 2015. A Multi-layered Acoustic Tokenizer (MAT) was proposed for automatic discovery of multiple sets of acoustic tokens from the given corpus. Each acoustic token set is specified by a set of hyperparameters that describe the model configuration. These sets of acoustic tokens carry different characteristics fof the given corpus and the language behind, thus can be mutually reinforced. The multiple sets of token labels are then used as the targets of a Multi-target Deep Neural Network (MDNN) trained on low-level acoustic features. Bottleneck features extracted from the MDNN are then used as the feedback input to the MAT and the MDNN itself in the next iteration. We call this iterative deep learning framework the Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN), which generates both high quality speech features for the Track 1 of the Challenge and acoustic tokens for the Track 2 of the Challenge. In addition, we performed extra experiments on the same corpora on the application of query-by-example spoken term detection. The experimental results showed the iterative deep learning framework of MAT-DNN improved the detection performance due to better underlying speech features and acoustic tokens.

This technical report summarizes the fixed point implementation for lattice-reduction aided detectors. More important, this report illustrates the performance of coded LR aided detectors.

This paper studies a new decentralized resource allocation strategy, named iterative spectrum shaping (ISS), for the multi-carrier-based multiuser communication system, where two coexisting users independently and sequentially update transmit power allocations over parallel subcarriers to maximize their individual transmit rates. Unlike the conventional iterative water-filling (IWF) algorithm that applies the single-user detection (SD) at each user's receiver by treating the interference from the other user as additional noise, the proposed ISS algorithm applies multiuser detection techniques to decode both the desired user's and interference user's messages if it is feasible, thus termed as opportunistic multiuser detection (OMD). Two encoding methods are considered for ISS: One is carrier independent encoding where independent codewords are modulated by different subcarriers for which different decoding methods can be applied; the other is carrier joint encoding where a single codeword is modulated by all the subcarriers for which a single decoder is applied. For each encoding method, this paper presents the associated optimal user power and rate allocation strategy at each iteration of transmit adaptation. It is shown that under many circumstances the proposed ISS algorithm employing OMD is able to achieve substantial throughput gains over the conventional IWF algorithm employing SD for decentralized spectrum sharing. Applications of ISS in cognitive radio communication systems are also discussed.

Multi-input multi-output (MIMO) systems can serve as the building blocks for spectrally efficient mobile multi-user tactical wireless systems; however, care must be taken to translate high per-link spectral efficiency into high network throughput. Mobile tactical networks generally have several features that complicate achievement of high multi-user MIMO spectral efficiency, including non-centralized, infrastructure-free operation and operation when all nodes are mobile, as well as requirements to minimize probability of interception and susceptibility to jamming. The paper presents results from both simulations and an extensive over-the-air measurement campaign, which illustrate the key features needed for spectrally efficient multi-user MIMO systems. High MIMO network throughput can be achieved by combining efficient MAC (taking into account MIMO signal processing resources) and transmitter stream control, as well as high performance MIMO signal processing and waveforms that minimize per-link signal-to-interference-and-noise ratio (SINR) requirements leading to effective spectral reuse. Iterative detection schemes provide such high spectral efficiency at low SINRs, achieving performance that approaches the Shannon bound, and provide scalable complexity. These methods are also well suited to operation in multi-user environments, supporting both interference- mitigation and joint detection approaches. The paper provides analysis and experimental results for waveforms and receiver signal processing for Space-Time Bit Interleaved Coded Modulation using multi- carrier waveforms. Results and parameter trades are presented for variations of this approach using List Sphere Detection and Soft-Symbol Cancellation as well as structures based on convolutional codes and turbo codes.

Under existing detection pipelines, seismic event hypotheses are formed from a parametric description of the waveform data obtained from a single pass over the incoming data stream. The full potential of signal processing algorithms is not being exploited due to simplistic assumptions made about the background against which signals are being detected. A vast improvement in the available computational resources allows the possibility of more sensitive and more robust context-based detection pipelines which glean progressively more information from multiple passes over the data. In the first year of this two year contract we have designed and implemented several extensions to an existing prototype detection framework to demonstrate the feasibility of improving performance from a systematic reprocessing of the raw data. The new components are: signal cancellation for stripping the incoming data stream of repeating and irrelevant signals prior to running primary detectors, adaptive beamforming and matched field processing for suppressing background signals and aftershock sequences, and the testing of event hypotheses by evaluating detection probabilities for both detecting and non-detecting stations, followed by optimized beamforming.

This paper considers a iterative Linear Minimum Mean Square Error (LMMSE) detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO) systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE detection greatly reduces the system computational complexity by departing the overall processing into many low-complexity distributed calculations. However, it is generally considered to be sub-optimal and achieves relatively poor performance. In this paper, we firstly present the matching conditions and area theorems for the iterative detection of the MIMO-NOMA systems. Based on the proposed matching conditions and area theorems, the achievable rate region of the iterative LMMSE detection is analysed. We prove that by properly design the iterative LMMSE detection, it can achieve (i) the optimal sum capacity of MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.

Downlink channel estimation with low pilot overhead is an important and challenging problem in large-scale MIMO systems due to the substantially increased MIMO channel dimension. In this letter, we propose a block iterative support detection (block-ISD) based algorithm for downlink channel estimation to reduce the pilot overhead, which is achieved by fully exploiting the block sparsity inherent in the block-sparse equivalent channel derived from the spatial correlations of MIMO channels. Furthermore, unlike conventional compressive sensing (CS) algorithms that rely on prior knowledge of the sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalized mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 84% pilot overhead than conventional channel estimation techniques.

An adaptive iterative decision multi-feedback detection algorithm with constellation constraints is proposed for multiuser multi-antenna systems. An enhanced detection and interference cancellation is performed by introducing multiple constellation points as decision candidates. A complexity reduction strategy is developed to avoid redundant processing with reliable decisions along with an adaptive recursive least squares algorithm for time-varying channels. An iterative detection and decoding scheme is also considered with the proposed detection algorithm. Simulations show that the proposed technique has a complexity as low as the conventional decision feedback detector while it obtains a performance close to the maximum likelihood detector.

Communications in dispersive direct-sequence code-division multiple-access (DS-CDMA) channels suffer from intersymbol and multiple-access interference, which can significantly impair performance. Joint maximum \textit{a posteriori} probability (MAP) equalization and multiuser detection with error control decoding can be used to mitigate this interference and to achieve the optimal bit error rate. Unfortunately, such optimal detection typically requires prohibitive computational complexity. This problem is addressed in this paper through the development of a reduced state trellis search detection algorithm, based on decision feedback from channel decoders. The performance of this algorithm is analyzed in the large-system limit. This analysis and simulations show that this low-complexity algorithm can obtain near-optimal performance under moderate signal-to-noise ratio and attains larger system load capacity than parallel interference cancellation.

This paper considers a low-complexity Gaussian Message Passing Iterative Detection (GMPID) algorithm for Multiple-Input Multiple-Output systems with Non-Orthogonal Multiple Access (MIMO-NOMA), in which a base station with $N_r$ antennas serves $N_u$ sources simultaneously. Both $N_u$ and $N_r$ are very large numbers and we consider the cases that $N_u>N_r$. The GMPID is based on a fully connected loopy graph, which is well understood to be not convergent in some cases. The large-scale property of the MIMO-NOMA is used to simplify the convergence analysis. Firstly, we prove that the variances of the GMPID definitely converge to that of Minimum Mean Square Error (MMSE) detection. Secondly, two sufficient conditions that the means of the GMPID converge to a higher MSE than that of the MMSE detection are proposed. However, the means of the GMPID may still not converge when $ N_u/N_r < (\sqrt{2}-1)^{-2}$. Therefore, a new convergent SA-GMPID is proposed, which converges to the MMSE detection for any $N_u> N_r$ with a faster convergence speed. Finally, numerical results are provided to verify the validity of the proposed theoretical results.

Under existing detection pipelines, seismic event hypotheses are formed from a parametric description of the waveform data obtained from a single pass over the incoming data stream. The full potential of signal processing algorithms is not being exploited due to simplistic assumptions made about the background against which signals are being detected. A vast improvement in the available computational resources allows the possibility of more sensitive and more robust context-based detection pipelines which glean progressively more information from multiple passes over the data. In the first year of this two year contract we designed and implemented several extensions to an existing prototype detection framework to demonstrate the feasibility of improving performance from a systematic reprocessing of the raw data: signal cancellation for stripping the incoming data stream of repeating and irrelevant signals, adaptive beam forming and matched field processing for suppressing background signals and aftershock sequences, and the testing of event hypotheses by evaluating detection probabilities for both detecting and non-detecting stations, followed by optimized beam forming. In this second year of the contract, we have evaluated and enhanced significantly the signal cancellation procedures, revised the detection framework architecture in order that the procedure can be distributed and scaled efficiently, and developed a procedure for optimal detection and location of aftershocks from a target source region as a component of an iterative pipeline. This last procedure exploits significantly the probability of detection work performed in the first year of the contract.

In this paper, we propose a serially concatenated turbo-encoded faster-than-Nyquist signaling (FTNS) transceiver that takes into account FTNS-specific colored noise effects. The proposed low-complexity receiver carries out soft-decision frequency-domain equalization with the aid of the minimum-mean square error criterion while whitening the colored noise. Simulation results demonstrate that the proposed multi-stage-concatenated FTNS system achieves a better error-ratio performance than previous systems that do not consider colored noise effects in the high-symbol-packing FTNS regime. Furthermore, as an explicit benefit of the proposed iterative decoder, near-capacity performance is achieved with practical decoding complexity.

The detection of chemical vapor plumes using passive hyperspectral sensors operating in the long wave infrared is a challenging problem with many applications. For adequate performance, detection algorithms require an estimate of a scenes background statistics, including the mean and covariance. Diffuse plumes with a large spatial extent are particularly difficult to detect in single-image schemes because of contamination of background statistics by the plume. To mitigate the effects of plume contamination, a first pass of the detector can be used to create a background mask. However, large diffuse plumes are typically not removed by a single pass. Instead, contamination can be reduced by using smoothed detection results as a background mask. In the proposed procedure, a detector bank is run on the cube, and a threshold applied to produce a binary image. The binary image can be modeled as a spatial point process consisting of high density and low density regions. By applying a spatial filter to the detection image, regions with overall higher intensity are detected as containing plume and can be removed from background statistic estimates. The key intuition is that regions with a higher density of hits are more likely to contain plume since plumes are spatially contiguous. We demonstrate with real plume data that this method can drastically improve detection performance over the single-pass method, and explore tradeoffs between different filter sizes and thresholds.

We propose iterative detection and decoding (IDD) algorithms with Low-Density Parity-Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems operating in block-fading and fast Rayleigh fading channels. Soft-input soft-output minimum mean-square error receivers with successive interference cancellation are considered. In particular, we devise a novel strategy to improve the bit error rate (BER) performance of IDD schemes, which takes into account the soft \textit{a posteriori} output of the decoder in a block-fading channel when Root-Check LDPC codes are used. A MIMO IDD receiver with soft information processing that exploits the code structure and the behavior of the log likelihood ratios is also developed. Moreover, we present a scheduling algorithm for decoding LDPC codes in block-fading channels. Simulations show that the proposed techniques result in significant gains in terms of BER for both block-fading and fast-fading channels.

We propose iterative detection and decoding (IDD) algorithms with Low-Density Parity-Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems operating in block-fading and fast Rayleigh fading channels. Soft-input soft-output minimum mean-square error receivers with successive interference cancellation are considered. In particular, we devise a novel strategy to improve the bit error rate (BER) performance of IDD schemes, which takes into account the soft \textit{a posteriori} output of the decoder in a block-fading channel when Root-Check LDPC codes are used. A MIMO IDD receiver with soft information processing that exploits the code structure and the behavior of the log likelihood ratios is also developed. Moreover, we present a scheduling algorithm for decoding LDPC codes in block-fading channels. Simulations show that the proposed techniques result in significant gains in terms of BER for both block-fading and fast-fading channels.

To use an iterative time-reveral techniques to provide robust focusing along the ocean bottom, with little signal processing effort involved and a-priori information on the environment, to enhance the detection and localization of proud or buried target in shallow water. The second goal of providing undersea communications augment this program is being addressed outside the SWAMSI efort but would come together in the final demo.

To use an iterative time-reveral techniques to provide robust focusing along the ocean bottom, with little signal processing effort involved and a-priori information on the environment, to enhance the detection and localization of proud or buried target in shallow water. The second goal of providing undersea communications augment this program is being addressed outside the SWAMSI efort but would come together in the final demo.

Under existing detection pipelines, seismic event hypotheses are formed from a parametric description of the waveform data obtained from a single pass over the incoming data stream. The full potential of signal processing algorithms is not being exploited due to simplistic assumptions made about the background against which signals are being detected. A vast improvement in the available computational resources allows the possibility of more sensitive and more robust context-based detection pipelines which glean progressively more information from multiple passes over the data. Under this contract we have evaluated iterative components at different levels of the pipeline hierarchy. At the level of the raw waveforms, we have evaluated schemes for the detection and cancellation of noise transients which can both reduce the detection capability of a station and supply the parametric datastreams with phase detections which may result in spurious event hypotheses. At the single array level, the sensitivity to sites of monitoring interest can be diminished by energy arriving from other directions, for example from ongoing aftershock sequences. We demonstrate how energy from the direction of interest can be enhanced at the expense of the nuisance energy using adaptive beamforming and empirical matched field processing. The aftershock scenario can have significant consequences for the generation of near real-time bulletins both due to the increased number of events and the deterioration of fully automatic event bulletins due to spurious phase association. We provide proof-of-concept of a system for spawning a targeted process for accurate aftershock characterization in a given source region such that all associated phases are removed from the parametric datastreams. New iterations of Global Association, or equivalent algorithms, would then read in pre-screened detection lists and have a lower likelihood of generating false events.

Sparse code multiple access (SCMA) is a promising multiplexing approach to achieve high system capacity. In this paper, we develop a novel iterative detection and decoding scheme for SCMA systems combined with Low-density Parity-check (LDPC) decoding. In particular, we decompose the output of the message passing algorithm (MPA) based SCMA multiuser detection into intrinsic part and prior part. Then we design a joint detection and decoding scheme which iteratively exchanges the intrinsic information between the detector and the decoder, yielding a satisfied performance gain. Moreover, the proposed scheme has almost the same complexity compared to the traditional receiver for LDPC-coded SCMA systems. As numerical results demonstrate, the proposed scheme has a substantial gain over the traditional SCMA receiver on AWGN channels and Rayleigh fading channels.

Theoretically. multiple-input multiple-output (MIMO) wireless systems can achieve remarkably high spectral efficiency as compared to conventional, single-antenna systems. This report identifies a number of problems which need to be solved in order to implement practical MIMO systems: channel estimation, correlated fading, slow fading, asynchronous reception, and frequency-selective fading. The effects of these non-ideal conditions on the performance of MIMO systems are evaluated. and directions are explored in which solutions may be found. The focus of the report is on MIMO systems employing an iterative (turbo) receiver. The results presented are based on the iterative tree search (ITS) detection scheme developed recently at CRC, but are expected to be typical of most iterative detectors that have been proposed in the MIMO literature. The main conclusion of the report is that iterative channel estimation and further development of an ITS-based detection scheme for asynchronous and wideband reception are the two most promising topics for future research in this area.

The objective of this project is to develop bi-static applications of the iterative time reversal techniques to focus along the ocean bottom. Iterative time-reversal provides a simple solution for self-adaptive focusing on strong reflectors (i.e. scattering targets) located on the ocean bottom without relying on predictive or modeling capabilities of the environment and of the target of interest. These robust focusing properties are crucial for mapping large and uncharted areas with little signal processing effort involved. With the experiments planned by Schmidt et al, there will be enough data to understand the ultimate limitations of the proposed self adaptive methods.

In Spectrally Efficient Frequency Division Multiplexing systems the input data stream is divided into several adjacent subchannels where the distance of the subchannels is less than that of Orthogonal Frequency Division Multiplexing(OFDM)systems. Since the subcarriers are not orthogonal in SEFDM systems, they lead to interference at the receiver side. In this paper, an iterative method is proposed for interference compensation for SEFDM systems. In this method a soft mapping technique is used after each iteration block to improve its performance. The performance of the proposed method is comparable to that of Sphere Detection(SD)which is a nearly optimal detection method.

Finding sparse solutions of underdetermined systems of linear equations is a fundamental problem in signal processing and statistics which has become a subject of interest in recent years. In general, these systems have infinitely many solutions. However, it may be shown that sufficiently sparse solutions may be identified uniquely. In other words, the corresponding linear transformation will be invertible if we restrict its domain to sufficiently sparse vectors. This property may be used, for example, to solve the underdetermined Blind Source Separation (BSS) problem, or to find sparse representation of a signal in an `overcomplete' dictionary of primitive elements (i.e., the so-called atomic decomposition). The main drawback of current methods of finding sparse solutions is their computational complexity. In this paper, we will show that by detecting `active' components of the (potential) solution, i.e., those components having a considerable value, a framework for fast solution of the problem may be devised. The idea leads to a family of algorithms, called `Iterative Detection-Estimation (IDE)', which converge to the solution by successive detection and estimation of its active part. Comparing the performance of IDE(s) with one of the most successful method to date, which is based on Linear Programming (LP), an improvement in speed of about two to three orders of magnitude is observed.

This letter proposes a novel method for accelerating iterative detection for spatially coupled (SC) systems. An SC system is constructed by one-dimensional coupling of many subsystems, which are classified into training and propagation parts. An irregular structure is introduced into the subsystems in the training part so that information in that part can be detected successfully. The obtained reliable information may spread over the whole system via the subsystems in the propagation part. In order to allow the subsystems in the training part to collaborate, shortcuts between them are created to accelerate iterative detection for that part. As an example of SC systems, SC code-division multiple-access (CDMA) systems are considered. Density Evolution for the SC CDMA systems shows that the proposed method can provide a significant reduction in the number of iterations for highly loaded systems.

Linear detectors such as zero forcing (ZF) or minimum mean square error (MMSE) are imperative for large/massive MIMO systems for both the downlink and uplink scenarios. However these linear detectors require matrix inversion which is computationally expensive for such huge systems. In this paper, we assert that calculating an exact inverse is not necessary to find the ZF/MMSE solution and an approximate inverse would yield a similar performance. This is possible if the quantized solution calculated using the approximate inverse is same as the one calculated using the exact inverse. We quantify the amount of approximation that can be tolerated for this to happen. Motivated by this, we propose to use the existing iterative methods for obtaining low complexity approximate inverses. We show that, after a sufficient number of iterations, the inverse using iterative methods can provide a similar error performance. In addition, we also show that the advantage of using an approximate inverse is not limited to linear detectors but can be extended to non linear detectors such as sphere decoders (SD). An approximate inverse can be used for any SD that requires matrix inversion. We prove that application of approximate inverse leads to a smaller radius, which in turn reduces the search space leading to reduction in complexity. Numerical results corroborate our claim that using approximate matrix inversion reduces decoding complexity in large/massive MIMO systems with no loss in error performance.

This paper considers base station cooperation (BSC) strategies for the uplink of a multi-user multi-cell high frequency reuse scenario where distributed iterative detection (DID) schemes with soft/hard interference cancellation algorithms are studied. The conventional distributed detection scheme exchanges {soft symbol estimates} with all cooperating BSs. Since a large amount of information needs to be shared via the backhaul, the exchange of hard bit information is preferred, however a performance degradation is experienced. In this paper, we consider a reduced message passing (RMP) technique in which each BS generates a detection list with the probabilities for the desired symbol that are sorted according to the calculated probability. The network then selects the best {detection candidates} from the lists and conveys the index of the constellation symbols (instead of double-precision values) among the cooperating cells. The proposed DID-RMP achieves an inter-cell-interference (ICI) suppression with low backhaul traffic overhead compared with {the conventional soft bit exchange} and outperforms the previously reported hard/soft information exchange algorithms.

In this paper, we consider the problem of iterative detection and decoding (IDD) for multi-antenna systems using low-density parity-check (LDPC) codes. The proposed IDD system consists of a soft-input soft-output parallel interference (PIC) cancellation scheme with linear minimum mean-square error (MMSE) receive filters and two novel belief propagation (BP) decoding algorithms. The proposed BP algorithms exploit the knowledge of short cycles in the graph structure and the reweighting factors derived from the hypergraph's expansion. Simulation results show that when used to perform IDD for multi-antenna systems both proposed BP decoding algorithms can consistently outperform existing BP techniques with a small number of decoding iterations.