Coherent communications in snapping-shrimp dominated ambient noise environments

Medical diagnosis is often hampered by the quality of the images. This happens in a wide range of image modalities. Image noise reduction is a crucial step, however difficult to be accomplished. Bayesian algorithms have been commonly used with success, namely with additive white Gaussian noise (AWGN) model. In fact, the noise corrupting some of the most used medical imaging modalities is not additive neither Gaussian but multiplicative described by Poisson or Rayleigh distributions. This paper proposes a unified framework with automatic hyper parameters estimation. The proposed framework deals with AWGN but also with both Poisson and Rayleigh distributions. The algorithm proposed herein, is based on a maximum a posteriori (MAP) criterion with the edge preserving prior based on the total variation (TV), which avoids the distortion of relevant anatomical details. The denoising technique is performed via single parametric iterative scheme parameterized for each noise model considered. Tests with real data from several medical imaging modalities testify the performance of the algorithm.

The capacity limits of fiber-optic communication systems in the nonlinear regime are not yet well understood. In this paper, we study the capacity of amplitude modulated first-order soliton transmission, defined as the maximum of the so-called time-scaled mutual information. Such definition allows us to directly incorporate the dependence of soliton pulse width to its amplitude into capacity formulation. The commonly used memoryless channel model based on noncentral chi-squared distribution is initially considered. Applying a variance normalizing transform, this channel is approximated by a unit-variance additive white Gaussian noise (AWGN) model. Based on a numerical capacity analysis of the approximated AWGN channel, a general form of capacity-approaching input distributions is determined. These optimal distributions are discrete comprising a mass point at zero (off symbol) and a finite number of mass points almost uniformly distributed away from zero. Using this general form of input distributions, a novel closed-form approximation of the capacity is determined showing a good match to numerical results. Finally, mismatch capacity bounds are developed based on split-step simulations of the nonlinear Schrdinger equation considering both single soliton and soliton sequence transmissions. This relaxes the initial assumption of memoryless channel to show the impact of both inter-soliton interaction and Gordon–Haus effects. Our results show that the inter-soliton interaction effect becomes increasingly significant at higher soliton amplitudes and would be the dominant impairment compared to the timing jitter induced by the Gordon–Haus effect.

Localization of sensor node is imperative in Wireless Sensor Networks (WSN) as data gathered at the user end become meaningless without location information of the node. In this work, impact of anchor position errors on WSN localization using Mobile Anchor Positioning (MAP) Algorithm has been investigated by evaluating two deployment strategies. In deployment I sensor nodes are randomly spread in the entire chosen area (1000 χ 1000) whilst deployment II has nodes spread in the central region of the chosen area. Firstly, MAP Algorithm employing three anchor nodes is investigated for the two deployments. To study the impact of mobile anchors on MAP, anchor number is increased to four and the two deployments are investigated again. Further, this work also investigates the effect of anchor position errors on localization accuracy of sensor nodes. Error term is modelled using Additive White Gaussian Noise (AWGN) Model. Performance metrices considered for validation are Average Root Mean Square Error and Execution time.

An analysis of performance of Serially Concatenated Convolutional Codes (SCCC) is evaluated on Additive White Gaussian Noise model channel, which is used as a basis, and actions of SCCC on Raleigh flat slow and flat fast faded channels are evaluated in this research work. In DCS the SCCC are utilized to reduce the bit error rates. SCCC encoder consists of RSCC. The major thrust is iterative decoding. This research has utilized BCJR and TDA decoding algorithms. This research analyzed SCCC with interleaved scheme. The BPSK modulation was considered. Performance metric for SCCC over Memory less AWGN channel, and memory based channels i.e. Rayleigh slow and fast Faded Channel is BER verses SNR.

Generalized signal-dependent noise model and parameter estimation for natural images

We propose an enhanced log-likelihood ratio (E-LLR) technique for soft-decision forward error correction (SD-FEC) in intensity-modulation direct-detection (IMDD) systems with optical amplifiers and demonstrate its effectiveness in 7-core weakly coupled fiber transmission. In amplified IMDD, square-law photodetection and modulator nonlinearities cause amplified spontaneous emission noise to produce non-Gaussian signal statistics, invalidating conventional LLR assumptions and degrading SD-FEC performance. To address this issue, we abandon the classic additive white Gaussian noise (AWGN) model and introduce a channel model based on multi-parameter chi-square distributions that more accurately reflect optical channel noise. Unlike previous Gaussian and chi-square approaches, E-LLR uses a training sequence to estimate the variance and non-centrality parameters for each pulse-amplitude modulation (PAM) level, generating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> distinct noncentral chi-square distributions for precise LLR computation in FEC decoding. Experimental results show that the proposed approach improves the receiver sensitivity by 1.13dB and reduces the decoding iterations of FEC compared with AWGN-model based schemes. These improvements contribute to reduced system complexity and decoding latency, while still achieving the required bit error rate threshold.

Upper and lower bounds on the capacity and minimum energy-per-bit for general additive white Gaussian noise (AWGN) and frequency-division AWGN (FD-AWGN) relay channel models are established. First, the max-flow min-cut bound and the generalized block-Markov coding scheme are used to derive upper and lower bounds on capacity. These bounds are never tight for the general AWGN model and are tight only under certain conditions for the FD-AWGN model. Two coding schemes that do not require the relay to decode any part of the message are then investigated. First, it is shown that the "side-information coding scheme" can outperform the block-Markov coding scheme. It is also shown that the achievable rate of the side-information coding scheme can be improved via time sharing. In the second scheme, the relaying functions are restricted to be linear. The problem is reduced to a "single-letter" nonconvex optimization problem for the FD-AWGN model. The paper also establishes a relationship between the minimum energy-per-bit and capacity of the AWGN relay channel. This relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energy-per-bit that do not differ by more than a factor of 1.45 for the FD-AWGN relay channel model and 1.7 for the general AWGN model.

We consider the additive white Gaussian noise (AWGN) cognitive fading interference channel consisting of one primary and multiple secondary transmitter-receiver pairs. The secondary transmitters have non-causal knowledge of the primary message. We find a tuple of achievable rates by utilizing the discrete superposition model (DSM), which is a simplified, deterministic channel model. The coding scheme devised for the DSM can be translated into a coding scheme for the AWGN model, where the rate achieved in the AWGN model is at most a constant gap below the rate achieved in the DSM. We then calculate the average throughput of the secondary pairs under the assumption of Rayleigh fading channels. The main result is that our scheme performs well in the weak interference regime. The sum-throughput increases with the number of secondary pairs.

In this paper we study the fundamental limitations in stabilization and tracking of multi-input, multi-output (MIMO) networked feedback systems. We adopt a parallel additive white noise (AWN) model for the MIMO communication channel, and consider as our performance measure the mean square error for a system's output to track a random reference signal with finite power. Of particular interest in our study is the joint design of the controller and the channel to mitigate the requirement on feedback stabilization and to better the tracking performance. To this effect we consider scaled AWN channels, with positive diagonal scaling matrices interpreted as static coders and decoders. By casting the problem as a constrained optimal H 2 control problem and employing duality principles, we derive necessary and sufficient conditions for the system to be mean-square stabilizable and analytical characterizations for the best achievable performance under channel input power constraint. While fundamental limits are known to exist, it is seen that an appropriate channel scaling can be employed to exploit the channel power effectively so as to fundamentally improve a system's stabilizability and tracking performance.

In this paper, the error performance of the tag-to-reader link of a passive UHF (Electronic Product Code - EPC - Generation 2) RFID system is examined. An error-correcting technique is introduced that is based on the cyclic redundancy check (CRC) bits transmitted by the tag with backscatter biphase-M (or FM0) modulation. The proposed technique uses a look-up table that allows the correction of single errors in the received EPC. Numerical results, using additive white Gaussian noise (AWGN) and flat Rayleigh fading channel models, show that a better performance is obtained, in the sense of either reducing the error rate or increasing the distance between tag and reader.

Additive asynchronous and cyclostationary impulsive noise limits\ncommunication performance in OFDM powerline communication (PLC) systems.\nConventional OFDM receivers assume additive white Gaussian noise and hence\nexperience degradation in communication performance in impulsive noise.\nAlternate designs assume a parametric statistical model of impulsive noise and\nuse the model parameters in mitigating impulsive noise. These receivers require\noverhead in training and parameter estimation, and degrade due to model and\nparameter mismatch, especially in highly dynamic environments. In this paper,\nwe model impulsive noise as a sparse vector in the time domain without any\nother assumptions, and apply sparse Bayesian learning methods for estimation\nand mitigation without training. We propose three iterative algorithms with\ndifferent complexity vs. performance trade-offs: (1) we utilize the noise\nprojection onto null and pilot tones to estimate and subtract the noise\nimpulses; (2) we add the information in the data tones to perform joint noise\nestimation and OFDM detection; (3) we embed our algorithm into a decision\nfeedback structure to further enhance the performance of coded systems. When\ncompared to conventional OFDM PLC receivers, the proposed receivers achieve SNR\ngains of up to 9 dB in coded and 10 dB in uncoded systems in the presence of\nimpulsive noise.\n

Objective and subjective evaluations of SPAC(Speech Processing system by use of Auto-Correlation function) in improving the quality of speech corrupted by additive white noise have shown a fairly good improvement in overall speech quality. The primary objective of this work is to verify quantitatively the subjective speech-to-noise-ratio (SNR) improvement achieved by SPAC. A pair comparison test was conducted to evaluate the SNR improvements for speech signal degraded by additive white noise and coding noise of a low bit-rate ADM codec by comparing to additive and multiplicative noise reference signals respectively. Experimental result shows that SPAC provides the statistically significant SNR improvement for both additive noise and coding noise of ADM at the low quality range. The improvement reaches about 6 dB for -5 dB SNR input having additive noise and about 13 dB for 6.7 kHz ADM which is equivalent to the speech signal of about 0 dB SNR having multiplicative noise.

Based on the additive white quantization noise model, linear transform coders are derived for Gaussian sources corrupted by noise. There are two alternative design objectives: minimizing the trace of the error correlation matrix and thus minimizing the mean-squared error, or minimizing the determinant of the error correlation matrix and thus maximizing information rate. It is shown that a solution to both problems is to first transform the noisy observations into canonical coordinates, quantize and apply a Wiener filter in this coordinate system, and then transform the result back to the original coordinates. Canonical coordinates are uncorrelated, and quantization and Wiener filtering are applied to each component independently. The type of canonical coordinate system depends on the design objective: Quantization in half-canonical coordinates minimizes the mean-squared error and quantization in full-canonical coordinates maximizes information rate. Finally, it is also demonstrated in this paper that majorization is the fundamental principle underlying proofs of optimal transform coding.

In this study, we propose a simple spectrum sensing method based on exploiting the properties of a group‐delay phaser. Following the theory that an additive white noise should have a flat spectrum over the band of interest, which is not the case for most data‐modulated signals, the spectrum shape of input waveforms has been the test variable. The latter enables a clear distinguishing method between a noise background and a communication signal of a transmission body operating over the desired band. The phaser scatters the frequency components of received signals in time space, allowing a time‐domain inspection of the corresponding spectral response. The accurate closed‐form analytical expression for the probability of detection in an additive white Gaussian noise (AWGN) channel has been derived, in addition to the false alarm probability. The probability of detection has been studied versus signal‐to‐noise ratio (SNR) in AWGN and multipath channels. As well, the receiver operating characteristic (ROC) curves have been plotted for different values of SNR. A good performance has been achieved by our scheme, which has recorded a detection probability of 0.86 for a false alarm probability of 0.1, and further shown a kind of robustness against noise uncertainties. Experimental works have eventually been conducted, and the empirical results also validate the effectiveness of the elaborated approach. An improvement of around 30% in the probability of detection has been realized over the energy‐based sensing technique, at low measures of false alarm probability.

The increasing demand for high-density and high-speed digital optical recording systems has made the development of advanced coding and signal processing techniques for fast and reliable data recovery increasingly important. In recent years, the parity-check (PC)-code-based reception technique has been widely studied for magnetic recording systems, and it is projected to be highly promising for high-density optical recording. The PC code is an inner error correction code (ECC), which can detect dominant short error events of the system, using only a few parity bits. This reduces the loss in error correction capability of the outer ECC due to random short errors and results in a simple and efficient solution to improve the overall performance. This thesis is dedicated to the design and analysis of PCcode- based recording systems to achieve higher recording capacity with low implementation complexity, for high-density blue laser disc systems. In particular, most of the key components of the PC-code-based optical recording system have been designed and optimized for different recording densities, and different proportions of white noise and media noise. During the development of advanced coding and detection techniques, it becomes necessary to investigate the system’s performance with different coding schemes and recording densities. In Chapter 2 of the thesis, we propose a generalized Braat-Hopkins model for optical recording channels, which provides a fair basis for the performance comparison of detection schemes over different coding schemes and recording densities, for channels with additive, white Gaussian noise (AWGN) and media noise. Various basic issues associated with the PC-code-based systems are investigated in Chapter 3. These include bounds for bit error rates and error event probabilities, the dominant error events at channel detector output, different PC codes, simple and efficient post-processors, the impact of error events that are split across data block boundaries and the corresponding remedy, as well as the effects of code rates and recording densities. Simulation results show that a 4-bit PC code achieves the best performance. The corresponding bit error rates (BERs) are very close to the performance bounds, at both nominal and high densities. Constrained codes, which serve as a key component in the read-write channel of data storage systems, are desired to have a high code rate with low-complexity encoder/decoder. In Chapter 4, we investigate the design of certain capacity-approaching constrained codes for optical recording systems. In particular, we derive analytically the relationship between the number of encoder states and the maximum size of these codes. We identify the minimum number of encoder states that maximizes the code rate, for any desired codeword length. The design of constrained PC codes is key to the development of PCcode- based systems, and the systematic design of efficient constrained PC codes remains a challenging problem. In Chapter 5, we propose a general and systematic code design technique for constructing capacity-approaching constrained PC codes, which can detect any type of error events of the system, with the minimum code rate loss. Compared to the rate 2/3 code without parity, the newly designed constrained 4-bit PC code achieves a performance gain of 2 dB at nominal density, and 1.5 dB at high density, at BER = 10-5. As the dominant noise for high-density optical recording systems, media noise severely degrades the performance of channel detectors and postprocessors that are designed for AWGN. In Chapter 6, we propose two novel modifications to the bit detector to combat media noise. We further develop a data-dependent post-processing scheme for error correction. Compared to the system designed without considering media noise and without PC codes, the overall performance gain of the developed scheme can be more than 11 dB at high media noise levels. In data storage systems, the ECC failure rate (EFR) serves as the ultimate measure of the data recovery performance. In Chapter 7, we develop semi-analytical approaches for estimating the EFR, and analyze EFRs for the developed PC-code-based systems, for the cases without and with interleaving. Our analysis shows that with the optimum interleaving degrees, compared to the rate 9/13 code without parity, the 2-bit PC code achieves a gain of around 0.5 dB, and the 4-bit PC code gains 0.6 dB, at high record ing density and EFR = 10-16. The thesis concludes in Chapter 8 with some suggestions for further work.