Phase noise (PN) generated from imperfect oscillators can destroy transmitted data, for instance, by random rotation of the received signal constellation, amplitude variation in multi-antenna systems, or spectrum regrowth. In this work, we develop a decision-directed PN compensation approach using recursive least squares (RLS) with a forgetting factor to compensate for independent local oscillator PN at a multi-antenna receiver in a frequency-selective system. We employ a single reconfigurable intelligent surface (RIS) configured offline and without PN knowledge via the power method. Simulation results show how RIS-only or RIS-aided links can improve PN estimation and detection over a direct link alone at lower signal-to-noise ratio (SNR) settings and motivate future PN estimation improvements in RIS-aided systems.

A multicell-coordinated beamforming solution for massive multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) systems is presented when employing low-resolution data converters and per-antenna level constraints. For a more realistic deployment, we aim to find the downlink (DL) beamformer that minimizes the maximum power on transmit antenna array of each basestation under received signal quality constraints while minimizing per-antenna transmit power. We show that strong duality holds between the primal DL formulation and its manageable Lagrangian dual problem which can be interpreted as the virtual uplink (UL) problem with adjustable noise covariance matrices. For a fixed set of noise covariance matrices, we claim that the virtual UL solution is effectively used to compute the DL beamformer and noise covariance matrices can be subsequently updated with an associated subgradient. Our primary contributions are then 1) formulating the quantized DL OFDM antenna power minimax problem and deriving its associated dual problem, 2) showing strong duality and interpreting the dual as a virtual quantized UL OFDM problem, and 3) developing an iterative minimax algorithm based on the dual problem. Simulations validate the proposed algorithm in terms of the maximum antenna transmit power and peak-to-average-power ratio.

We consider an integrated access and backhaul (IAB) node operating in full-duplex (FD) mode. We analyze simultaneous transmission from the New Radio gNB to the IAB node on the backhaul uplink, IAB node to a user equipment (UE) on the access downlink, and IAB transmitter to the IAB receiver on the self-interference (SI) channel. Our contributions include (1) a low complexity algorithm to jointly design the hybrid analog/digital beamformers for all three nodes to maximize the sum spectral efficiency of the access and backhaul links by canceling SI and maximizing received power; (2) derivation of all-digital beamforming and spectral efficiency upper bound for use in benchmarking; and (3) simulations to compare full vs. half duplex modes, hybrid vs. all-digital beamforming algorithms, proposed hybrid vs. conventional beamforming algorithms, and spectral efficiency upper bound. In simulations, the proposed algorithm shows significant reduction in SI power and increase in sum spectral efficiency.

In this paper, we provide an analytical framework for full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks with low resolution analog-to-digital and digital-to-analog converters (ADCs and DACs). Matched filters are employed at the FD base stations (BSs) at the transmit and receive sides. For both reverse and forward links, our contributions are (1) derivations of the signal-to-quantization- plus-interference-and-noise ratio (SQINR) for general and special cases; (2) derivations of spectral efficiency for asymptotic cases as well as for power scaling laws; and (3) quantifying effects of quantization error, loopback self-interference, and inter-user interference for hexagonal cells and Poisson Point Process (PPP) tessellations on outage probability and spectral efficiency.

Reconfigurable Intelligent Surfaces (RIS) are passive or semi-passive heterogeneous metasurfaces which consist of many tunable elements. RIS is gaining momentum as a promising new technology to enable transforming the propagation environment into controllable parameter. In this paper, we investigate the co-design of per-subcarrier power allocation matrices and multielement RIS phase shifts in downlink wideband MIMO transmission using 28 GHz frequency bands. Our contributions in improving RIS-aided links include (1) enhanced system modeling with pathloss and blockage modeling, and uniform rectangular array (URA) design, (2) design of gradient ascent co-design algorithm, and (3) asymptotic (Big O) complexity analysis of proposed algorithm and runtime complexity evaluation.

In this work, we present a solution for coordinated beamforming for large-scale downlink (DL) communication systems with low-resolution data converters when employing a perantenna power constraint that limits the maximum antenna power to alleviate hardware cost. To this end, we formulate and solve the antenna power minimax problem for the coarsely quantized DL system with target signal-to-interference-plus-noise ratio requirements. We show that the associated Lagrangian dual with uncertain noise covariance matrices achieves zero duality gap and that the dual solution can be used to obtain the primal DL solution. Using strong duality, we propose an iterative algorithm to determine the optimal dual solution, which is used to compute the optimal DL beamformer. We further update the noise covariance matrices using the optimal DL solution with an associated subgradient and perform projection onto the feasible domain. Through simulation, we evaluate the proposed method in maximum antenna power consumption and peak-to-average power ratio which are directly related to hardware efficiency.

In this work, we present a solution for coordinated beamforming and power allocation when base stations employ a massive number of antennas equipped with low-resolution analog-to-digital and digital-to-analog converters. We address total power minimization problems of the coarsely quantized uplink (UL) and downlink (DL) communication systems with target signal-to-interference-plus-noise ratio (SINR) constraints. By combining the UL problem with minimum mean square error combiners and deriving the Lagrangian dual of the DL problem, we prove UL-DL duality and show there is no duality gap even with coarse data converters. Inspired by strong duality, we devise an iterative algorithm to determine the optimal UL transmit powers, and then linearly amplify the UL combiners with proper weights to acquire the optimal DL precoder. Simulation results validate strong duality and evaluate the proposed method in terms of total power consumption and achieved SINR.

In this paper, we investigate a coordinated multipoint (CoMP) beamforming and power control problem for base stations (BSs) with a massive number of antenna arrays under coarse quantization at low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Unlike high-resolution ADC and DAC systems, non-negligible quantization noise that needs to be considered in CoMP design makes the problem more challenging. We first formulate total power minimization problems of both uplink (UL) and downlink (DL) systems subject to signal-to-interference-and-noise ratio (SINR) constraints. We then derive strong duality for the UL and DL problems under coarse quantization systems. Leveraging the duality, we propose a framework that is directed toward a twofold aim: to discover the optimal transmit powers in UL by developing iterative algorithm in a distributed manner and to obtain the optimal precoder in DL as a scaled instance of UL combiner. Under homogeneous transmit power and SINR constraints per cell, we further derive a deterministic solution for the UL CoMP problem by analyzing the lower bound of the SINR. Lastly, we extend the derived result to wideband orthogonal frequency-division multiplexing systems to optimize transmit power and beamformer for all subcarriers. Simulation results validate the theoretical results and proposed algorithms.

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty.

For low-resolution analog-to-digital converter (ADC) systems, only high-complexity receive antenna selection has been developed and transmit antenna selection has been limited to a single antenna selection in prior work. In this paper, we propose low-complexity receive antenna selection algorithms and analyze transmit antenna selection by considering antenna selection at a base station with large antenna arrays and low-resolution ADCs. For downlink antenna selection, we show a selection criterion with zero-forcing precoding equivalent to a perfect quantization system; sum rate increases with number of selected antennas; derivation of the sum rate loss function from using a antenna subset; and sum rate loss reaches a maximum at a point of total transmit power and decreases beyond that point to converge to zero. For wideband orthogonal-frequency-division-multiplexing (OFDM) systems, our results hold when entire subcarriers share a common subset of antennas. For uplink antenna selection, we generalize a greedy antenna selection criterion; propose a quantization-aware fast antenna selection algorithm using the criterion; and derive a lower bound on sum rate achieved by the proposed algorithm. For wideband OFDM systems, we extend our algorithm and derive a lower bound on its sum rate. Simulation results validate theoretical analyses and show increases in sum rate over conventional algorithms.

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time.

In this paper, we investigate learning-based maximum likelihood (ML) detection for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog- to-digital converters (ADCs). To overcome the significant dependency of learning-based detection on the training length, we propose two one-bit ML detection methods: a biased-learning method and a dithering-and-learning method. The biased-learning method keeps likelihood functions with zero probability from wiping out the obtained information through learning, thereby providing more robust detection performance. Extending the biased method to a system with knowledge of the received signal-to-noise ratio, the dithering-and- learning method estimates more likelihood functions by adding dithering noise to the quantizer input. The proposed methods are further improved by adopting the post likelihood function update, which exploits correctly decoded data symbols as training pilot symbols. The proposed methods avoid the need for channel estimation. Simulation results validate the detection performance of the proposed methods in symbol error rate.

In this paper, we propose a two-stage analog combining architecture for millimeter wave (mmWave) communications with hybrid analog to digital beamforming and low-resolution analog-to-digital converters (ADCs). We first derive a two-stage combining solution by solving a mutual information (MI) maximization problem without a constant modulus constraint on analog combiners. With the derived solution, the proposed receiver architecture splits the analog combining into a channel gain aggregation stage followed by a spreading stage to maximize the MI by effectively managing quantization error. We show that the derived two-stage combiner achieves the optimal scaling law with respect to the number of radio frequency (RF) chains and maximizes the MI for homogeneous singular values of a MIMO channel. Then, we develop a two-stage analog combining algorithm to implement the derived solution under a constant modulus constraint for mmWave channels. Simulation results validate the algorithm performance in terms of MI.

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future performance improvement rewards, we propose two algorithms: 1) closed loop power control (PC) for downlink voice over LTE (VoLTE) and 2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the signal-to-interference plus noise ratio (SINR) of a user equipment (UE) meets the target SINR. It does so without the UE having to send power control requests. The SON fault management algorithm uses RL to improve the performance of an outdoor base station cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL-based algorithms outperform the industry standards today in realistic cellular communication environments.

We investigate uplink user scheduling for millimeter wave (mm-wave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mm-wave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have 1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and 2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mm-wave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. The simulation results validate the sum rate performance of the proposed algorithms and the derived ergodic rate expressions.

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

Millimeter wave (mmWave) spectrum has drawn attention due to its tremendous available bandwidth. The high propagation losses in the mmWave bands necessitate beamforming with a large number of antennas. Traditionally each antenna is paired with a high-speed analog-to-digital converter (ADC), which results in high power consumption. A hybrid beamforming architecture and one-bit resolution ADCs have been proposed to reduce power consumption. However, analog beamforming and one-bit quantization make channel estimation more challenging. In this paper, we propose a narrowband channel estimation algorithm for mmWave communication systems with one-bit ADCs and hybrid beamforming based on generalized approximate message passing (GAMP). We show through simulation that 1) GAMP variants with one-bit ADCs have better performance than do least-squares estimation methods without quantization, 2) the proposed one-bit GAMP algorithm achieves the lowest estimation error among the GAMP variants, and 3) exploiting more frames and RF chains enhances the channel estimation performance.

In millimeter wave (mmWave) systems, we investigate uplink user scheduling when a base station employs low-resolution analog-to-digital converters (ADCs) with a large number of antennas. To reduce power consumption in the receiver, low-resolution ADCs can be a potential solution for mmWave systems in which many antennas are likely to be deployed to compensate for the large path loss. Due to quantization error, we show that the channel structure in the beamspace, in addition to the channel magnitude and beamspace orthogonality, plays a key role in maximizing the achievable rates of scheduled users. Consequently, we derive the optimal criteria with respect to the channel structure in the beamspace that maximizes the uplink sum rate for multi- user multiple input multiple output (MIMO) systems with a zero-forcing receiver. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mmWave systems with low-resolution ADCs. Numerical results validate that the proposed algorithm outperforms conventional user scheduling methods in terms of the sum rate.

A spectrum- and energy-efficient system is essential for millimeter wave communication systems that require large antenna arrays with power-demanding ADCs. We propose an ADC bit allocation (BA) algorithm that solves a minimum mean squared quantization error problem under a power constraint. Unlike existing BA methods that only consider an ADC power constraint, the proposed algorithm regards total receiver power constraint for a hybrid analog-digital beamforming architecture. The major challenge is the non-linearities in the minimization problem. To address this issue, we first convert the problem into a convex optimization problem through real number relaxation and substitution of ADC resolution switching power with constant average switching power. Then, we derive a closed-form solution by fixing the number of activated radio frequency (RF) chains M. Leveraging the solution, the binary search finds the optimal M and its corresponding optimal solution. We also provide an off-line training and modeling approach to estimate the average switching power. Simulation results validate the spectral and energy efficiency of the proposed algorithm. In particular, existing state-of-the-art digital beamformers can be used in the system in conjunction with the BA algorithm as it makes the quantization error negligible in the low-resolution regime.

Millimeter wave (mmWave) systems operating over a wide bandwidth and using a large number of antennas impose a heavy burden on power consumption. In a massive multiple-input multiple-output (MIMO) uplink, analog-to-digital converters (ADCs) would be the primary consumer of power in the base station receiver. This paper proposes a bit allocation (BA) method for mmWave multi-user (MU) massive MIMO systems under a power constraint. We apply ADCs to the outputs of an analog phased array for beamspace projection to exploit mmWave channel sparsity. We relax a mean square quantization error (MSQE) minimization problem and map the closed-form solution to non-negative integer bits at each ADC. In link-level simulations, the proposed method gives better communication performance than conventional low-resolution ADCs for the same or less power. Our contribution is a near optimal low-complexity BA method that minimizes total MSQE under a power constraint.

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here, we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the HDR IMAGE GRADient-based Evaluator. NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Toward ameliorating this we make here the following contributions: 1) we design HDR picture NR IQA models and algorithms using both standard space-domain NSS features as well as novel HDR-specific gradient-based features that significantly elevate prediction performance; 2) we validate the proposed models on a large-scale crowdsourced HDR image database; and 3) we demonstrate that the proposed models also perform well on legacy natural SDR images. The software is available at: http://live.ece.utexas.edu/research/Quality/higradeRelease.zip.

Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multiexposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing image quality assessment models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are: 1) the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators and MEF algorithms, with and without post-processing; 2) a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300 000 opinion scores on 1811 images from over 5000 unique observers; and 3) a detailed study of the correlation performance of the state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images. The database is available at http://signal.ece.utexas.edu/%7Edebarati/HDRDatabase.zip.

In this paper, we propose space-time fronthaul compression of baseband uplink LTE signals for cellular networks, in which baseband units (BBUs) support remote radio heads (RRHs) through fronthaul links. In particular, we assume massive antenna arrays in which the number of antennas in a RRH is much larger than the number of active users. The proposed method consists of two phases: dimensionality reduction phase and individual quantization phase. The key idea of the first phase is to apply principal component analysis (PCA). It performs low-rank approximation of a matrix - composed of received signals - by exploiting the correlation of the received signals across space and time. In the second phase, our method individually quantizes the dimensionality-reduced signal by applying transform coding with bit allocation to reduce the number of quantization bits. An LTE link-level simulator provides numerical results which show that the method achieves up to 8 × compression ratio for the uplink with 64 antennas and 4 active users, along with improvement in communication system performance as a result of denoising.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in the many applications in which humans are the ultimate consumers of visual information. This paper assesses the visual quality in mapping of high dynamic range (HDR) images to standard dynamic range (SDR) images with 8 bits/color/pixel. In previous work, the Tone-Mapped image Quality Index (TMQI) compares the original HDR image with the rendered SDR image. TMQI quantifies distortions locally and pools them by uniform averaging, in addition to measuring naturalness of the SDR image. For SDR images, perceptual pooling strategies have improved correlation of image quality assessment (IQA) algorithms with subjective scores. The primary contributions of this paper are: (1) integrating local information-based pooling strategies in the TMQI IQA algorithm, (2) measuring image naturalness by using mean-subtracted contrast-normalized pixels, and (3) testing the proposed methods on JPEG compressed tone-mapped images and tone-mapped images for SDR displays using subjective scores.

Non-Gaussian noise/interference severely limits communication performance of narrowband powerline communication (PLC) systems. Such noise/interference is dominated by periodic impulsive noise whose statistics varies with the AC cycle. The periodic impulsive noise statistics deviate significantly from that of additive white Gaussian noise, thereby causing dramatic performance degradation in conventional narrowband PLC systems. In this paper, we propose a robust transmission scheme and corresponding receiver methods to combat periodic impulsive noise in OFDM-based narrowband PLC. Towards that end, we propose (1) a time-frequency modulation diversity scheme at the transmitter and a diversity demodulator at the receiver to improve communication reliability without decreasing data rates; and (2) a semi-online algorithm that exploits the sparsity of the noise in the frequency domain to estimate the noise power spectrum for reliable decoding at the diversity demodulator. In the simulations, compared with a narrowband PLC system using Reed-Solomon and convolutional coding, whole-packet interleaving and DBPSK/BPSK modulation, our proposed transceiver methods achieve up to 8 dB gains in Eb/N0with convolutional coding and a smaller-sized interleaver/deinterleaver.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in the many applications in which humans are the ultimate consumers of visual information. For assessing subjective quality of natural images, such as those taken by optical cameras, significant progress has been made for several decades. To aid in the benchmarking of objective image quality assessment (IQA) algorithms, many natural image databases have been annotated with subjective ratings of the images by human observers. Similar information, however, is not as readily available for synthetic images commonly found in video games and animated movies. In this paper, our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database, and (2) evaluating the performance of more than 20 full reference IQA algorithms for natural images on the synthetic image database. The ESPL Synthetic Image Database contains 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format. After collecting 26000 individual human ratings, we compute the differential mean opinion score (DMOS) for each image to evaluate IQA algorithm performance.

Non-Gaussian noise/interference severely limits communication performance of narrowband powerline communication (PLC) systems. Such noise/interference is dominated by periodic impulsive noise whose statistics varies with the AC cycle. The periodic impulsive noise statistics deviates significantly from that of additive white Gaussian noise, thereby causing dramatic performance degradation in conventional narrowband PLC systems. In this paper, we propose a robust transmission scheme and corresponding receiver methods to combat periodic impulsive noise in OFDM-based narrowband PLC. Towards that end, we propose (1) a time-frequency modulation diversity scheme at the transmitter and a diversity demodulator at the receiver to improve communication reliability without decreasing data rates; and (2) a semi-online algorithm that exploits the sparsity of the noise in the frequency domain to estimate the noise power spectrum for reliable decoding at the diversity demodulator. In the simulations, compared with a narrowband PLC system using Reed-Solomon and convolutional coding, whole-packet interleaving and DBPSK/BPSK modulation, our proposed transceiver methods achieve up to 8 dB gains in Eb/N0with convolutional coding and a smaller-sized interleaver/deinterleaver.

In this article, a novel method of performing subband analysis of digital signals is proposed. Conventional subband decomposition algorithms typically use a binary tree filterbank structure comprised of halfband filters. Due to design limitations of finite length filters, conventional decomposition algorithms typically suffer from interference due to aliasing. While longer halfband filters may reduce aliasing, such filters also increase latency and implementation complexity. Our proposed algorithm uses a novel structure of quadrature mirror filters to ensure aliasing is present outside of the spectral region of interest. Simulation results indicate that, compared to conventional algorithms, the proposed algorithm 1) reduces interference from aliasing by over 30dB, 2) reduces signal processing latency, and 3) reduces implementation complexity.

The gyroscope is playing a key role in helping estimate 3D camera rotation for various vision applications on cell phones, including video stabilization and feature tracking. Successful fusion of gyroscope and camera data requires that the camera, gyroscope, and their relative pose to be calibrated. In addition, the timestamps of gyroscope readings and video frames are usually not well synchronized. Previous paper performed camera-gyroscope calibration and synchronization offline after the entire video sequence has been captured with restrictions on the camera motion, which is unnecessarily restrictive for everyday users to run apps that directly use the gyroscope. In this paper, we propose an online method that estimates all the necessary parameters, whereas a user is capturing video. Our contributions are: 1) simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter and 2) generalization of the multiple-view coplanarity constraint on camera rotation in a rolling shutter camera model for cell phones. The proposed method is able to estimate the needed calibration and synchronization parameters online with all kinds of camera motion and can be embedded in gyro-aided applications, such as video stabilization and feature tracking. Both Monte Carlo simulation and cell phone experiments show that the proposed online calibration and synchronization method converge fast to the ground truth values.

Periodic impulsive noise synchronous to the main powerline frequency is the dominant noise component in OFDM-based narrowband (NB) powerline communications (PLC). Such noise occurs in periodic bursts, where a single burst could corrupt multiple OFDM symbols. Standardized NB PLC systems use frequency-domain interleaving (FDI) in combination with forward error correction to combat impulsive noise. Alternate designs adopt time-domain block interleaving (TDI) in which the receiver deinterleaver scatters an impulsive noise burst into short impulses over a large number OFDM symbols. In bursty impulsive noise, TDI-OFDM (FDI-OFDM) works better at high (low) SNR. In this paper, we develop non-parametric methods for periodic impulsive noise mitigation in coded TDI-OFDM systems. We exploit the sparse structure of the time-domain noise after the deinterleaver, and propose sparse Bayesian learning based algorithms that estimate and remove the noise impulses by observing the null and pilot tones of received signal and using decision feedback from the decoder. The proposed methods do not assume any statistical noise model and hence do not require any training. In simulations, the proposed methods in TDI-OFDM systems achieve up to 6 dB SNR gain over FDI-OFDM systems at typical NB PLC SNR values.

We present a novel video stabilization method for cell phone cameras. Our video stabilization is based on a pure 3D rotation motion model, which can better capture the motion of the camera compared with 2D models. 3D camera rotation can be reliably captured by a gyroscope as commonly found on a smart phone or tablet. In this paper we directly smooth the sequence of camera rotation matrices for the video frames. Our contributions are (1) a smoothness metric for a sequence of 3D rotation matrices based on geodesic distance on a non-linear manifold, and (2) an efficient global motion smoothing algorithm using manifold optimization. Our smoothness metric better exploits the manifold structure of sequences of rotation matrices. Experimental results show that our video stabilization method outperforms state-of-the-art methods by generating more stable and visually pleasant videos.

Asynchronous impulsive noise and periodic impulsive noises limit communication performance in OFDM powerline communication systems. Conventional OFDM receivers that assume additive white Gaussian noise experience degradation in communication performance in impulsive noise. Alternate designs assume a statistical noise model and use the model parameters in mitigating impulsive noise. These receivers require training overhead for parameter estimation, and degrade due to model and parameter mismatch. To mitigate asynchronous impulsive noise, we exploit its sparsity in the time domain, and apply sparse Bayesian learning methods to estimate and subtract the noise impulses. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones; (2) we add the information in the date tones to perform joint noise estimation and symbol detection; (3) we use decision feedback from the decoder to further enhance the accuracy of noise estimation. These algorithms are also embedded in a time-domain block interleaving OFDM system to mitigate periodic impulsive noise. Compared to conventional OFDM receivers, the proposed methods achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in asynchronous impulsive noise, and up to 6 dB in coded systems in periodic impulsive noise.

The five papers in this special section are devoted to the topic of new software and hardware programs and services used for error-tolerant multimedia applications.

Many wireless data communication systems such as LTE, and Wi-Fi, are increasingly facing interference that is much stronger than thermal noise. Interference may arise due to dense spatial reuse of spectrum intended to increase user data rates, or from other devices emitting radiation in the same spectrum, or from electronic circuitry within the communication platform. For a multi-antenna receiver operating in an interference-limited channel, we evaluate four diversity combining algorithms in terms of outage probability in the low-outage regime. The contributions of this paper are (1) derivation of closed-form expressions for the output signal-to-interference ratio (SIR) statistics of fixed weight, maximal ratio, selection and post-detection combining; (2) comparison of the relative outage performance of these algorithms; and (3) proposed diversity combining algorithms to reduce outage probability. Our results can be applied in analyzing the outage performance and throughput capacity of both centralized and decentralized interference-limited wireless networks.

A Smart Grid intelligently monitors and controls energy flows in an electric grid. Having up-to-date distributed readings of grid conditions helps utilities efficiently scale generation up or down to meet demand. Narrowband powerline communication (PLC) systems can provide these up-to-date readings from subscribers to the local utility over existing power lines. A key challenge in PLC systems is overcoming additive non-Gaussian noise. In this paper, we propose to use a cyclostationary model for the dominant component of additive non-Gaussian noise. The key contributions are (1) fitting measured data from outdoor narrowband PLC system field trials to a cyclostationary model, and (2) developing a cyclostationary noise generation model that fits measured data. We found that the period in the cyclostationary model matched half of the period of the main powerline frequency, which is consistent with previous work in indoor PLC additive noise modeling.

Video acquired by handheld CMOS cameras may suffer from rolling shutter artifacts. Rolling shutter artifacts, which are due to the rows in the image sensor array being exposed sequentially from top to bottom, increase with the speed of the relative motion between the scene and camera. To rectify these artifacts, one needs to recover the projection parameters for each row. In this paper, we propose a probabilistic method to estimate 3-D camera rotation by using video and inertial measurements on the handheld platform, such as a smart phone. Our contributions are (1) an efficient sensor fusion algorithm using an extended Kalman filter, and (2) a quality assessment method using vanishing point detection. Experiments indicate that the proposed sensor fusion algorithm produces a more accurate orientation estimate and better rectifies rolling shutter artifacts.

A video display device having a lower number of bits per pixel than that required by the video to be displayed quantizes the video prior to its display. Halftoning can perform this quantization while attempting to reduce the visibility of certain quantization artifacts. Quantization artifacts are, nevertheless, not eliminated. A temporal artifact known as dirty-window effect (DWE) can be commonly observed in medium frame-rate binary video halftones. In this paper, we propose video halftone enhancement algorithms to reduce DWE. We assess the performance of the proposed algorithms by presenting objective measures for DWE in the original and the improved halftone videos. The expected contributions of this paper include three medium frame-rate binary video halftone enhancement algorithms that do the following: 1) reduce DWE under a spatial quality constraint; 2) reduce DWE under a spatial quality constraint with reduced complexity; and 3) reduce DWE under spatial and temporal quality constraints.

Communication in decentralized wireless networks is limited by interference. Because transmissions typically last for more than a single contention time slot, interference often exhibits a strong statistical dependence over time that results in temporally correlated communication performance. The temporal dependence in interference increases as user mobility decreases and/or the total transmission time increases. We propose a network model that spans the extremes of temporal independence to long-term temporal dependence. Using the proposed model, closed-form single hop communication performance metrics are derived that are asymptotically exact in the low outage regime. The primary contributions are (i) deriving the joint temporal statistics of network interference and showing that it follows a multivariate symmetric alpha stable distribution; (ii) utilizing the joint interference statistics to derive closed-form expressions for local delay, throughput outage probability, and average network throughput; and (iii) using the joint interference statistics to redefine and analyze the network transmission capacity that captures the throughput-delay-reliability tradeoffs in single hop transmissions. Simulation results verify the closed-form expressions derived in this paper and we demonstrate up to 2× gain in network throughput and reliability by optimizing certain parameters of medium access control layer protocol in view of the temporal correlations.

Additive asynchronous impulsive noise limits communication performance in certain OFDM systems, such as powerline communications, cellular LTE and 802.11n systems. Under additive impulsive noise, the fast Fourier transform (FFT) in the OFDM receiver introduces time-dependence in the subcarrier noise statistics. As a result, complexity of optimal detection becomes exponential in the number of subcarriers. Many previous approaches assume a statistical model of the impulsive noise and use parametric methods in the receiver to mitigate impulsive noise. Parametric methods degrade with increasing model mismatch, and require training and parameter estimation. In this paper, we apply sparse Bayesian learning techniques to estimate and mitigate impulsive noise in OFDM systems without the need for training. We propose two non-parametric iterative algorithms: (1) estimate impulsive noise by its projection onto null and pilot tones so that the OFDM symbol is recovered by subtracting out the impulsive noise estimate; and (2) jointly estimate the OFDM symbol and impulsive noise utilizing information on all tones. In our simulations, the estimators achieve 5dB and 10dB SNR gains in communication performance respectively, as compared to conventional OFDM receivers.

Powerline distribution networks are increasingly being employed to support smart grid communication infrastructure and in-home LAN connectivity. However, their primary function of power distribution results in a hostile environment for communication systems. In particular, asynchronous impulsive noise, with levels as high as 50dB above thermal noise, causes significant degradation in communication performance. Much of the prior work uses limited empirical measurements to propose a statistical model for instantaneous statistics of asynchronous noise. In this paper, we (i) derive a canonical statistical-physical model of the instantaneous statistics of asynchronous noise based on the physical properties of the PLC network, and (ii) validate the distribution using simulated and measured PLC noise data. The results of this paper can be used to analyze, simulate, and mitigate the effect of the asynchronous noise on PLC systems.

Real-time streaming signal processing systems typically de sire high throughput and low latency. Many such systems can be modeled as synchronous data flow graphs. In this paper, we address the problem of multi-objective mapping of SDF graphs onto heterogeneous multi-processor platforms. The primary contributions include (1) an integer linear programming (ILP) model that globally optimizes throughput, latency and cost; (2) a low-complexity two-stage heuristic based on a combination of an evolutionary algorithm with an ILP to generate either a single sub-optimal mapping solution or a Pareto front for design space optimization. In our simulations, the proposed heuristic shows a 10-6gap from the ILP optimal solution, with up to 12× better run-time efficiency.

An IEEE 802.11b/g/n wireless local area network (WLAN) experiences significant radio frequency interference (RFI) from microwave ovens, cordless phones, Bluetooth, and other WLANs operating in the 2.4 GHz band. In the 2.4 GHz band, operating microwave ovens emit interference that can either prevent an access point from transmitting or cause dramatic increase in bit errors in the receiver. This paper reduces disruption in real-time transmission and reception of high-definition streaming video by mitigating oven-generated RFI. The proposed contributions of this paper include (1) a statistical-physical model of oven-generated RFI in WLAN channels that is dependent on distance and frequency, (2) an increase in the information rate bound of up to 5 bits/s/Hz due to the more realistic statistical-physical model of oven-generated RFI, and (3) a proposed transmission strategy to achieve the higher information rate bound.

Image deconvolution aims to recover an image that has been degraded by a linear operation such as blurring during image acquisition. Deconvolution based on maximum a-posteriori (MAP) estimation requires the global prior probability of the original image. Conventional methods usually model the image priors by uniformly characterizing the statistical properties of either some forward measurements of images or the representation coefficients in frames, neglecting the local image statistics. In this paper, we adopt local sparse representation in image deconvolution. Our contributions include proposing (1) a joint model of natural images combining sparse representation of image patches and sparse gradient priors, and (2) an efficient iterative algorithm to infer the MAP estimate of image deconvolution using the proposed model. Experiments indicate that the proposed method can recover the original image with high peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index compared with state-of-the-art methods.

Channel shortening filters have been used in acoustics to reduce reverberation, in error control decoding to reduce complexity, and in communication systems to reduce inter-symbol interference. The cascade of a channel and a channel shortening filter would produce an overall impulse response that has more of the energy in the channel impulse response compacted into fewer adjacent samples. Once designed, channel shortening filters operate on a per-sample basis. In this paper, we evaluate sparse FIR filters, which use more design complexity and less per-sample processing complexity, for channel shortening. Our contributions include (1) proposing a new sparse FIR filter design method for channel shortening, and (2) evaluating design tradeoffs in energy compaction vs. implementation complexity for sparse and non-sparse FIR filters. Our simulation results for ADSL channels show that sparse designs could achieve the same energy compaction with half as many coefficients than non-sparse FIR filters for low filter orders.

With increasing spatial reuse of the radio spectrum, co-channel interference is becoming a dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference from an annulus field of Poisson interferers. Our contributions include (1) demonstrating the applicability of the symmetric alpha stable and Middleton Class A distributions in modeling co-channel interference in various topologies of interferers, and (2) deriving analytical conditions on the system parameters for which these distributions accurately model the interference statistics. Through simulation, we compare the decay rate of tail probabilities of the empirical co-channel interference and the symmetric alpha stable, Middleton Class A, and Gaussian models for different topologies of interferers. Practical applications include co-channel interference modeling for various wireless network environments, including ad hoc and cellular networks.

With increasing spatial reuse of the radio spectrum, co-channel interference is becoming the dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference. Statistical modeling of interference is a useful tool to analyze the outage probabilities in wireless networks and to design interference-aware transceivers. Our contributions include (1) developing a unified framework to derive interference models for various wireless network environments, (2) demonstrating the applicability of the symmetric alpha stable and Middleton class A distributions in modeling co-channel interference in ad-hoc and cellular network environments, and (3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Simulation results allow us to compare the key properties of empirical co-channel interference and their statistical models under different wireless network environments.

Multi-input multi-output (MIMO) receivers have generally been designed and their communication performance analyzed under the assumption of additive Gaussian noise. Wireless transceivers, however, may also be affected by radio frequency interference (RFI) that is well modeled using non-Gaussian impulsive statistics. In this paper, we derive bounds on the communication performance for a two transmit, two receive antenna MIMO system in the presence of RFI. Our contributions include derivation of (1) channel capacity in the presence of RFI, (2) probability of symbol error for uncoded transmissions, and (3) Chernoff bound on the pairwise error probability and cutoff rate as a measure of the throughput performance for coded transmissions. Comparison with the communication performance bounds for receivers designed assuming additive Gaussian noise demonstrates degradation in communication performance in the presence of RFI.

Previous research efforts on OFDMA resource allocation have typically assumed the availability of perfect channel state information (CSI). Unfortunately, this is unrealistic, primarily due to channel estimation errors, and more importantly, channel feedback delay. In this paper, we develop optimal resource allocation algorithms for the OFDMA downlink assuming the availability of only partial (imperfect) CSI. We consider both continuous and discrete ergodic weighted sum rate maximization subject to total power constraints, and average bit error rate constraints for the discrete rate case. We approach these problems using a dual optimization framework, allowing us to solve these problems with O(MK) complexity per symbol for an OFDMA system with K used subcarriers and M active users, while achieving relative optimality gaps of less than 10-5for continuous rates and less than 10-3for discrete rates in simulations based on realistic parameters.

Research in cognitive radios has renewed interest in tools, such as spectrum estimation and modulation identification, to characterize the radio frequency (RF) environment. The use of multiple antennas for multiple-input multiple-output (MIMO) communications presents a new challenge in detecting and classifying signals. In this paper, we propose a cyclostationarity-based statistical test to detect space-time block codes, focusing on the two transmitter Alamouti space-time block code (STBC). Our test exploits a new characterization of the Alamouti code using fourth order cyclic frequencies. The test requires only a single receive antenna, and does not require any symbol synchronization.

Multi-input multi-output (MIMO) receivers have been designed and their communication performance analyzed under the assumption of additive Gaussian noise. Wireless transceivers, however, may be affected by radio frequency interference (RFI) that is well modeled using non-Gaussian impulsive statistics. In this paper, we consider the problem of receiver design for a two transmit, two receive antenna MIMO system in the presence of RFI. First, we show that RFI is well modeled using a bivariate Middleton Class A model and validate the model with measured data. Using this RFI model, we demonstrate that conventional MIMO receivers experience significant degradation in communication performance. Then we derive the maximum likelihood (ML) receiver assuming bivariate Middleton Class A noise. Furthermore, we develop a parameter estimation method for this noise model and propose two sub-optimal ML receivers with reduced computational complexity. Simulations show significant improvement in symbol error rate performance of the proposed techniques over receivers designed assuming additive Gaussian noise.

In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is impulsive in nature. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10-5when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model impulsive noise using Middleton class A and symmetric alpha stable (SaS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of parameter estimation algorithms to measured RFI data from the computer platform, (3) the communication performance vs. computational complexity tradeoffs for the correlation receiver, Wiener filter, and Bayesian detector, and (4) the performance of myriad filtering in combating RFI interference modeled as SaS interference.

OFDMA resource allocation assigns subcarriers and power, and possibly data rates, to each user. Previous research efforts to optimize OFDMA resource allocation with respect to communication performance have focused on formulations considering only instantaneous per-symbol rate maximization, and on solutions using suboptimal heuristic algorithms. This paper intends to fill gaps in the literature through two key contributions. First, we formulate continuous and discrete ergodic weighted sum rate maximization in OFDMA assuming the availability of perfect channel state information (CSI). Our formulations exploit time, frequency, and multi-user diversity, while enforcing various notions of fairness through weighting factors for each user. Second, we derive algorithms based on a dual optimization framework that solve the OFDMA ergodic rate maximization problem with O(MK) complexity per OFDMA symbol for M users and K subcarriers, while achieving data rates shown to be at least 99.9999% of the optimal rate in simulations based on realistic parameters. Hence, this paper attempts to demonstrate that OFDMA resource allocation problems are not computationally prohibitive to solve optimally, even when considering ergodic rates.

In this paper, we propose a resource allocation algorithm for ergodic weighted-sum rate maximization in downlink OFDMA systems. In contrast to most previous research that focused on maximizing instantaneous rates using deterministic optimization techniques, we focus on maximizing ergodic rates using stochastic optimization techniques, which allow us to exploit the temporal dimension, in addition to the frequency and multiuser dimensions. Furthermore, in contrast to most previous algorithms that used greedy suboptimal heuristics with quadratic complexity, we use a dual optimization approach that resulted in a simple subcarrier, rate, and power allocation algorithm that has complexity O(MK) for an M-user, K-subcarrier OFDMA system. Surprisingly, our method is shown to result in duality gaps less than 10-4in scenarios of practical interest, thereby allowing us to claim practical optimality. We present simulation results for a 3GPP-LTE system employing adaptive modulation.

In this paper, we derive an optimal resource allocation algorithm for ergodic weighted-sum capacity maximization in OFDMA systems assuming the availability of only partial (imperfect) CSI. Using a dual optimization framework, we show that the optimal power allocation is multi-level waterfilling based on the conditional expected value of the channel-to-noise ratio, and the optimal user is selected based on the maximum weighted conditional expected capacity penalized by the required power. The algorithm is shown to have O(MK) complexity for an OFDMA system with K used subcarriers and M active users, and achieves relative duality gaps of less than 10-5(99.99999% optimal).

In the process network (PN) model, multiple concurrent processes communicate over unidirectional FIFO queues. PN is useful for modeling signal processing systems of streaming data, and naturally captures parallelism in these systems. PN provides formal execution properties to alleviate the difficulties of threaded and distributed programming, and naturally maps onto parallel and distributed targets. For a large class of PN, clever run-time scheduling can permit execution in bounded memory. In general, PN termination and boundedness cannot be statically determined, so correct bounded scheduling of PN requires run-time deadlock detection. We present the first algorithm that correctly performs dynamic deadlock detection and resolution for bounded scheduling of PN. The proposed algorithm is a modification of a distributed deadlock detection algorithm by Mitchell and Merritt.

Previous research efforts on OFDMA resource allocation have typically assumed the availability of perfect channel state information (CSI). Unfortunately, this is unrealistic, primarily due to channel estimation errors, and more importantly, channel feedback delay. In this paper, we develop optimal resource allocation algorithms for OFDMA systems assuming the availability of only partial (imperfect) CSI. We consider ergodic weighted sum discrete rate maximization subject to total power constraints. We approach this problem using a dual optimization framework, allowing us to solve this problem with O(MK) complexity per symbol for an OFDMA system with K used subcarriers and M active users, while achieving relative optimality gaps of less than 10-3(99.999% optimal).

Previous research efforts to optimize OFDMA resource allocation with respect to communication performance have focused on formulations considering only instantaneous per-symbol rate maximization, and on solutions using suboptimal heuristic algorithms. This paper intends to fill gaps in the literature through two key contributions. First, we formulate weighted sum ergodic capacity maximization in OFDMA assuming the availability of perfect channel state information (CSI). Our formulations exploit time, frequency, and multi-user diversity, while enforcing various notions of fairness through weighting factors for each user. Second, we derive algorithms based on a dual optimization framework that solve the OFDMA ergodic capacity maximization problem with O (MK) complexity per OFDMA symbol for M users and K subcarriers, while achieving data rates shown to be at least 99.9999% of the optimal rate in simulations based on realistic parameters. Hence, this paper attempts to demonstrate that OFDMA resource allocation problems are not computationally prohibitive to solve optimally, even when considering ergodic rates.

At the time of image acquisition, professional photographers apply many rules of thumb to improve the composition of their photographs. This paper develops a joint optical-digital processing framework for automating composition rules during image acquisition for photographs with one main subject. Within the framework, we automate three photographic composition rules: repositioning the main subject, making the main subject more prominent, and making objects that merge with the main subject less prominent. The idea is to provide to the user alternate pictures obtained by applying photographic composition rules in addition to the original picture taken by the user. The proposed algorithms do not depend on prior knowledge of the indoor/outdoor setting or scene content. The proposed algorithms are also designed to be amenable to software implementation on fixed-point programmable digital signal processors available in digital still cameras.

Grayscale error diffusion introduces nonlinear distortion (directional artifacts and false textures), linear distortion (sharpening), and additive noise. Tone-dependent error diffusion (TDED) reduces these artifacts by controlling the diffusion of quantization errors based on the input graylevel. We present an extension of TDED to color. In color-error diffusion, which color to render becomes a major concern in addition to finding optimal dot patterns. We propose a visually meaningful scheme to train input-level (or tone-) dependent color-error filters. Our design approach employs a Neugebauer printer model and a color human visual system model that takes into account spatial considerations in color reproduction. The resulting halftones overcome several traditional error-diffusion artifacts and achieve significantly greater accuracy in color rendition.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). However, algorithms that approach the DPC sum capacity do not appear viable in the forseeable future, which motivates lower complexity interference suppression techniques. Block diagonalization (BD) is a linear preceding technique for downlink multiuser MIMO systems. With perfect channel knowledge at the transmitter, BD can eliminate other users' interference at each receiver. In this paper, we study the sum capacity of BD with and without receive antenna selection. We analytically compare BD without receive antenna selection to DPC for a set of given channels. It is shown that (1) if the user channels are orthogonal to each other, then BD achieves the same sum capacity as DPC; (2) if the user channels lie in the same subspace, then the gain of DPC over BD can be upper bounded by the minimum of the number of transmit and receive antennas. These observations also hold for BD with receive antenna selection. Further, we study the ergodic sum capacity of BD with and without receive antenna selection in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is proposed for easy estimation of the gap between the sum capacity of DPC and BD without receive antenna selection.

This paper studies the codebook design problem for noncoherent multiple input multiple output (MIMO) communications. Each codeword in the codebook is considered to be a point in a Grassmann manifold. In this paper, the codebook design is formulated as an inverse eigenvalue problem. A new algorithm using reflection matrices is proposed to obtain the optimal codebook for noncoherent block fading channels where the channel state information (CSI) is unknown at both the receiver and transmitter. The key contribution of this paper is that our algorithm is able to construct an optimal codebook via a sequence of reflection matrices.

We investigate prediction algorithms that exploit both temporal and spatial correlations in MIMO correlated narrowband fading wireless channels. We first derive the optimal two dimensional minimum mean square error (2D-MMSE) prediction filter that maximally exploits the available temporal and spatial correlations. We then propose a lower complexity 2-step prediction algorithm, which first exploits temporal correlations using a classical single-input single-output (SISO) MMSE time- domain prediction filter for each entry in the MIMO channel, followed by an MMSE spatial smoothing step to exploit the spatial correlations. Compared to the SISO and specular prediction approaches, this approach either achieves lower MSE with a slight increase in complexity, or comparable MSE with lower complexity, in a wide range of wireless channel conditions. The same advantage holds for the average mutual information.

We propose a low-complexity adaptive high- resolution channel prediction algorithm for pilot symbol assisted orthogonal frequency division multiplexing (OFDM) systems. The algorithm is derived assuming a general time- and frequency- selective ray-based physical channel model, wherein each ray is parameterized by a complex amplitude, time-delay, and Doppler frequency. The algorithm is based on an improved rank and subspace adaptive estimation of signal parameters via rotational invariance techniques (ESPRIT). The adaptive ESPRIT is used to efficiently extract the slowly varying time-delays and Doppler frequencies of each ray, followed by a simple rotational update to compute the complex amplitudes. Our algorithm has a principal computational complexity that is linear in the number of pilot subcarriers used for prediction, in contrast to cubic complexity required for a non-adaptive block processing based algorithm. We compare our approach with a previously proposed adaptive OFDM channel prediction algorithm based on standard least mean square (LMS) and recursive least squares (RLS) adaptive filters, and show that our algorithm achieves lower mean square error at a comparable computational complexity. We provide simulation results based on the IEEE 802.16e standard.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with Dirty Paper Coding (DPC). Deploying DPC in real-time systems is, however, impractical. Block Diagonalization (BD) is an alternative precoding technique for downlink multiuser MIMO systems, which can eliminate inter-user interference at each receiver, at the expense of suboptimal sum capacity vs. DPC. In this paper, we study the sum capacity loss of BD for a fixed channel. We show that 1) if the user channels are orthogonal to each other, then BD achieves the complete sum capacity; and 2) if the user channels lie in a common row vector space, then the gain of DPC over BD can be bounded by the minimum of the number of transmit and receive antennas and the number of users. We also compare the ergodic sum capacity of DPC with that of BD in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is derived, which can be evaluated with a few numerical integrations. With this bound, we can easily estimate how far away BD is from being optimal in terms of ergodic sum capacity, which is useful in directing practical system designs.

A perceptual image hash function maps an image to a short binary string based on an image's appearance to the human eye. Perceptual image hashing is useful in image databases, watermarking, and authentication. In this paper, we decouple image hashing into feature extraction (intermediate hash) followed by data clustering (final hash). For any perceptually significant feature extractor, we propose a polynomial-time heuristic clustering algorithm that automatically determines the final hash length needed to satisfy a specified distortion. We prove that the decision version of our clustering problem is NP complete. Based on the proposed algorithm, we develop two variations to facilitate perceptual robustness versus fragility tradeoffs. We validate the perceptual significance of our hash by testing under Stirmark attacks. Finally, we develop randomized clustering algorithms for the purposes of secure image hashing.

We propose an image hashing paradigm using visually significant feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to introduce randomness, which, in turn, reduces vulnerability to adversarial attacks. The proposed hash algorithm withstands standard benchmark (e.g., Stirmark) attacks, including compression, geometric distortions of scaling and small-angle rotation, and common signal-processing operations. Content changing (malicious) manipulations of image data are also accurately detected. Detailed statistical analysis in the form of receiver operating characteristic (ROC) curves is presented and reveals the success of the proposed scheme in achieving perceptual robustness while avoiding misclassification.

The motivation of this paper is to find the class of channels that provides the largest capacity with both the transmit power constraint and channel Frobenius norm constraints. We study both the point-to-point case and the broadcast case. For point-to-point MIMO channels, the optimal channels must have T* equal singular values, where T* is dependent on the available transmit power and the channel Frobenius norm. This result agrees with the previous work by Chiurtu et al. For multiuser broadcast channels, we obtain an upper bound on the sum capacity. We also show that the bound is asymptotically tight for high SNR when all user channels have the same Frobenius norm constraint and N/sub t/ /spl ges/ KN/sub r/, where N/sub t/ and N/sub r/ are the numbers of transmit and receive antennas and K is the number of users.

Adaptive OFDM improves the system throughput by adjusting transmission parameters based on channel state information (CSI) estimated at or received by the transmitter. The improvement, however, is conditioned on the quality of the CSI, which can be compromised by estimation and quantization errors, and more significantly by delay. Channel prediction has been previously proposed to combat feedback delay. In this paper, a novel OFDM channel prediction algorithm that uses a 2 times 1-dimensional frequency estimation to determine the time-delays and Doppler frequencies of each propagation path is investigated. The algorithm assumes a general far-field scatterer, frequency-selective wireless channel model and hence is applicable to a wide variety of wireless channel conditions. It is shown that the method requires less feedback information and has better mean-squared error performance than previous methods. We provide simulation results for IEEE 802.16e channels

The process network (PN) model, which consists of concurrent processes communicating over first-in first out unidirectional queues, is useful for modeling and exploiting functional parallelism in streaming data applications. The PN model maps easily onto multi-processor and/or multi-threaded targets. Since the PN model is Turing complete, memory requirements cannot be predicted statically. In general, any bounded-memory scheduling algorithm for this model requires run-time deadlock detection. The few PN implementations that perform deadlock detection detect only global deadlocks. Not all local deadlocks, however, will cause a PN system to reach global deadlock. In this paper, we present the first local deadlock detection algorithm for PN models. The proposed algorithm is based on the Mitchell and Merritt algorithm and is suitable for both parallel and distributed PN implementations.

Surviving geometric attacks in image authentication is considered to be of great importance. This is because of the vulnerability of classical watermarking and digital signature based schemes to geometric image manipulations, particularly local geometric attacks. In this paper, we present a general framework for image content authentication using salient feature points. We first develop an iterative feature detector based on an explicit modeling of the human visual system. Then, we compare features from two images by developing a generalized Hausdorff distance measure. The use of such a distance measure is crucial to the robustness of the scheme, and accounts for feature detector failure or occlusion, which previously proposed methods do not address. The proposed algorithm withstands standard benchmark (e.g. Stirmark) attacks including compression, common signal processing operations, global as well as local geometric transformations, and even hard to model distortions such as print and scan. Content changing (malicious) manipulations of image data are also accurately detected

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones for printing and display. FM halftoning fixes the dot size (e.g., to one pixel in conventional error diffusion) and varies the dot frequency according to the intensity of the original grayscale image. We generalize error diffusion to produce FM halftones with user-controlled dot size and shape by using block quantization and block filtering. As a key application, we show how block-error diffusion may be applied to embed information in hardcopy using dot shape modulation. We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base images. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. We refer to the encoded printed version as an image barcode due to its high information capacity that differentiates it from common hardcopy watermarks. The encoding/halftoning strategy is based on a modified version of block-error diffusion. Encoder stability, image quality versus information capacity tradeoffs, and decoding issues with and without explicit knowledge of the base image are discussed.

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.

Discrete multitone is a popular discrete Fourier transform based implementation of multicarrier modulation for wireline communications. The paper examines the performance of a complex-tap filter bank structure for channel equalization in discrete multitone modulation systems. The structure under study passes the received signal through a number of logical paths. Each path takes care of one data carrying subcarrier. More specifically, each path cascades a finite impulse response frequency selective equalizer and a Goertzel filter computing a single point discrete Fourier transform. The delay on each logical path is individually optimized for best performance. In the presented simulations testing the achievable bit rate on an asymmetric digital subscriber line transceiver, the filter bank appears to benchmark the bit rate performance among existing discrete multitone equalization methods. An iterative training procedure, which depends on the second-order statistics of the input and output sequences, is proposed to show the achievability of performance.

We compare the capacities achieved by space-time water-filling and spatial water-filling for MIMO fading channels. Both the effects of fast fading and shadowing are considered. It is found that for Rayleigh fast fading MIMO channels, the spectral efficiency per antenna achieved by one-dimensional spatial water-filling is close to two-dimensional space-time water-filling. However, with log-normal shadowing, space-time water-filling achieves significantly higher capacity per antenna than spatial water-filling at low to moderate SNR regimes. Furthermore, space-time water-filling has lower computational complexity than spatial water-filling. It is also shown that space-time water-filling requires a priori knowledge of the channel gain distribution, and for Rayleigh channels with log-normal shadowing, the spectral efficiency advantage over spatial water-filling comes with an increased channel outage probability.

Internet traffic primarily consists of packets from elastic flows, i.e., Web transfers, file transfers (FTP), and e-mail, whose transfers are mediated via the transmission control protocol. We develop a conditional sampling technique to analyze throughput correlations among elastic flow classes based on flow level measurements from current network traffic monitoring tools. The primary contributions of the paper are: (1) a demonstration of throughput correlation among temporally overlapping flows on congested resources by using analytical/simulation models; (2) application of a multivariate statistical method (principal components) to infer network properties, such as the number of resources shared by flows in the network, from non-intrusive, flow level measurements collected at a single site. Our proposal for using flow level measurements to infer network properties differs significantly from previous network tomography research that has employed end-to-end packet level measurements for making inferences.

When taking pictures, professional photographers apply photographic composition rules, e.g. avoidance of mergers. A merger occurs when equally focused foreground and background regions appear to merge as one object. This paper presents an unsupervised algorithm that: (a) detects the main subject; (b) detects background objects merging with the main subject; and (c) reduces the visibility of merging background objects. Detection of the main subject requires automated adjustment of camera settings. The rest of the algorithm does not adjust or use the camera settings. The algorithm does not make assumptions about the scene setting (indoor/outdoor) or content. The algorithm is amenable to implementation on a fixed-point processor.

Many digital signal processing algorithms are first developed in floating point and later mapped into fixed point for digital hardware implementation. During this mapping, wordlengths are searched to minimize total hardware cost and maximize system performance. Complexity and distortion measures have been separately researched for optimum wordlength selection. This paper proposes a complexity-and-distortion measure (CDM) method that combines these two measures. The CDM method trades off these two measures using a weighting factor. The proposed method is applied to wordlength design of a fixed broadband wireless demodulator. For this case study, the proposed method finds the optimal solution in one-third the time that exhaustive search takes. The contributions of this paper are: (1) a generalization of search methods based on complexity or distortion measures; (2) a framework of automatic wordlength optimization; and (3) a wireless demodulator case study.

Conventional grayscale error diffusion halftoning produces worms and other objectionable artifacts. Tone dependent error diffusion (Li, P. and Allebach, J.P. Proc. SPIE Color Imaging, vol.4663, p.310-21, 2002) reduces these artifacts by controlling the diffusion of quantization errors based on the input graylevel. Li and Allebach designed error filter weights and thresholds for each (input) graylevel with optimization based on a human visual system (HVS) model. We extend tone dependent error diffusion to color. In color error diffusion, what color to render becomes a major concern in addition to finding optimal dot patterns. We present a visually optimum design approach for input level (tone) dependent error filters (for each color plane). The resulting halftones reduce traditional error diffusion artifacts and achieve greater accuracy in color rendition.

We develop methods to infer path or bottleneck sharing among TCP flow classes based on flow level measurements available from the current traffic monitoring tools. Our premise is that flows that temporally overlap on the congested resources have correlated throughputs. We propose to use factor analysis to explore the correlation structure of flow class throughputs in order to hypothesize which flow classes might share congested resources. The effectiveness of this "black box" approach is studied using the empirical data. We show that making such inferences based on flow level statistics is viable in practice, and can serve as an effective, novel tool for network design and configuration decisions. Our work on inferring bottleneck sharing differs significantly from the previous work in that we consider flow level instead of packet level statistics, and hence may potentially influence research in that area. Possible applications of this technique include network monitoring and root cause analysis of poor performance.

Perceptual image hashing maps an image to a fixed length binary string based on the image's appearance to the human eye, and has applications in image indexing, authentication, and watermarking. We present a general framework for perceptual image hashing using feature points. The feature points should be largely invariant under perceptually insignificant distortions. To satisfy this, we propose an iterative feature detector to extract significant geometry preserving feature points. We apply probabilistic quantization on the derived features to enhance perceptual robustness further. The proposed hash algorithm withstands standard benchmark (e.g. Stirmark) attacks including compression, geometric distortions of scaling and small angle rotation, and common signal processing operations. Content changing (malicious) manipulations of image data are also accurately detected.

During initialization, discrete multitone receivers train a time domain equalizer (TEQ) to shorten the channel impulse response to a preset length, /spl nu/+1. G. Arslan et al. report a minimum intersymbol interference (Min-ISI) method for TEQ design (see IEEE Trans. Sig. Process., vol.49, no.12, p.3123-35, 2001). Min-ISI TEQs give the highest bit rates among single-FIR TEQs amenable to real-time implementation on programmable fixed-point digital signal processors (DSPs). The Min-ISI method, however, has several disadvantages: (1) sensitivity to transmission delay; (2) inability to design TEQs longer than /spl nu/+1 taps; (3) sensitivity to the fixed-point computation in the Cholesky decomposition. We develop an alternate Min-ISI cost function, from which we derive: (1) a fast search method for the optimal transmission delay; (2) extensions to design arbitrary-length Min-ISI TEQs; (3) an iterative Min-ISI method. The iterative Min-ISI method avoids Cholesky decomposition, designs arbitrary length TEQs, and achieves the bit rate performance of the original Min-ISI method.

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular systems. A key issue in MU-OFDM is the allocation of the OFDM subcarriers and power among users sharing the channel. Previous allocation algorithms cannot ensure fairness in advance. In this paper, a proportional rate adaptive resource allocation method for MU-OFDM is proposed. Subcarrier and power allocation are carried out sequentially to reduce the complexity, and an optimal power allocation procedure is derived, through which proportional fairness is achieved. Simulation results show that this low-complexity MU-OFDM system achieves double the capacity of a fixed time division approach to OFDM multiple access, and also has higher capacity than previously derived suboptimal power distribution schemes.

Time-domain equalization is crucial in reducing intercarrier and intersymbol interference in multicarrier systems. A channel shortening time-domain equalizer (TEQ), which is a finite impulse response (FIR) filter, placed in cascade with the channel, produces an effective impulse response that is shorter than the channel impulse response. We show that finite length minimum mean squared error (MMSE) and maximum shortening SNR (MSSNR) TEQs are approximately symmetric, and infinite length MSSNR TEQs with a unit norm TEQ (UNT) constraint are exactly symmetric. A symmetric TEQ halves FIR implementation complexity, enables the frequency-domain equalizer and TEQ to be trained in parallel, and exhibits only a small loss in bit rate over nonsymmetric TEQs. In addition, a symmetric MSSNR-UNT TEQ reduces training computational complexity by a factor of 4 and doubles the length of the TEQ that can be designed.

Image halftoning converts a high-resolution image to a low-resolution image, e.g., a 24-bit color image to a three-bit color image, for printing and display. Vector error diffusion captures correlation among color planes by using an error filter with matrix-valued coefficients. In optimizing vector error filters, Damera-Venkata and Evans (see IEEE Trans. Image Processing, vol.10, p.1552-65, Oct. 2001) transform the error image into an opponent color space where Euclidean distance has perceptual meaning. This letter evaluates color spaces for vector error filter optimization. In order of increasing quality, the color spaces are YIQ, YUV, opponent (by Poirson and Wandell, 1993), and linearized CIELab (by Flohr, Kolpatzik, Balasubramanian, Carrara, Bouman, and Allebach, 1993).

This paper presents an efficient method for updating echo canceller (EC) coefficients in asymmetric digital subscriber line transceivers during transmission that removes the dependency of updates on the far-end signal. The new method improves on the frequency-domain EC architecture by Ho et al. (1996), in that it converges faster, improves the stability to perturbation, and is not prone to bias from pilot tones. The new EC updating system architecture is presented for both the central office and remote terminal systems and is fully compliant with the ITU-T G.DMT standard.

Discrete Multitone (DMT) modulation is the industry standard for Asymmetrical Digital Subscriber Loop (ADSL) modems. DMT modulation allows for simplified equalization when the effective duration of the channel memory is shorter than the predefined length of the cyclic prefix (CP). A single time-domain equalizer (TEQ) is typically used to truncate the channel memory such that the data rate loss due to the use of the CP is reduced. Because the single TEQ is optimized over all sub channels, it is difficult to find an ideal solution in which the number of bits supported on each sub channel is optimized. This paper proposes a dual-path TEQ, which adds a second TEQ that is biased to selected sub channels to increase transmission throughput. The implementation complexity is lower than other band-partition equalization methods such as per tone equalization.

Video streaming has already been very popular on the Internet through services such as news bulletins from different parts of the world and on-demand music-video clips. With rapidly developing wireless technologies, video streaming to mobile devices will be very common. We investigate the self-similar scaling behavior that is present in variable bit rate (VBR) MPEG-4 video. As the usage of video services over packet-based wireless networks increases, new workload models will be necessary to study the quality of service aspects of video traffic. A key finding of our study is that MPEG-4 video encoder output traffic has fractal behavior and this behavior exists regardless of the compression ratio.

One interesting feature of the new JPEG2000 image coding standard is support of region of interest (ROI) coding using the maximum shift (Maxshift) method, which allows for arbitrarily shaped ROI image compression without shape coding or explicitly transmitting any shape information to the decoder. The major disadvantage of the Maxshift method is that it cannot adjust the scaling value which determines the degree of relative importance between the ROI and the background wavelet coefficients. The bitplane-by-bitplane shift (BbBShift) method was introduced to support both arbitrary ROI shape and arbitrary scaling without shape coding. We propose a generalized BbBShift (GBbBShift) method, which delivers much more flexibility than both Maxshift and BbBShift for "degree-of-interest" adjustment of the ROI with insignificant effect on coding efficiency and computational complexity. Experiments show that it can provide significantly better visual quality than Maxshift at low bit rates. GBbBShift is not compliant with the current JPEG2000 definitions. In order to use it, a new ROI coding mode would need to be added to the standard.

Video coding techniques employ characteristics of the human visual system (HVS) to achieve high coding efficiency. Lee (2000) and Bovik have exploited foveation, which is a non-uniform resolution representation of an image reflecting the sampling in the retina, for low bit-rate video coding. We develop a fast approximation of the foveation model and demonstrate real-time foveation techniques in the spatial domain and discrete cosine transform (DCT) domain. We incorporate fast DCT domain foveation into the baseline H.263 video encoding standard. We show that DCT-domain foveation requires much lower computational overhead but generates higher bit rates than spatial domain foveation. Our techniques do not require any modifications of the decoder.

We optimize the noise shaping behavior of color error diffusion by designing an optimized error filter based on a proposed noise shaping model for color error diffusion and a generalized linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm.

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones. In FM halftoning the dot size and shape is fixed (equal to one pixel) and the dot frequency is varied in accordance to the graylevel values of the underlying grayscale image. We generalize error diffusion to produce FM halftones with user controlled dot size and shape using block quantization and a block filter in the feedback loop. We call this modified quantization and feedback process block error diffusion. The block filters are designed from well known scalar error filter prototypes and retain their properties. Further, we show that choosing a structured block filter results in an efficient parallel implementation of block error diffusion.

In the early 1990s, the state-of-the-art in commercial chromosome image acquisition was grayscale. Automated chromosome classification was based on the grayscale image and boundary information obtained during segmentation. Multi-spectral image acquisition was developed in 1990 and commercialized in the mid-1990s. One acquisition method, multiplex fluorescence in-situ hybridization (M-FISH), uses five color dyes. We propose a segmentation algorithm for M-FISH images that minimizes the entropy of classified pixels within possible chromosomes. This method is shown to correctly decompose even difficult clusters of touching and overlapping chromosomes. Finally, an example image is given to illustrate the algorithm.

Interferometric synthetic aperture radar (INSAR) and laser altimeter (LIDAR) systems are both widely used for mapping topography. INSAR can map extended areas but accuracies are limited over vegetated regions, primarily because the observations are not measurements of true surface topography. The measurements correspond to a height above the true surface that depends on both the sensor and the vegetation. Conversely, topography from LIDAR is very accurate, but coverage is limited to smaller regions. The authors demonstrate how these technologies can be used synergistically. First, the authors determine surface elevations and vegetation heights from dual-baseline INSAR data by inverting an INSAR scattering model. The authors then combine sparse LIDAR observations with the INSAR inversion results to improve the estimates of ground elevations and vegetation heights. This is accomplished via a multiresolution Kalman Filter that provides both the estimates and a measure of their uncertainty at each location. Combining data from the two sensors provides estimates that are more accurate than those obtained from INSAR alone yet have dense, extensive coverage, which is difficult to obtain with LIDAR. Contributions of this work include (1) combining physical modeling with multiscale estimation to accommodate nonlinear measurement-state relationships and (2) improving estimates of ground elevations and vegetation heights for remote sensing applications.

Grayscale digital image halftoning quantizes each pixel to one bit. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to the input in order to diffuse the quantization error among the neighboring grayscale pixels. Error diffusion introduces nonlinear distortion (directional artifacts), linear distortion (sharpening), and additive noise. Threshold modulation, which alters the quantizer input, has been previously used to reduce either directional artifacts or linear distortion. This paper presents an adaptive threshold modulation framework to improve halftone quality by optimizing error diffusion parameters in the least squares sense. The framework models the quantizer implicitly, so a wide variety of quantizers may be used. Based on the framework, we derive adaptive algorithms to optimize 1) edge enhancement halftoning and 2) green noise halftoning. In edge enhancement halftoning, we minimize linear distortion by controlling the sharpening control parameter. We may also break up directional artifacts by replacing the thresholding quantizer with a deterministic bit flipping (DBF) quantizer. For green noise halftoning, we optimize the hysteresis coefficients.

Traditional error diffusion halftoning is a high quality method for producing binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error diffusion may be extended to color images by using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we propose three contributions. First, we analyze vector color error diffusion based on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts the key characteristics of color error diffusion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos (1987) and by Kite et al. (1997) as special cases. Second, based on our model, we optimize the noise shaping behavior of color error diffusion by designing error filters that are optimum with respect to any given linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filter has a parallel implementation as a polyphase filterbank.

We propose an optimum channel shortening method for discrete multitone (DMT) transceivers. The proposed method shortens a given channel to a desired length while maximizing the number of bits transmitted on a DMT symbol. The key to the optimum solution is the definition of the SNR in a subchannel using the equivalent signal, noise, and ISI paths in the system. Our simulation results show that the proposed method outperforms the best existing method with a 18% increase in the bit rate. We show that the maximum shortening SNR method is a special case of the proposed method and both methods are nearly equivalent when the input energy distribution is constant over all subchannels.

A multifilter is a filter with matrix-valued coefficients, and is used in the processing of vector-valued signals, e.g. color images. Convolution becomes a vector sum of matrix-vector multiplication. In this paper, we efficiently implement a multifilter as a parallel combination of scalar filters. Each scalar filter works on one component of the input vector signal, which increases processing speed by the dimension of the vector-valued signal. This means that by using N processors, the throughput is increased by a factor of N while the total memory usage remains unchanged. We also present a frequency-domain analysis of the filtering.

In asynchronous CDMA systems, transmission over frequency-selective channels subjects the signal to interchip interference and intersymbol interference, both of which cause multiple access interference (MAI). MAI cannot be easily eliminated without the knowledge of the channel parameters. Both blind adaptive and subspace based multiuser receivers have been proposed to eliminate the interference. We propose a self-recovering RAKE receiver that does not require training sequences or knowledge of interfering user signature waveforms. The contribution of this paper is the combination of constant modulus algorithm and previously derived constrains. The constrains depend on the multipath structure of the desired signal. The proposed receiver converges faster than the blind adaptive multiuser receivers. Its fast convergence and low complexity make it well suited for rapidly time-varying mobile radio frequency selective fading channels. By simulation, we compare the proposed method with blind adaptive constrained LMS RAKE receiver and minimum output energy receiver.

This paper aims to provide a quantitative understanding of the performance of DSP and multimedia applications on very long instruction word (VLIW), single instruction multiple data (SIMD), and superscalar processors. We evaluate the performance of the VLIW paradigm using Texas Instruments Inc.'s TMS320C62xx processor and the SIMD paradigm using Intel's Pentium II processor (with MMX) on a set of DSP and media benchmarks. Tradeoffs in superscalar performance are evaluated with a combination of measurements on Pentium II and simulation experiments on the SimpleScalar simulator. Our benchmark suite includes kernels (filtering, autocorrelation, and dot product) and applications (audio effects, G.711 speech coding, and speech compression). Optimized assembly libraries and compiler intrinsics were used to create the SIMD and VLIW code. We used the hardware performance counters on the Pentium II and the stand-alone simulator for the C62xx to obtain the execution cycle counts. In comparison to non-SIMD Pentium II performance, the SIMD version exhibits a speedup ranging from 1.0 to 5.5 while the speedup of the VLIW version ranges from 0.63 to 9.0. The benchmarks are seen to contain large amounts of available parallelism, however, most of it is inter-iteration parallelism. Out-of-order execution and branch prediction are observed to be extremely important to exploit such parallelism in media applications.

The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. We propose a new approach that can blindly measure blocking artifacts in images without reference to the originals. The key idea is to model the blocky image as a non-blocky image interfered with a pure blocky signal. The task of the blocking effect measurement algorithm is then to detect and evaluate the power of the blocky signal. The proposed approach has the flexibility to integrate human visual system features such as the luminance and the texture masking effects.

This paper presents an extensible framework for the simultaneous constrained optimization of multiple properties of digital IIR filters. The framework optimizes the pole-zero locations for behavioral properties of magnitude and phase response, and the implementation property of quality factors, subject to constraints on the same properties. We formulate the constrained nonlinear optimization problem as a sequential quadratic programming (SQP) problem. SQP solvers are robust when providing formulas for the gradients of the objective function and constraints. We program Mathematica to compute the gradient formulas and convert the formulas into Matlab programs to perform the optimization, The automated approach eliminates errors in manipulating the algebraic equations and transcribing equations into software. The key contributions are (1) an automated, extensible, multicriteria digital IIR filter optimization framework, and (2) a novel filter design. We have released the source code on the Internet.

We model a degraded image as an original image that has been subject to linear frequency distortion and additive noise injection. Since the psychovisual effects of frequency distortion and noise injection are independent, we decouple these two sources of degradation and measure their effect on the human visual system. We develop a distortion measure (DM) of the effect of frequency distortion, and a noise quality measure (NQM) of the effect of additive noise. The NQM, which is based on Peli's (1990) contrast pyramid, takes into account the following: 1) variation in contrast sensitivity with distance, image dimensions, and spatial frequency; 2) variation in the local luminance mean; 3) contrast interaction between spatial frequencies; 4) contrast masking effects. For additive noise, we demonstrate that the nonlinear NQM is a better measure of visual quality than peak signal-to noise ratio (PSNR) and linear quality measures. We compute the DM in three steps. First, we find the frequency distortion in the degraded image. Second, we compute the deviation of this frequency distortion from an allpass response of unity gain (no distortion). Finally, we weight the deviation by a model of the frequency response of the human visual system and integrate over the visible frequencies. We demonstrate how to decouple distortion and additive noise degradation in a practical image restoration system.

Halftones and other binary images are difficult to process with causing several degradation. Degradation is greatly reduced if the halftone is inverse halftoned (converted to grayscale) before scaling, sharpening, rotating, or other processing. For error diffused halftones, we present (1) a fast inverse halftoning algorithm and (2) a new multiscale gradient estimator. The inverse halftoning algorithm is based on anisotropic diffusion. It uses the new multiscale gradient estimator to vary the tradeoff between spatial resolution and grayscale resolution at each pixel to obtain a sharp image with a low perceived noise level. Because the algorithm requires fewer than 300 arithmetic operations per pixel and processes 7x7 neighborhoods of halftone pixels, it is well suited for implementation in VLSI and embedded software. We compare the implementation cost, peak signal to noise ratio, and visual quality with other inverse halftoning algorithms.

Digital halftoning quantizes a graylevel image to one bit per pixel. Halftoning by error diffusion reduces local quantization error by filtering the quantization error in a feedback loop. In this paper, we linearize error diffusion algorithms by modeling the quantizer as a linear gain plus additive noise. We confirm the accuracy of the linear model in three independent ways. Using the linear model, we quantify the two primary effects of error diffusion: edge sharpening and noise shaping. For each effect, we develop an objective measure of its impact on the subjective quality of the halftone. Edge sharpening is proportional to the linear gain, and we give a formula to estimate the gain from a given error filter. In quantifying the noise, we modify the input image to compensate for the sharpening distortion and apply a perceptually weighted signal-to-noise ratio to the residual of the halftone and modified input image. We compute the correlation between the residual and the original image to show when the residual can be considered signal independent. We also compute a tonality measure similar to total harmonic distortion. We use the proposed measures for edge sharpening, noise shaping, and tonality to evaluate the quality of error diffusion algorithms.

We present a generalized optimal minimum phase digital FIR filter design algorithm that supports (1) arbitrary magnitude response specifications, (2) high coefficient accuracy, and (3) real and complex filters. The algorithm uses the discrete Hilbert transform relationship between the magnitude spectrum of a causal real sequence and its minimum phase delay phase spectrum given by Cizek (1970). We extend the transform pair to the complex case and show that the algorithm gives arbitrary coefficient accuracy. We present design examples that exceed the coefficient accuracy of the optimal real minimum phase filters reported by Chen and Parks (1986) and reduce the length of the optimal complex linear phase filters designed by Karam and McClellan (1995).

Synthetic aperture radar (SAR) systems are mounted on airplanes and satellites, which have limited downlink and storage capacity, yet SAR image sequences may be produced at rates of several Gbps. Compression is difficult because SAR images contain significant high-frequency information, such as terrain boundaries and terrain texture. In assessing the quality of compressed images, peak signal-to-noise ratio and mean-squared error are inadequate because they assume that distortion is solely due to image-independent additive noise. In this paper, we provide objective measures to assess the visual quality of SAR images compressed by JPEG and SPIHT coders. The human visual system responds differently to linear distortion and noise injection (nonlinear distortion plus additive noise). Our key contributions are that we (1) decouple and quantify the linear distortion and noise injection in JPEG and SPIHT coders, and (2) introduce a new edge correlation quality measure which we use to quantify nonlinear distortion.

We present raster image processing algorithms for rehalftoning error diffused halftones and producing interpolated error diffused halftones. Rehalftoning converts a halftone created by one method into one created by another method. In interpolated halftoning, interpolation increases the image size before halftoning, e.g. for printing. Both rehalftoning and interpolated halftoning introduce blur and noise in the output image. To compensate for the blur, we use modified error diffusion, which has a variable gain parameter to control the sharpness. We derive optimal formulas for the sharpness control parameter to make the overall frequency response flat. The high-frequency noise is masked by error diffusion. The proposed algorithms yield halftones of high fidelity at a low computational cost.

Optical Doppler Tomography (ODT) is a noninvasive 3-D optical interferometric imaging technique that measures static and dynamic structures in a sample. To obtain the dynamic structure, e.g. blood flowing in tissue, a velocity estimation algorithm detects the Doppler shift in the received interference fringe data with respect to the carrier frequency. Previous velocity estimation algorithms use conventional Fourier magnitude techniques that do not provide sufficient frequency resolution in fast ODT systems because of the high data acquisition rates and hence short time series. In this paper, we propose a nonlinear algorithm that uses the phase shift between two successive scans of interference fringe data to give a high-resolution estimate of the Doppler shift. The algorithm detects Doppler shifts of 0.1 to 3 kHz with respect to a 1 MHz carrier. In processing 5 frames/s with 100×100 pixels/frame and 32 samples/pixel, i.e. 1.6 million samples/s, the algorithm requires 26 million multiply-accumulates/s. The algorithm works well at 4 bits/sample. The low complexity and small input data size are well-suited for real-time implementation in software. We provide a mathematical analysis of the Doppler shift resolution by modeling the interference fringe data as an AM-FM signal.

The JBIG2 standard supports lossless and lossy coding models for text, halftone, and generic regions in bi-level images. For the JBIG2 lossy halftone compression mode, halftones are descreened before encoding. Previous JBIG2 descreening implementations produce high-quality images for clustered dot halftones at high compression rates but significantly degrade the image quality for stochastic halftones, even at much lower rates. In this paper, we develop (1) a flexible, computationally efficient, JBIG2-compliant method for compressing stochastic halftones that reduces noise, artifacts, and blurring; (2) quality measures for linear and nonlinear distortion in compressed halftones; and (3) rate-distortion tradeoffs for the encoder parameters.

We present a fast, non-iterative technique for producing grayscale images from error diffused and dithered halftones. The first stage of the algorithm consists of a Gaussian filter and a median filter, while the second stage consists of a bandpass filter, a thresholding operation, and a median filter. The second stage enhances the rendering of edges in the inverse halftone. We compare our algorithm to the best reported statistical smoothing, wavelet, and Bayesian algorithms to show that it delivers comparable PSNR and subjective quality at a fraction of the computation and memory requirements. For error diffused halftones, our technique is seven times faster than the MAP estimation method and 75 times faster than the wavelet method. For dithered halftones, our technique is 200 times faster than the MAP estimation method. A C implementation of the algorithm is available.

We present an inverse halftoning algorithm for error diffused halftones. At each pixel, the algorithm applies a separable 7/spl times/7 FIR filter parameterized by the computed local horizontal and vertical gradients. All operations are entirely local; only 7 rows of image storage and fewer than 300 operations per pixel are required. The algorithm can be easily implemented in embedded software or hardware. We compare our algorithm with previously reported approaches, and show that it delivers comparable PSNR and subjective quality at a fraction of the computation and memory requirements. A C implementation of the algorithm is available.

Many current general purpose processors are using extensions to the instruction set architecture to enhance the performance of digital signal processing (DSP) and multimedia applications. In this paper, we evaluate the X86 architecture's multimedia extension (MMX) instruction set on a set of benchmarks. Our benchmark suite includes kernels (filtering, fast Fourier transforms, and vector arithmetic) and applications (JPEG compression, Doppler radar processing, imaging, and G.722 speech encoding). Each benchmark has at least one non-MMX version in C and an MMX version that makes calls to an MMX assembly library. The versions differ in the implementation of filtering, vector arithmetic, and other relevant kernels. The observed speed up for the MMX versions of the suite ranges from less than 1.0 to 6.1. In addition to quantifying the speedup, we perform detailed instruction level profiling using Intel's VTune profiling tool. Using VTune, we profile static and dynamic instructions, microarchitecture operations, and data references to isolate the specific reasons for speedup or lack thereof. This analysis allows one to understand which aspects of native signal processing instruction sets are most useful, the current limitations, and how they can be utilized most efficiently.

The rapidly emerging and increasing complexity of video coding standards require a new design paradigm. This paper describes a modular, scalable, extensible simulation and design methodology for system-level design of video codecs. Video codec dataflow is modeled by synchronous dataflow, and implemented in a heterogeneous CAD framework. As a result, generic video codec is decomposed into basic modules. Each module is easy to interface and extend. Any video codec standard (e.g., H.263+ or MPEG-4) can be mapped on that basis and be retargeted for various architectures including DSPs and ASIPs. We develop module libraries for video codecs which can be dynamically linked to an extensible framework for simulation and algorithm development. Some parts of the basic modules can be mixed with other domain modules which may have different interaction semantics especially for hardware design and retargeting purposes. We based our framework on the Ptolemy software environment.

The International Telecommunication Union (ITU) recommendations for dual-tone multifrequency (DTMF) signaling are not met by conventional DTMF detectors. We present an efficient DTMF detection algorithm based on the nonuniform discrete Fourier transform that meets all of the ITU recommendations. The key innovations are the use of two sliding windows and development of sophisticated timing tests. Our algorithm requires no buffering of input samples. To perform DTMF detection on n telephone channels, our algorithm requires approximately n MIPS on a digital signal processor (DSP), 75+30n words of data memory, and 1000 words of program memory. Using the new algorithm, a single fixed-point DSP can perform ITU-compliant DTMF detection on the 24 telephone channels of a T1 time-division multiple multiplexed telecommunications line.

In a multi-transmitter broadcast system, the weight vector for each message signal can provide an additional degree of freedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. The design of optimal weight vectors that maximize the overall channel capacity is an open problem. Under certain power constraints, the channel capacity R is a highly nonlinear function of the M-dimensional weight vectors {w/sub i/}, where M is the number of transmitters. Hence, a closed-form algebraic solution that maximizes R over {w/sub i/} does not seem to be tractable. We decouple the weight vectors in R to simplify the optimization problem to a search for the maxima of a smooth multidimensional function. Based on this decoupling, we derive and evaluate two algorithms for computing weight vectors for the two-user and three-user cases: orthogonal and optimal. We also propose a near-optimum algorithm for the two-user case. The optimal algorithm requires an iterative search.

The error diffusion algorithm for digital halftoning is equivalent in form to a noise-shaping feedback coder, a class of delta-sigma modulator. The white noise assumption of the quantizer error is known to be false; in fact, the quantizer error is seen to be highly correlated with the input image. To account for this correlation, we use a gain model for the quantizer. This model accurately predicts the edge sharpening and noise shaping caused by all error diffusion schemes. It also permits an extension of error diffusion to oversampled imagery.

We define and construct a new family of compactly supported, nonseparable two-dimensional wavelets, "biorthogonal quincunx Coifman wavelets" (BQCWs), from their one-dimensional counterparts using the McClellan transformation. The resulting filter banks possess many interesting properties such as perfect reconstruction, vanishing moments, symmetry, diamond-shaped passbands, and dyadic fractional filter coefficients. We derive explicit formulas for the frequency responses of these filter banks. Both the analysis and synthesis lowpass filters converge to an ideal diamond-shaped halfband lowpass filter as the order of the corresponding BQCW system tends to infinity. Hence, they are promising in image and multidimensional signal processing applications. In addition, the synthesis scaling function in a BQCW system of any order is interpolating (or cardinal), which has been known as a desired merit in numerical analysis.

Two extensions of the McClellan transformation have been proposed to transform even-length symmetric one-dimensional (1-D) prototype filters into even/odd-length multidimensional filters. We show that, unlike the original McClellan transformation, the two extended McClellan transformations suffer from a limitation in the design of multidimensional two-channel finite impulse response (FIR) perfect reconstruction (PR) filterbanks and wavelets in the sense that there does not exist any transformation function that can preserve PR after transformation, However, for one of the transformations, we point out that near-PR filterbanks are possible.

In multiple dimensions, the cascade of an upsampler by L and a downsampler by M commutes if and only if the integer matrices L and M are right coprime and LM=ML. This letter presents algorithms to design L and M that yield commutative upsampler/dowsampler cascades. We prove that commutativity is possible if the Jordan canonical form of the rational (resampling) matrix R=LM/sup -1/ is equivalent to the Smith-McMillan form of R. A necessary condition for this equivalence is that R has an eigendecomposition and the eigenvalues are rational.

We are developing a sophomore course to serve as a first course in electrical engineering. The course focuses on discrete-time systems. Its goal is to give students an intuitive understanding of concepts such as sinusoids, frequency domain, sampling, aliasing, and quantization. In the laboratory, students build simulations and real-time systems to test these ideas. By using a combination of high-level and DSP assembly languages, the students experiment with a variety of views into the representation, design, and implementation of systems. The students are exposed to a digital style of implementation based on programming both desktop and embedded processors.

No abstract available.

A typical path in learning digital signal processing begins at the theoretical end and progresses toward the practical constraints imposed by implementation in hardware or software. On this path, the student would learn how to convert mathematical theory into algorithms and then algorithms into efficient implementations. In this paper, we first summarize the electronic courseware we have already developed in Mathematica, MATLAB, and Ptolemy to teach DSP theory, algorithms, and implementation, respectively. Then, we discuss ways to integrate our efforts to help students discover the connections between these topics.

The implementation of basic signal analysis and transforms for DSP (digital signal processing) with a symbolic mathematical program called Mathematica is described. By extending its capabilities through a proper representation of knowledge of discrete signals, operators, and transforms, Mathematica can take the forward and inverse z-transforms symbolically and apply the discrete Fourier transform (DFT) as a symbolic summation. In addition to the 150+ common mathematical operators and functions built into Mathematica, the rule-based package defines 23 systems and 8 basic signals. Six of these systems provide the ability to take forward and inverse z-transforms, DFTs, and DTFTs of multidimensional signal processing expressions. More importantly, this package allows the user to manipulate signals (functions or data) and systems (operators) as algebraic expressions. The user can convert symbolic signal processing expressions to graphical, numeric, and textual representations.>