nils-herrmann/clean_scirepeval huggingface dataset:
ADC Bit Optimization for Spectrum- and Energy-Efficient Millimeter Wave Communications

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.
Zero-copy Queues for Native Signal Processing Using the Virtual Memory System

The high performance of current general-purpose processors makes it feasible to perform digital signal processing on the main CPU of a workstation. As the performance gap between CPU speed and memory speed continues to increase, it becomes obvious that DSP applications on workstations must address memory performance. We present a technique for using the virtual memory system to implement zero-copy queues. We present benchmark results for a frequency-domain FIR filter using this technique, compared to manual data circularity management. The technique presented can significantly reduce the overhead of data management when processing continuous, overlapping streams of data.
WLC43-5: Low-Complexity Adaptive High-Resolution Channel Prediction for OFDM Systems

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.
Real-time sonar beamforming on a Unix workstation using process networks and POSIX threads

Traditionally, expensive custom hardware has been required to implement data-intensive sonar beamforming algorithms in real-time. We develop a sonar beamformer in software by merging the following technologies: (1) symmetric multiprocessing on Unix workstations, (2) lightweight POSIX threads, and (3) the process network model of computation. We find that it is feasible for a 4-GFLOP digital interpolation process network beamformer to run in real-time on a Sun workstation with 16 UltraSPARC-II processors running at 336 MHz. The workstation beamformer significantly reduces cost and development time over an equivalent hardware beamformer.
Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

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.
Flicker assessment of low-to-medium frame-rate binary video halftones

Video display on devices with limited bit-depth capabilities requires conversion of the original (continuous-tone) video having a higher bit-depth to one having a lower bit-depth. Video halftoning refers to a process that attempts to perform this conversion such that the resulting halftone video is perceptually similar to the continuous-tone video. This quantization process, however, results in artifacts. A video halftoning algorithm can be assessed by quantifying the artifacts introduced by it. In this paper, we discuss an approach to assess flicker, a typical temporal artifact, in low-to-medium frame-rate binary video halftones produced from grayscale continuous-tone videos.
A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments

We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than thermal noise. We first show that the bit-error-rate optimal receiver jointly estimates the propagation channel coefficients, the noise impulses, the finite-alphabet symbols, and the unknown bits. We then propose a near-optimal yet computationally tractable approach to this joint estimation problem using loopy belief propagation. In particular, we merge the recently proposed “generalized approximate message passing” (GAMP) algorithm with the forward-backward algorithm and soft-input soft-output decoding using a “turbo” approach. Numerical results indicate that the proposed receiver drastically outperforms existing receivers under impulsive noise and comes within 1 dB of the matched-filter bound. Meanwhile, with N tones, the proposed factor-graph-based receiver has only O(NlogN) complexity, and it can be parallelized.
Symbolic z-transforms using DSP knowledge bases

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.<<ETX>>
Heterogeneous multiprocessor mapping for real-time streaming systems

Real-time streaming signal processing systems typically desire 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 10−6 gap from the ILP optimal solution, with up to 12x better run-time efficiency.
Entropy estimation for segmentation of multi-spectral chromosome images

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 previously introduced a segmentation algorithm for M-FISH images that minimizes the entropy of classified pixels within possible chromosomes. In this paper, we extend this entropy-minimization algorithm to work on raw image data, which removes the requirement for pixel classification. This method works by estimating entropy front raw image data rather than calculating entropy from classified pixels. A successful example image is given to illustrate the algorithm. Finally, it is determined that entropy estimation for minimum entropy segmentation adds a heavy computational burden without contributing any significant increase in classification performance, and thus not worth the effort.
Tone dependent color error diffusion

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.
3D rotational video stabilization using manifold optimization

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.
Halftoning-Inspired Methods for Foveation in Variable-Acuity Superpixel Imager Cameras

We compare approaches for generating binary control signals for variable acuity superpixel imager (VASI™) cameras. We foveate a set of images using control signals generated by various halftoning approaches and then measure their performance via figures of merit (FOMs). We find that two novel approaches provide superior FOM values but inferior bandwidth control, making them unsuitable for use with VASI™ cameras. Floyd-Steinberg error diffusion gives the best combination of FOM values and bandwidth control. Our contributions include a comparison of approaches, a lookup table method to improve bandwidth control, and two novel methods for binarizing VASI™ control signals.  Our objective is to compare how different halftoning- inspired approaches perform at generating the binary control signals used by Variable Acuity Superpixel Imager (VASI™) foveating cameras, in the context of an automatic target acquisition and recognition (ATA/ATR) application. To accomplish this, we measure five objective figures of merit (FOMs) after foveating a small set of test images with a variety of halftoning-inspired approaches. VASI™ cameras have a number of characteristics that are attractive for the ATR application, but to use them the user must specify a binary control signal that defines the camera's pixel-by-pixel behavior. The translation from a continuous- valued desired resolution signal to a binary VASI™ control signal can be based on halftoning. It must be efficient to avoid lowering the camera's effective frame rate. It must also accurately achieve the target resolution - or equivalently a target bandwidth reduction, which we express as percentage of original bandwidth (PBW) - because this drives the frame rate achieved by the VASI™ camera.
Network tomography based on flow level measurements

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.
Lossy compression of stochastic halftones with JBIG2

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.
Cyclostationary noise modeling in narrowband powerline communication for Smart Grid applications

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.
Short range wireless channel prediction using local information

Wireless channels change due to the mobility of users, which coupled with system delays, causes outdated channel state information (CSI) to be used for transmitter optimization techniques such as adaptive modulation. Channel prediction allows the system to adapt modulation methods to an estimated future CSI. The primary contribution of this paper is a low complexity channel prediction method using polynomial approximation. The method is local in the sense that only a few previous channel samples are required to estimate the next CSI. The computational complexity of the proposed method is demonstrated to be negligible compared to previous methods. Simulation results show that the proposed method accurately tracks slowly to moderately fading channels. The proposed method's usefulness is demonstrated by applying it to a multiuser OFDM system. As an example, a multiuser OFDM with a system delay of 5 ms and a Doppler spread of 40 Hz loses about 17% of its capacity due to imperfect CSI. Using the proposed algorithm to predict the CSI, the capacity loss is less than 1%.
Two-dimensional phase unwrapping using neural networks

Imaging systems that construct an image from phase information in received signals include synthetic aperture radar (SAR) and optical Doppler tomography (ODT) systems. A fundamental problem in the image formation is phase ambiguity, i.e., it is impossible to distinguish between phases that differ by 2/spl pi/. Phase unwrapping in two dimensions essentially consists of detecting the pixel locations of the phase discontinuities, finding an ordering among the pixel locations for unwrapping the phase, and adding offsets of multiples of 2/spl pi/. In this paper, we propose a new method for detecting phase discontinuities. The method is based on a supervised feedforward multilayer perceptron neural network. We train and test the neural network on simulated phase images formed in an ODT system. For the ODT phase images, the new method detects the correct unwrapping locations where some conventional methods fail. The key contribution of the paper is a one-pass pixel-parallel low-complexity method for detecting phase discontinuities.
Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

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.
Modeling and quality assessment of halftoning by error diffusion

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.
Fast estimation of weight vectors to optimize multi-transmitter broadcast channel capacity

We improve capacity of broadcast channels by employing multiple transmitters and exploiting the spatial diversity among the users. We derive fast algorithms to compute orthogonal, near-optimal, and optimal weight vectors for broadcasting message signals to two and three users. The key innovation is that we decouple the weight vectors in the measure of channel capacity to (1) simplify the optimization problem to a search for the maxima of a smooth multidimensional function and (2) derive closed-form expressions for the orthogonal and near-optimal algorithms.
Low-Power Multipliers with Data Wordlength Reduction

Multiprecision multipliers reduce power consump- tion by selecting smaller multipliers (i.e., submultiplier) according to the wordsize of the input operands. However, arbitrary levels of bit precision are not achieved by multiprecision multipliers. Two proposed wordlength reduction techniques that reduce power consumption with arbitrary levels of bit precision are considered. Expectation values of bit switching activity for reduction in the signed right shift method and the truncation method are derived. The signed right shift method and the truncation method are applied to a 16-bit radix-4 modified Booth multiplier and a 16-bit Wallace multiplier. The truncation method with 8-bit operands reduces the power consumption by 56% in the Wallace multiplier and 31% in the Booth multiplier. The signed right shift method shows no power reduction in the Wallace multiplier and 25% power reduction in the Booth multiplier. Unequal levels of precision in operands show different power reduction value for the Booth multiplier. The non-recoded operand in the Booth multiplier with 8-bit reduction has 13% more sensitivity in power consumption than the recoded multiplicand.