nils-herrmann/clean_scirepeval huggingface dataset:
Access Point Selection for Multi-Rate IEEE 802.11 Wireless LANs

Access Point (AP) selection is important in WLANs as it affects the throughput of the joining station (STA). In this paper, we propose a class of AP selection algorithms to maximize the joining STA's expected throughput by considering interference at STAs, and transmit opportunities (TXOPs) at APs. Specifically, we collect a binary-valued local channel occupancy signal, called busy-idle (BI) signal, at each node and require the APs to periodically broadcast their BI signal and a quantity representing their TXOPs. This enables the joining STA to estimate throughput from candidate APs before selecting one. We use NS-2 simulations to demonstrate the effectiveness of our algorithms for saturated UDP and TCP downlink traffic, and compare them with received signal power (rxpwr) algorithm, load based algorithm, and Fukuda algorithm. For a random topology consisting of 24 APs and 60 STAs, our algorithms increase the joining STAs average throughput by as much as 42% and 24% compared to rxpwr for UDP and TCP respectively. In addition, the achieved average throughput is 90% and 93% of that obtained via the optimal selection. We also show that, in contrast to rxpwr, the throughput of proposed algorithms remains close to optimal with the increase in AP or STA density.
Matching pursuit video coding .I. Dictionary approximation

We have shown in previous works that overcomplete signal decomposition using matching pursuits is an efficient technique for coding motion-residual images in a hybrid video coder. Others have shown that alternate basis sets may improve the coding efficiency or reduce the encoder complexity. In this work, we introduce for the first time a design methodology which incorporates both coding efficiency and complexity in a systematic way. The key to the method is an algorithm which takes an arbitrary 2-D dictionary and generates approximations of the dictionary which have fast two-stage implementations according to the method of Redmill et al. ). By varying the quality of the approximation, we can explore a systematic tradeoff between the coding efficiency and complexity of the resulting matching pursuit video encoder. As a practical result, we show that complexity reduction factors of up to 1000 are achievable with negligible coding efficiency losses of about 0.1-dB PSNR.
Video multicast using layered FEC and scalable compression

The use of scalable video with layered multicast has been shown to be an effective method to achieve rate control in heterogeneous networks. We propose the use of layered forward error correction (FEC) as an error-control mechanism in a layered multicast framework. By organizing FEC into multiple layers, receivers can obtain different levels of protection commensurate with their respective channel conditions. Efficient network utilization is achieved as FEC streams are multicast, and only to receivers that need them. Furthermore, FEC is used without overall rate expansion by selectively dropping data layers to make room for FEC layers. Effects of bursty losses are amortized by staggering the FEC streams in time, giving rise to a tradeoff between delay and quality. For rate control at the receivers, we propose an equation-based approach that computes network usage as a function of measured network characteristics. We show that equation-based rate control achieves more fair bandwidth sharing amongst competing sessions as compared to existing multicast rate control schemes such as RLM and RLC. Fairness is achieved since competing sessions sharing a path will measure similar network characteristics. Simulations and actual MBONE experiments are performed using error-resilient, scalable video compression. We find that video quality is significantly improved at the same communication rate when layered FEC is used.
Highly scalable, low-delay video compression

We propose a class of scalable video compression algorithms, within which compression performance may be exchanged for end-to-end delay. Each of the algorithms in this class produces a highly scalable bit stream, from which subsets may be extracted for compatibility with a wide range of display frame sizes, frame rates and bit rate constraints. Moreover, with modest end-to-end delay, the reconstructed video quality associated with any of these subsets is often superior to that obtained using an implementation of the inherently non-scalable MPEG-1 compression standard, operated with equivalent resolution and bit rate constraints. We describe scalable compressed data structures based on a layered substream abstraction, with simple, generic scaling operations, for both constant bit rate and constant distortion scaling criteria.<<ETX>>
Analysis of tones in the double loop /spl Sigma//spl Delta/ modulator with unstable filter dynamics

In this paper we analyze the tone behavior of the double loop /spl Sigma//spl Delta/ modulator with unstable filter dynamics. We show that some unstable limit cycles have an attractor region in their neighborhood which results in tones in the spectrum corresponding to the fundamental or harmonics of these limit cycles. We develop the tools to determine whether or not an unstable limit cycle has an attractor in its neighborhood and obtain a method to approximate the boundaries of these attractors.<<ETX>>
Inverse problem for two-dimensional fractal sets using the wavelet transform and the moment method

Fractal geometry has provided statistical and deterministic models for classes of signals and images that represent many natural phenomena and objects. Iterated function systems (IFS) theory provides a convenient way to describe and classify deterministic fractals in the form of a recursive definition. As a result, it is conceivable to develop image representation schemes based on the IFS parameters that correspond to a given fractal image. The authors propose the use of the wavelet transform and of the moment method for the solution of the inverse problem of recovering IFS parameters from fractal images. The redundancy of a fractal with respect to scale variation is mirrored by its wavelet decomposition, thus providing a method to estimate the scaling parameters for a class of IFSs modeling the image. Displacement parameters and probabilities are then found using the moment method. Experimental results verifying the approach are presented.<<ETX>>
Content analysis of video using principal components

We use principal component analysis (PCA) to reduce the dimensionality of features of video frames for the purpose of content description. This low-dimensional description makes practical the direct use of all the frames of a video sequence in later analysis. The PCA representation circumvents or eliminates several of the stumbling blocks in current analysis methods and makes new analyses feasible. We demonstrate this with two applications. The first accomplishes high-level scene description without shot detection and key-frame selection. The second uses the time sequences of motion data from every frame to classify sports sequences.
Matching-pursuit video coding .II. Operational models for rate and distortion

For pt. I see ibid., vol.12 , no.1, (2000).We introduce two models for predicting the rate and distortion of the matching-pursuit video codec. The first model is based on a pre-coding analysis pass using the full matching-pursuit dictionary. The second model is based on a reduced-complexity analysis pass. We evaluate these models for use within existing rate-distortion optimization techniques. Our prediction results suggest that the models have sufficient accuracy to be useful in this context, and that significant complexity reductions could be achieved compared to exact rate-distortion computation.
Inverse problem and approximation of fractal-like images

In this paper we consider an inverse problem and an approximation problem for fractal images. The inverse problem involves finding the Iterated Function System (IFS) parameters for a class of signals that are exactly generated via an IFS. We make use of the wavelet transform and of the image moments to solve the inverse problem. The approximation problem involves finding a fractal IFS generated image whose moments either match exactly or in a mean squared error sense a range of moments of the original image. The approximating measures are generated by an IFS model of a special form, and provide a general basis for the approximation of arbitrary images. Experimental results verifying our approach are presented.<<ETX>>
Reconstruction of oversampled band-limited signals from ΣΔ encoded binary sequences

Considers the application of /spl Sigma//spl Delta/ modulators to analog-to-digital conversion. The authors have previously shown that for constant input signals, optimal nonlinear decoding can achieve large gains in signal-to-noise ratio (SNR) over linear decoding. The present paper shows a similar result for band-limited input signals. The new nonlinear decoding algorithm is based on projections onto convex sets (POCS), and alternates between a time-domain operation and a band limitation to find a signal invariant under both. The time-domain operation results in a quadratic programming problem. The band limitation can be based on singular value decomposition of a certain matrix. The authors show simulation results for the SNR performance of a POCS-based decoder and a linear decoder for the single loop, double loop and two-stage /spl Sigma//spl Delta/ modulators and for a specific fourth-order interpolative modulator. Depending on the modulator and the oversampling ratio, improvements in SNR of up to 10-20 dB can be achieved.
Orientation adaptive subband coding of images

A subband coding scheme is proposed. It utilizes orientation of local image features to avoid the highly objectionable Gibbs-like phenomena observed at reconstructed image edges with conventional subband schemes at low bit rates. At comparable bit rates, the subjective image quality obtained by the authors' orientation adaptive scheme is considerably enhanced over a conventional separable subband coding scheme, as well as other separable approaches, such as the JPEG compression standard.<<ETX>>
Joint source/channel coding of scalable video over noisy channels

We propose an optimal bit allocation strategy for a joint source/channel video codec over noisy channel when the channel state is assumed to be known. Our approach is to partition source and channel coding bits in such a way that the expected distortion is minimized. The particular source coding algorithm we use is rate scalable and is based on 3D subband coding with multi-rate quantization. We show that using this strategy, transmission of video over very noisy channels still renders acceptable visual quality, and outperforms schemes that use equal error protection only. The flexibility of the algorithm also permits the bit allocation to be selected optimally when the channel state is in the form of a probability distribution instead of a deterministic state.
Tree detection in LiDAR data

In this paper, we present an approach to detecting trees in registered aerial image and range data obtained via LiDAR. The motivation for this problem comes from automated 3D city modeling, in which such data is used to generate the models. Representing the trees in these models is problematic because the data are usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both LiDAR and aerial imagery. In this paper we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region growing algorithm using weighted features from aerial image and LiDAR, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using weighted support vector machines (SVM), allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and the results presented as receiver operating characteristic curves are shown to validate our approach
An optimum cascade structure for the sigma delta modulator

We derive the MSE performance of the single Imp BA modulator for decoders based on average filtering. In doing so, we modify the basic Structnre of the modulator in order to improve its MSE chxacteristics. The modified structure cax either utilize a switch and a single loop modulator or a number of single loop XA modulators cascaded with each other. The hasic idea behind the modified structure is to requantize the residue assciated with each quantization Step in order to reduce the overdl quantization noise. Wc derive the optimum number of stages in the cascade structure for a fixed number of samples, and show the MSE performance of the cascade modulator to be considerably better than that of a single loop modulator.
Location-based image retrieval for urban environments

Image based localization is an important problem with many applications. The basic idea is to match a user generated query image against a database of geo-tagged images with known 6 degrees of freedom poses. Once this retrieval problem is solved, it is possible to recover the pose of the query image. A challenging problem in image retrieval is performance degradation as the size of the image database grows. In this paper we describe an approach to large scale image retrieval for user localization in urban environment by taking advantage of coarse position estimates available, e.g. via cell tower triangulation, on many mobile devices today. The basic idea is to partition the large image database for a large region into a number of overlapping cells each with its own prebuilt search and retrieval structure. We demonstrate retrieval results over a ∼12,000 image database covering a 1 km2 area of downtown Berkeley.
Lower bounds on the MSE of the single and double loop sigma delta modulators

It is shown that the single- and double-loop sigma-delta ( Sigma Delta ) encoders with constant input can be analyzed as nonuniform quantizers. Specifically, expressions are derived for the transition points, i.e. the input values at which the output sequence changes from one codeword to another. This is used to find upper bounds on the number of codewords that the encoders are capable of producing as source coders, given the initial state or states. The upper bound is O(N/sup 2/) for the single-loop encoder, and O(N/sup 3/) for the double-loop encoder, where N is the oversampling ratio. It is shown that the mean squared error (MSE) for both single- and double-loop decoders is lower-bounded by O(N/sup -3/). The optimal decoders based on table look-up decoding are shown to have O(N/sup -3/) MSE performance. For the double-loop encoder, an optimal decoder with O(N/sup -5/) MSE performance can be achieved by a slight reduction in the dynamic range of the input.<<ETX>>
Classifying urban landscape in aerial LiDAR using 3D shape analysis

The classification of urban landscape in aerial LiDAR point clouds is useful in 3D modeling and object recognition applications in urban environments. In this paper, we introduce a multi-category classification system for identifying water, ground, roof, and trees in airborne LiDAR. The system is organized as a cascade of binary classifiers, each of which performs unsupervised region growing followed by supervised, segment-wise classification. Categories with the most discriminating features, such as water and ground, are identified first and are used as context for identifying more complex categories, such as trees. We use 3D shape analysis and region growing to identify “planar” and “scatter” regions that likely correspond to ground/roof and trees respectively. We demonstrate results on two urban datasets, the larger of which contains 200 million LiDAR returns over 7km2. We show that our ground, roof, and tree classifiers, when trained on one dataset, perform well on the other dataset.
Content analysis of video using principal components

We use principal component analysis (PCA) to reduce the dimensionality of features of video frames for the purpose of content description. This low dimensional description makes practical the direct use of all the frames of a video sequence in later analysis. The PCA representation circumvents or eliminates several of the stumbling blocks in current analysis methods, and makes new analyses feasible. We demonstrate this with two applications. The first accomplishes high level scene description without shot detection and key frame selection. The second uses the time sequences of motion data from every frame to classify sports sequences.
Dictionary approximation for matching pursuit video coding

Previously, we demonstrated an efficient video codec based on overcomplete signal decomposition using matching pursuits. Dictionary design is an important issue for this system, and others have shown alternate dictionaries which lead to either coding efficiency improvements or reduced encoder complexity. We introduce for the first time a design methodology which incorporates both coding efficiency and complexity in a systematic way. The key to our new method is an algorithm which takes an arbitrary 2-D dictionary and generates approximations of the dictionary which have fast 2-stage implementations. By varying the quality of the approximation, we can explore a systematic tradeoff between the coding efficiency and complexity of the matching pursuit video encoder. As a practical result, we show cases where complexity is reduced by a factor of 500 to 1000 in exchange for small coding efficiency losses of around 0.1 dB PSNR.
Surface completion of shape and texture based on energy minimization

In this paper, we propose a novel surface completion method to generate plausible shapes and textures for missing regions of 3D models. The missing regions are filled in by minimizing two energy functions for shape and texture, which are both based on similarities between the missing region and the rest of the object; in doing so, we take into account the positive correlation between shape and texture. We demonstrate the effectiveness of the proposed method experimentally by applying it to two models.
Temporally-Consistent Phase Unwrapping for a Stereo-Assisted Structured Light System

Phase shifted sinusoidal projection patterns are extensively used in structured light systems due to their low decoding complexity and projection of only three frames per reconstruction. However, they require correct unwrapping of phase images to reconstruct depth accurately. For time varying scenes, it is important for the phase unwrapped results to be temporally coherent. In this paper, we propose a spatio-temporal phase unwrapping algorithm for a structured light system made of a projector and a pair of stereo cameras. Unlike existing frame by frame spatial domain approaches, our proposed algorithm results in a temporally consistent three-dimensional unwrapped phase volume for time varying scenes. We experimentally show the effectiveness of our approach in recovering absolute phase for scenes with multiple disjoint objects, significant motion, and large depth discontinuities.
Cross layer techniques for adaptive video streaming over wireless networks

Wireless multimedia delivery is becoming increasingly more important in today's networks. Unlike wired packet switched networks that suffer from congestion related loss and delay, the wireless networks have to deal with a time varying, error prone, physical channel that in many instances is also severely bandwidth constrained. As such, the solutions needed for wireless video streaming applications are fundamentally different from wired streaming. In this paper, we propose a set of end to end application layer techniques for adaptive video streaming over wireless networks. The adaptation is done both with respect to channel and data. Our approach combines the flexibility and programmability of application layer adaptations, with low delay and bandwidth efficiency of link layer techniques. Socket level simulations are presented to verify the effectiveness of our approach.
Planar 3D modeling of building interiors from point cloud data

We present an automatic system for planar 3D modeling of building interiors from point cloud data generated by range scanners. This is motivated by the observation that most building interiors may be modeled as a collection of planes representing ceilings, floors, walls and staircases. Our proposed system, which employs model-fitting and RANSAC, is capable of detecting large-scale architectural structures, such as ceilings and floors, as well as small-scale architectural structures, such as staircases. We experimentally validate our system on a number of challenging point clouds of real architectural scenes.
Watertight As-Built Architectural Floor Plans Generated from Laser Range Data

In this paper we describe an approach to automatically generate a floor plan for the interior environment of a building using laser scan data. This floor plan is meant to accurately indicate the positions of walls within an area of interest in the building. This proposed algorithm separates the floors of a building scan, selects a representative sampling of wall scans for each floor, and triangulates these samples to develop a watertight representation of the walls for each of the scanned areas. Curves and straight line segments are fit to these walls, in order to mitigate any registration errors from the original scans. This method is not dependent on the scanning system and can successfully process noisy scans with non-zero registration error. Most of the processing is performed after a dramatic dimensionality reduction, yielding a scalable approach. We demonstrate the effectiveness of our approach on a three-story point cloud from a commercial building as well as on the lobby and hallways of a hotel.
Stability and scaling of double loop Sigma Delta modulators

The application of the double loop Sigma Delta modulator to analog-to-digital conversion is considered. To avoid integrator saturation in implementations, it is necessary to know the maximum occurring integrator output voltages. The authors address this problem by deriving upper bounds on the steady-state values of integrator outputs for the general double loop modulator with constant inputs. They use the upper bounds to derive scaling factors that are optimized with respect to stability as well as an approximate performance measure. Simulations show that the results which are proven for constant inputs can be useful in the design of modulators with dynamic inputs, and that gains of about 2 dB in both dynamic range and peak signal-to-noise ratio can be achieved for time-varying inputs.<<ETX>>