nils-herrmann/clean_scirepeval huggingface dataset:
Neural net based continuous phase modulation receivers

Feed-forward neural nets (NN) are proposed as receivers for partial-response CPM (continuous phase modulation) systems. The approach involves replacing the entire receiver structure, excluding timing recovery, with a neural-net unit whose inputs are time samples of the incoming baseband signals and whose outputs are the decoded symbols. The authors present simulation results for coherent and incoherent NN-based receivers, and compare their performance with the optimum maximum-likelihood (ML) receiver. A performance analysis of NN-based receivers at high signal-to-noise ratio (SNR) is presented. There is an excellent match between the predicted and actual noise in the network at high SNR. It is found that at high SNR the ratio between the output and input noise variance is a constant depending on the number of saturated nodes, the product of the input and output weights, and the temperature parameter of the nonlinearity.<<ETX>>
Interference Aware Multipath Selection for Video Streaming in Wireless Ad Hoc Networks

In this paper, we propose a novel multipath selection framework for video streaming over wireless ad hoc networks. We propose a heuristic interference-aware multipath routing protocol based on the estimation of concurrent packet drop probability of two paths, taking into account interference between links. Through both simulations and actual experiments, we show that the performance of the proposed protocol is close to that of the optimal solution, and is better than that of other heuristic protocols.
Flow Control Over Wireless Network and Application Layer Implementation

Flow control, including congestion control for data transmission, and rate control for multimedia streaming, is an important issue in information transmission in both wireline and wireless networks. Widely accepted flow control methods in wireline networks are TCP (1) for data, and TCP Friendly Rate Control (TFRC) (2) for multimedia. Kelly (3) (4) has laid down theoretical framework for TCP in wireline networks demonstrating its optimality, fairness, and stability. However, TCP and TFRC both assume that packet loss in wireline networks is primarily due to congestion, and as such, are not applicable to wireless networks in which the bulk of packet loss is due to errors at the physical layer. In this paper we first show flow control in the wireless networks can be formulated as the same concave optimization problem Kelly defined in the wireline networks. TCP and TFRC in the wireless networks pursue the optimal solution using inaccurate feedback. All existing approaches to this TCP/TFRC over wireless problem correct the inaccurate feedback by casting modifications to existing protocols, such as TCP, or infrastructure elements such as routers, thereby making them hard to deploy in practice. In this paper, we formulate the problem as another concave optimization problem with a different utility function, and propose a new class of solutions. Our approach is end-to-end, and achieves reasonable performance by adjusting the number of connections of a user according to a properly selected control law. The control law is based on only one bit of information, which can be reliably measured at the application layer. We show that the control system has a unique stable equilibrium that solves the concave optimization problem, implying scalability and optimality of the solution. We apply our results to design a practical rate control scheme for data transmission over wireless networks, and characterize its performance using NS-2 simulations and actual experiments over Verizon Wireless 1xRTT data network. Analysis and simulation results also indicate our scheme is applicable to both wireline and wireless scenarios.
Fast, Automated, Scalable Generation of Textured 3D Models of Indoor Environments

3D modeling of building architecture from mobile scanning is a rapidly advancing field. These models are used in virtual reality, gaming, navigation, and simulation applications. State-of-the-art scanning produces accurate point-clouds of building interiors containing hundreds of millions of points. This paper presents several scalable surface reconstruction techniques to generate watertight meshes that preserve sharp features in the geometry common to buildings. Our techniques can automatically produce high-resolution meshes that preserve the fine detail of the environment by performing a ray-carving volumetric approach to surface reconstruction. We present methods to automatically generate 2D floor plans of scanned building environments by detecting walls and room separations. These floor plans can be used to generate simplified 3D meshes that remove furniture and other temporary objects. We propose a method to texture-map these models from captured camera imagery to produce photo-realistic models. We apply these techniques to several data sets of building interiors, including multi-story datasets.
Learning dictionaries for matching pursuits based video coders

We present a learning scheme for designing dictionaries of two-dimensional functions for matching pursuits (MP) based video coding. The motivation is to improve the performance of such codecs by adapting the structure of the dictionary functions to specific bit-rates or types of sequences. The scheme we propose is based on vector quantization (VQ), and uses an inner-product based distortion measure. The different processing steps, consisting of data extraction from the motion compensated error frames, training, pruning, and testing, are presented in detail. We find that for high bit-rate QCIF sequences we can achieve improvements of up to 0.66 dB.
Path Selection for Multi-Path Streaming in Wireless Ad Hoc Networks

In this paper, we propose a novel multi-path selection framework for streaming over wireless ad hoc networks. Our approach is to approximately estimate the concurrent packet drop probability of two paths by taking into account the interference between different links, and to select the best path pair based on that estimation. We prove the optimal path selection problem to be NP-hard, and propose a heuristic solution, whose performance is shown to be close to that of the optimal solution, while significantly outperforming other heuristic protocols.
Constructing 3D city models by merging ground-based and airborne views

In this paper, we present a fast approach to automated generation of textured 3D city models with both high details at ground level, and complete coverage for bird's-eye view. A close-range facade model is acquired at the ground level by driving a vehicle equipped with laser scanners and a digital camera under normal traffic conditions on public roads; a far-range Digital Surface Map (DSM), containing complementary roof and terrain shape, is created from airborne laser scans, then triangulated, and finally texture mapped with aerial imagery. The facade models are first registered with respect to the DSM by using Monte-Carlo-Localization, and then merged with the DSM by removing redundant parts and filling gaps. The developed algorithms are evaluated on a data set acquired in downtown Berkeley.
Binary combinatorial coding

A novel binary entropy code, called combinatorial coding (CC), is presented. The theoretical basis for CC has been described previously under the context of universal coding, enumerative coding, and minimum description length. The code described in these references works as follows: assume the source data are binary of length M, memoryless, and generated with an unknown parameter /spl theta/ (the probability that a "1" occurs). The compression efficiency, and encoding and decoding speed of CC against Huffman and arithmetic coding were tested. Over the entire test, CC achieved the compression efficiency of arithmetic coding, together with the coding speed of Huffman coding.
Variable bit rate MPEG video storage on parallel disk arrays

We discuss issues related to the storage and retrieval of variable bit rate (VBR) MPEG video data on parallel disk arrays for multiple simultaneous users. We store data blocks corresponding to constant real-time playback duration. Because of the VBR characteristics and the constant bandwidth read channel, disk overload occurs at peak usage periods. Our strategy to deal with disk overload is to temporarily stop service to low-priority users. To do so, we propose a number of classes of service corresponding to various probabilities of loss, during which time service is suspended. Thus, a major problem is estimating probability of loss or suspension so that a video server system can guarantee service with actual loss probabilities that do not exceed the specified thresholds at the call setup time. We propose three techniques for computing loss probabilities: histogram convolution, Central Limit Theorem, and Cramer's rule. We present an efficient placement strategy and an optimal admissions control strategy that guarantees loss probability thresholds while maximizing the number of requests that can be satisfied at all qualities. We extend these ideas to encompass scalability by allowing appropriate frames in the MPEG-encoded data to be dropped without fully suspending service to any one user. This allows the system to make intelligent choices in gracefully degrading the request data rates during periods of peak usage. Finally, we test the data placement and admissions control algorithms with a discrete event disk array system simulator and show that the results are in accordance with theoretical predictions.<<ETX>>
View generation for three-dimensional scenes from video sequences

This paper focuses on the representation and view generation of three-dimensional (3-D) scenes. In contrast to existing methods that construct a full 3-D model or those that exploit geometric invariants, our representation consists of dense depth maps at several preselected viewpoints from an image sequence. Furthermore, instead of using multiple calibrated stationary cameras or range scanners, we derive our depth maps from image sequences captured by an uncalibrated camera with only approximately known motion. We propose an adaptive matching algorithm that assigns various confidence levels to different regions in the depth maps. Nonuniform bicubic spline interpolation is then used to fill in low confidence regions in the depth maps. Once the depth maps are computed at preselected viewpoints, the intensity and depth at these locations are used to reconstruct arbitrary views of the 3-D scene. Specifically, the depth maps are regarded as vertices of a deformable 2-D mesh, which are transformed in 3-D, projected to 2-D, and rendered to generate the desired view. Experimental results are presented to verify our approach.
Lossless Compression Algorithms for Hierarchical IC Layout

An important step in today's integrated circuit (IC) manufacturing is optical proximity correction (OPC). While OPC increases the fidelity of pattern transfer to the wafer, it also significantly increases IC layout file size. This has the undesirable side effect of increasing storage, processing, and I.O. times for subsequent steps of mask preparation. In this paper, we propose two techniques for compressing layout data, including OPC layout, while remaining compliant with existing industry standard formats such as OASIS and GDSII. Our approach is to eliminate redundancies in the representation of the geometrical data by finding repeating groups of geometries between multiple cells and within a cell. We refer to the former as ldquointercell subcell detection (InterSCD)rdquo and the latter as ldquointracell subcell detection (IntraSCD).rdquo We show both problems to be nondeterministic polynomial time hard (NP-hard), and propose two sets of heuristics to solve them. For OPC layout data, we also propose a fast compression method based on IntraSCD which utilizes the hierarchical information in the pre-OPC layout data. We show that the IntraSCD approach can also be effective in reconstructing hierarchy from flattened layout data. We demonstrate the results of our proposed algorithms on actual IC layouts for 90-nm, 130-nm, and 180-nm feature size circuit designs.
Constructing 3D City Models by Merging Aerial and Ground Views

We describe an approach to register and merge detailed facade models with a complementary airborne model. The airborne modeling process provides a half-meter resolution model with a bird's-eye view of the entire area, containing terrain profile and building tops. The ground-based modeling process results in a detailed model of the building facades. Using the DSM obtained from airborne laser scans, we localize the acquisition vehicle and register the ground-based facades to the airborne model by means of Monte Carlo localization (MCL). We merge the two models with different resolutions to obtain a 3D model.
Decoder complexity and performance comparison of matching pursuit and DCT-based MPEG-4 video codecs

Matching pursuits is an overcomplete expansion technique which has been successfully applied to the problem of coding motion residual images in a hybrid video coder. In this paper, the coding efficiency and decoder complexity of the method are compared to that of the DCT-based MPEG-4 standard with and without post-processing. Without post-processing, matching pursuits is shown to have significantly better PSNR and visual quality and similar decoding complexity compared to the MPEG-4 DCT decoder. To achieve reasonable quality at low bit rates, the DCT-based scheme requires post-processing, while the patching pursuit scheme does not. We show that the MPEG-4 post-processing filters have a prohibitive cost, increasing decoder complexity by a factor of 3 to 8. Finally, we introduce an all-integer matching pursuit implementation. The performance is shown to be within 0.05 dB of the original floating point algorithm.
Modulus quantization for matching-pursuit video coding

Overcomplete signal decomposition using matching pursuits has been shown to be an efficient technique for coding motion-residual images in a hybrid video coder. Unlike orthogonal decomposition, matching pursuit uses an in-the-loop modulus quantizer which must be specified before coding begins. This complicates the quantizer design, since the optimal quantizer depends on the statistics of the matching-pursuit coefficients which in turn depend on the in loop quantizer actually used. In this paper, we address the modulus quantizer design issue, specifically developing frame-adaptive quantization schemes for the matching-pursuit video coder. Adaptive dead-zone subtraction is shown to reduce the information content of the modulus source, and a uniform threshhold quantizer is shown to be optimal for the resulting source. Practical two-pass and one-pass algorithms are developed to jointly determine the quantizer parameters and the number of coded basis functions in order to minimize coding distortion for a given rate. The compromise one-pass scheme performs nearly as well as the full two-pass algorithm, but with the same complexity as a fixed-quantizer design. The adaptive schemes are shown to outperform the fixed quantizer used in earlier works, especially at high bit rates, where the gain is as high as 1.7 dB.
An optimization approach for removing blocking effects in transform coding

This paper proposes a new approach which can be applied to conventional transform coding without introducing additional information or significant blurring. It exploits the correlation between intensity values of boundary pixels of two neighboring blocks. Specifically, it is based on the theoretical and empirical observation that under mild assumptions, quantization of transform coefficients of two neighboring blocks increases the mean squared difference (MSD) between neighboring pixels on their boundaries by an amount dependent upon the width of quantization intervals of the transform coefficients. Therefore, among all permissible inverse quantized coefficients, the set which minimizes this MSD is most likely to reduce the blocking effect. To estimate the set of unquantized coefficients, a constrained quadratic programming problem is solved in which the quantization decision intervals provide upper and lower bound constraints on the coefficients. Computer simulations are used to evaluate the performance of the proposed technique for the 256*256 image known as Lena.<<ETX>>
Simultaneous fingerprinting and mapping for multimodal image and WiFi indoor positioning

In this paper, we propose an end-to-end system which can be used to simultaneously generate (a) 3D models and associated 2D floor plans and (b) multiple sensor e.g. WiFi and imagery signature databases for the large scale indoor environments in a fast, automated, scalable way. We demonstrate ways of recovering the position of a user carrying a mobile device equipped with a camera and WiFi sensor in an indoor environment. The acquisition system consists of a man portable backpack of sensors carried by an operator inside buildings walking at normal speeds. The sensor suite consists of laser scanners, cameras and an IMU. Particle filtering algorithms are used to recover 2D and 3D path of the operator, a 3D point cloud, the 2D floor plan, and 3D models of the environment. The same walkthrough that produces 2D maps also generates multi-modal sensor databases, in our case WiFi and imagery. The resulting WiFi database is generated much more rapidly than existing systems due to continuous, rather than stop-and-go or crowd-sourced WiFi signature acquisition. We also use particle filtering algorithms in an Android application to combine inertial sensors on the mobile device, with 2D maps and WiFi and image sensor databases to localize the user. Experimental for the second floor of the electrical engineering building at UC Berkeley campus show that our system achieves an average localization error of under 2m.
Automatic Indoor 3D Surface Reconstruction with Segmented Building and Object Elements

Automatic generation of 3D indoor building models is important for applications in augmented and virtual reality, indoor navigation, and building simulation software. This paper presents a method to generate high-detail watertight models from laser range data taken by an ambulatory scanning device. Our approach can be used to segment the permanent structure of the building from the objects within the building. We use distinct techniques to mesh the building structure and the objects to efficiently represent large planar surfaces, such as walls and floors, while still preserving the fine detail of segmented objects, such as furniture or light fixtures. Our approach is scalable enough to be applied on large models composed of several dozen rooms, spanning over 14,000 square feet. We experimentally verify this method on several datasets from diverse building environments.
Atom modulus quantization for matching pursuit video coding

We provide an analytical study of the selection and modulus quantization of matching pursuits (MP) coefficients. We demonstrate that an optimal rate-distortion trade-off is achieved by selecting the atoms up to a dead-zone threshold, and by defining the modulus quantizer in terms of that threshold. In doing so, we take into account quantization error re-injection resulting from inserting the modulus quantizer inside the MP atom computation loop. In-loop quantization affects the stepsize of the uniform quantizer, and results in a non-uniform optimal entropy constrained quantizer. Improvements larger than one dB are obtained for video coding.
Path diversity with forward error correction (PDF) system for packet switched networks

Packet loss and end-to-end delay limit delay sensitive applications over the best effort packet switched networks such as the Internet. In our previous work, we have shown that substantial reduction in packet loss can be achieved by sending packets at appropriate sending rates to a receiver from multiple senders, using disjoint paths, and by protecting packets with forward error correction. In this paper, we propose a path diversity with forward error correction (PDF) system for delay sensitive applications over the Internet in which, disjoint paths from a sender to a receiver are created using a collection of relay nodes. We propose a scalable, heuristic scheme for selecting a redundant path between a sender and a receiver, and show that substantial reduction in packet loss can be achieved by dividing packets between the default path and the redundant path. NS simulations are used to verify the effectiveness of PDF system.
Tree Detection in Urban Regions Using Aerial Lidar and Image Data

In this letter, 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 3-D city modeling, in which such data are 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 letter, 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 the weighted support vector machines, 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
Consistent Stereo-Assisted Absolute Phase Unwrapping Methods for Structured Light Systems

Phase shifted sinusoidal patterns have proven to be effective in structured light systems, which typically consist of a camera and projector. They offer low decoding complexity, require as few as three projection frames per reconstruction, and are well suited for capturing dynamic scenes. In these systems, depth is reconstructed by determining the phase projected onto each pixel in the camera and establishing correspondences between camera and projector pixels. Typically, multiple periods are projected within the set of sinusoidal patterns, thus requiring phase unwrapping on the phase image before correspondences can be established. A second camera can be added to the structured light system to help with phase unwrapping. In this work, we present two consistent phase unwrapping methods for two-camera stereo structured light systems. The first method enforces viewpoint consistency by phase unwrapping in the projector domain. Loopy belief propagation is run over the graph of projector pixels to select pixel correspondences between the left and right camera that align in 3-D space and are spatially smooth in each 2-D image. The second method enforces temporal consistency by unwrapping across space and time. We combine a quality guided phase unwrapping approach with absolute phase estimates from the stereo cameras to solve for the absolute phase of connected regions. We present results for both methods to show their effectiveness on real world scenes.