nils-herrmann/clean_scirepeval huggingface dataset:
Image Estimation Using Doubly Stochastic Gaussian Random Field Models

The two-dimensional (2-D) doubly stochastic Gaussian (DSG) model was introduced by one of the authors to provide a complete model for spatial filters which adapt to the local structure in an image signal. Here we present the optimal estimator and 2-D fixed-lag smoother for this DSG model extending earlier work of Ackerson and Fu. As the optimal estimator has an exponentially growing state space, we investigate suboptimal estimators using both a tree and a decision-directed method. Experimental results are presented.
Distortion-optimal receiver grouping for MD-FEC coded video streaming

Multiple Description with Forward Error Correction (MD-FEC) coding provides the flexibility, easy adaptivity and distortion-rate optimality that are desirable for delivering streaming video in a network environment with time-varying bandwidth fluctuations and random packet losses. In this paper, we consider the issue of how diverse receivers of a video stream should be grouped - where each group receives a MD-FEC coded bitstream optimized for that group - so that the average video distortion is minimized across all receivers. We show that a sequential grouping solution is optimal for linear distortion-rate functions. For non-linear distortion-rate functions, while the optimal grouping structure may not be sequential in general, we observe that the approximation factor attained by the best sequential solution can be characterized in terms of the “degree of convexity” of the distortion-rate function. Numerical experiments with realistic distortion-rate functions reveal that the difference between the globally optimal grouping solution and the best sequential solution, is typically small. We provide a dynamic programming based polynomial-time algorithm to compute the best sequential solution.
A framework for implementing cooperative motion on industrial controllers

The use of two or more robots jointly manipulating a common object, termed cooperative control, has many advantages in industrial applications. Much of the previous work on cooperative control, however, has emphasized capabilities, such as joint torque control, not generally present on industrial robots. This paper applies the concept of cooperative control to industrial robots by taking advantage of those capabilities that do exist on industrial robots. Specifically, a design approach that makes use of tool-based coordinate systems, trajectory generation, real-time modification, and distributed control of multiple robots is presented. These concepts are implemented on a system of two industrial robots using an adaptive fuzzy controller, and the results are discussed.
Joint source-network error control coding for scalable overlay video streaming

In this paper, we propose a joint source-network error control coding (JSNC) scheme which efficiently integrates scalable video coding, error control coding and overlay infrastructure to stream video to heterogeneous users. The distributed overlay nodes adapt both the video bitstream and error control coding based on both user requirements and available bandwidth. A novel fine granular adaptive FEC (FGA-FECcheme, a generalization of MD-FEC, is proposed for error recovery during video transmission to heterogeneous users. Encoding once, the FGA-FEC can satisfy multiple heterogeneous users simultaneously without decoding/re-encoding FEC at intermediate nodes.
Blue electroluminescence in forward-biased ZnSe diodes

The electroluminescence emission spectrum of forward-biased MIS diodes prepared on high-purity single-crystal ZnSe has been studied over the temperature range 20-290 K. At room temperature the emission consisted of two intense bands in the blue at 4655 and 4770 Å, while at the lowest temperatures much more structure was observed. Thus at 20 K free and bound exciton emission was particularly intense, and the donor-acceptor (DA) pair band emission (the Q series), together with its accompanying free-to-bound series was also prominent. The bound exciton, pair band, and free-to-bound emission were all substantially quenched as the temperature was increased, so that at room temperature the two blue bands were the only remaining components of the luminescence. Evidence is produced to demonstrate that the band at 4655 Å is associated with free exciton recombination following scattering from free electrons in the conduction band. The band at 4770 Å is attributed to free exciton recombination with the emission of two longitudinal optical (LO) phonons.
Aliasing Reduction via Frequency Roll-Off for Scalable Image/Video Coding

The extracted low-resolution video from a motion compensated 3-D subband/wavelet scalable video coder is unnecessarily sharp and sometimes contains significant aliasing, compared to that obtained by the MPEG4 lowpass filter. In this paper, we propose a content adaptive method for aliasing reduction in subband/wavelet scalable image and video coding. We try to make the low-resolution frame (LL subband) visually and energy-wise similar to that of the MPEG4 decimation filter through frequency roll-off. Scaling of the subbands is introduced to make the variances of the subbands comparable in these two cases. Thanks to the embedded properties of the EZBC coder, we can achieve the needed scaling of energies in each subband by subbitplane shift in the extractor and value (coefficient) scaling in the decoder. Two methods are presented which offer substantial peak signal-to-noise ratio (PSNR) gain for lower spatial resolution, as well as substantial reduction in visible aliasing, with little or no reduction in full resolution PSNR.
Characterization and Modeling of LDMOS Power FETs for RF Power Amplifier Applications

In this review we present a measurement-based approach to the creation of a successful circuit model of a high-power RF FET. We describe some of the measurement challenges that we face in the characterization and validation of the FET model, and our approach to their solution. We also outline some of the simulation and modeling techniques that are used in the construction of the complete transistor model. The model itself is fully nonlinear, with a self-consistent dynamic electrothermal component, and includes the in-package matching and package components, which are derived from electro-magnetic simulations.
Evolution analysis of Binary Partition Tree for hierarchical video simplified segmentation

This paper proposes volumetric hierarchical video segmentation and evolution analysis. The contribution of this paper is twofold: Firstly, a single Binary Partition Tree (BPT) is proposed to represent the entire video segmentation. Every node represents not only a single region but also a series of correlated regions in subsequent frames. Secondly, we propose a method to identify a stop merging criteria by exploiting the discontinuity of volume evolution. The pre-segmentation of the video is produced by 26 neighbourhood watershed. A volume adjacency graph (VAG) is constructed and merging cost between all neighbouring volume as edges. Iterative merging among neighbouring volumes is performed sequentially from the lowest volume distance. Volume model combines colour and temporal direction. Every iteration produces new larger volume; a new node is issued the VAG is updated. The edge in the VAG with the lowest merging cost a selected in every iteration. The history of merging is recorded in the BPT. In order to identify salient nodes, volume evolution analysis is proposed; progressing from the initial partition at the lowest leaf of the BPT towards the root. Discontinuity in the evolution of colour mean and temporal direction indicates a reluctance to merge between a pair of volumes. Therefore, they may belong to different objects. The evolution analysis identifies all salient nodes in the temporal domain of the BPT. In order to simplify the BPT, nodes below a salient node are pruned. The processing gives salient nodes with temporal registration over a video sequence for higher-level cognition.
Calibrating Pan-Tilt Cameras in Robot Hand-Eye Systems Using a Single Point

This paper describes a method that requires only a single projected point to calibrate a pair of cameras attached to pan-tilt units (PTUs) in a hand-eye robot configuration. Most existing calibration methods require either a set of known calibration locations or a large number of corresponding image features. These requirements usually imply human intervention, which complicates the calibration procedure. Rather than using multiple points in the workspace, the proposed algorithm uses a single stationary point generated by aiming a laser at a planar surface for the entire calibration. The single point provides a high intensity feature that is easy to extract from the image. To exercise all of the PTU and camera degrees of freedom, a large number of random robot/PTU configurations are computed automatically and 60 configurations, where both cameras can extract the single calibration point, are retained. As a result, this approach minimizes image processing requirements and virtually eliminates the correspondence problem. Using the Levenberg-Marquardt non-linear minimization algorithm, the Denavit-Hartenberg (D-H) parameters for the pan-tilt axes along with the internal and external parameters of the cameras are computed. This method requires no human intervention, employs simple image processing to detect the high intensity laser point, and produces a triangulation error of roughly 3mm at a range of 1m.
Fine-Grained Scalable Video Caching

Caching has been shown to enhance network performance. In this paper, we study fine-grain scalable video caching. We start from a single cache scenario by providing a solution to the caching allocation problem that optimizes the average expected video quality for the most popular video clips. Actual trace data is applied to verify the performance of our algorithm and compare its backhaul link bandwidth consumption relative to non-scalable video caching. In addition, we extend our analysis to collaborative caching and integrate network coding for further transmission efficiency. Our experimental results demonstrate considerable performance enhancement.
Fully orthogonal multi-carrier predistortion linearization for RF power amplifiers

This paper presents a fully orthogonal frequency-selective baseband predistortion linearization system for RF multi-carrier power amplifiers (PA) affected by strong differential memory effects. A new scheme is introduced for removing the unwanted inband frequency components generated by the interband predistortion corrections, thus establishing full orthogonality between the interband and inband predistortion. The demonstration study is performed on a two-carrier OFDM signal of 8 MHz bandwidth each, separated by 16 MHz for a total RF bandwidth of 32 MHz. Both the OFDM signal generator and the new orthogonal multi-carrier linearization algorithm proposed were implemented in a field programmable gate array (FPGA) and applied to the experimental investigation of the linearization of a Class AB/Class C LDMOS PA at 3.5 GHz. It is demonstrated that with the new orthogonality implementation, 5 dB inband distortion introduced by the interband predistortion steps is automatically suppressed such that multiple iterative steps between the interband and inband linearizations are no longer required in the optimization. Adjacent channel leakage ratio (ACLR) of up to −45 dBc for inband and interband are demonstrated experimentally.
Region-based analysis and retrieval for tracking of semantic objects and provision of augmented information in interactive sport scenes

This paper introduces techniques for segmentation and tracking based on analysis of region derived descriptors. By partitioning the image into a series of homogeneous regions, the problem of object extraction changes from pixel based to database analysis. A region based approach has distinct advantages over pixel. In particular, it has low dimensionality, is resilient to rotation, shear, and photometric changes, preserves boundaries, gives implied segmentation, provides comprehensive description, has reduced drift, and permits detailed analysis. The method is amenable to the introduction of prior knowledge permitting the simplification of segmentation, which is otherwise an ill-posed problem. Objects are defined as collections of contiguous regions with known statistics, allowing their identification by performing correlations in the database. The interframe difference of region based sequences is high for low-motion scenes due to segmentation noise, but performs consistently better for high motion, making it suitable for tracking. When applied to tracking the method proves robust, occlusion insensitive, and unlike the other techniques, it re-establishes lost lock.
A nonlinear electro-thermal model for high power RF LDMOS transistors

A new nonlinear, charge-conservative, dynamic electro-thermal compact model for LDMOS RF power transistors is described in this paper. The transistor is characterized using pulsed I-V and S-parameter measurements, to ensure isothermal conditions. The intrinsic model current and charge sources are obtained by integration of the real and imaginary components, respectively, of the small-signal Y-parameters: this yields a charge-conservative model by design. A thermal sub-circuit is used to introduce dynamic thermal dependence, and thermal threshold voltage shift is built in. DC and large-signal validation of the model is presented.
Wide Band Suppression of Motion-Induced Vibration

Motion profiles such as swing free and input shaping are intended to eliminate residual vibrations. This paper demonstrates a simple generalized method of creating and understanding pulse-based profiles, and presents an approach to suppress a wider band of induced vibration than previously attainable. The current approach of input shaping ignores the potential contributions the base profile and pulse shape can have on the profile. When examined from a signal processing point of view these pulse-based profiles can be thought of as sampled low pass filters. The sampling of the filter kernel defines the magnitude of the pulses, but as in any digital system it also produces aliasing. An interpolating filter can then be used to both remove the aliasing and to smooth and shape the base profile. This dual purpose of the interpolating filter allows the use of the swing free step input in conjunction with a rate-limit as a base profile. A generalized methodology is proposed which can create smooth motion profiles with continuous derivatives, while keeping the computational efficient of traditional input shaping. Experimental validation of two multi-DOF systems is shown.
Portable Space Mapping for Efficient Statistical Modeling of Passive Components

In this paper, a portable space-mapping technique is presented for efficient statistical modeling of passive components. The proposed technique utilizes the cost-effective model composition of a statistical space mapping, while introducing the portable mapping concept for flexible model development for passive modeling. The portable mapping is a single-development-multiple-use versatile wrapper, such that after development it can be conveniently combined with any nominal model to form a set of statistical models of different speed and accuracy. This provides variety in model selection for different design needs. To further reduce modeling cost, i.e., the simulation time required for model data generation, a smart sampling technique is used to achieve better sampling fidelity with smaller sample size. The portable statistical mapping technique is demonstrated through modeling a transmission line and a spiral inductor.
Statistical Space Mapping Approach for Large-Signal Nonlinear Device Modeling

An efficient nonlinear statistical modeling technique for microwave devices is presented. A new statistical space mapping concept is introduced that can expand a large-signal nominal model into a large-signal statistical model. The nominal model is extracted or trained from one complete set of large-signal data. The statistical property is achieved by a dynamic mapping between the behavior of the nominal model and that of the statistical samples of a given population of devices. The parameters in the mapping, which are statistical parameters, can be extracted from DC and small-signal S-parameter data of many device samples. Example of a MESFET device modeling and its use in statistical design of a three-stage amplifier circuit demonstrate that the statistical space-mapped model can approximate the large-signal statistical characteristics using only one set of large-signal data. It helps to efficiently develop large-signal statistical models while reducing the expense of otherwise massive large-signal measurements for many devices
Low level switched model-based coder for low bit-rate applications

Switching a model-based coder to a standard transform coder during times of poor modelling can exploit the favourable attributes of an MBC whilst maintaining the image integrity. Previous investigations have employed switching where the entire frame is interchanged between a modelled image and an H.261 coded image according to a bit-rate/quality metric. This contribution explores the use of switching only in selected regions at a macroblock level and demonstrates that the subjective quality and PSNR are improved, particularly for scenes containing large translational and rotational motion.
Unpinning the GaAs Fermi level with thin heavily doped silicon overlayers

A theoretical investigation is made into the unpinning of the GaAs Fermi level (E/sub f/) at Schottky contacts by thin interfacial layers of heavily doped Si. The pinning mechanism is assumed to be a plane of interface states in the GaAs. The method used is to solve Poisson's equation numerically as a two-point boundary problem across the semiconductor. The results show that E/sub f/ can be moved from its pinned position to near the edge of the silicon valence band maximum or conduction band minimum at the Si/GaAs heterojunction with heavily doped p-Si or n-Si overlayers, respectively. This unpinning is observed with and without thick metallization on the Si. Exactly analogous results are obtained for Ge interfacial layers. The results are in good agreement with the experimental results obtained by J.R. Waldrop and R.W. Grant (1988). Although the unpinning is seen to occur for interface-state densities sufficient to pin E/sub f/ at the free GaAs surface, interface-state densities high enough to result in significant E/sub f/ pinning at metal/GaAs contacts are seen to prevent such unpinning by Si interfacial layers. It is therefore suggested that Si (or Ge) deposition gives rise to fewer interface states in the GaAs than does metal deposition.
An extrinsic component parameter extraction method for high power RF LDMOS transistors

A new extrinsic network and extrinsic parameter extraction methodology is developed for high power RF LDMOS transistor modeling. This new method uses accurate manifold de-embedding using electromagnetic simulation, and optimization of the extrinsic network parameter values over a broad frequency range. The new extrinsic network accommodates feedback effects which are observed in high power transistors. This improved methodology allows us to achieve a good agreement between measured and modeled S-parameters in the frequency range of 0.5 to 6 GHz for different bias conditions. Large-signal verification of this new model shows a very good match with measurements at 2.14 GHz.
Kalman filtering in two dimensions

The Kalman filtering method is extended to two dimensions. The resulting computational load is found to be excessive. Two new approximations are then introduced. One, called the strip processor, updates a line segment at a time; the other, called the reduced update Kalman filter, is a scalar processor. The reduced update Kalman filter is shown to be optimum in that it minimizes the post update mean-square error (mse) under the constraint of updating only the nearby previously processed neighbors. The resulting filter is a general two-dimensional recursive filter.
Smart Modeling of Microwave Devices

Modeling and computer-aided design (CAD) techniques are essential for microwave design, especially with the drive towards first-pass design success. We have described neural networks for microwave modeling and design. Neural networks are suitable when modeling a required relationship for which analytical formulas are hard to derive, or for which the computational effort is too high. This relationship can be either of the IO relationship of the overall model (straight neural network model), the output-input relationship (inverse model), a relationship between existing model and desired data (neuro-SM), or relationship of a subpart of the overall model (knowledge based neural network). Neural networks are fast to evaluate, and the neural network formulas are easy to implement into microwave CAD. The simplicity of adding input neurons or hidden neurons makes neural network flexible in handling functions of different dimensions and of different degree of nonlinearity. We have also demonstrated that neural networks are helpful in developing parametric or scalable models for passive and active microwave devices.
Time-frequency transforms: a new approach to first heart sound frequency dynamics

The binomial joint time-frequency transform is used to test the hypothesis that first heart sound frequency rises during the isovolumic contraction period. Cardiac vibrations were recorded from eight open-chest dogs using an ultralight accelerometer cemented directly to the epicardium of the anterior left ventricle. Three characteristic time-frequency spectral patterns were evident in the animals investigated: (1) a frequency component that rose from approximately 40-140 Hz in a 30-50-ms interval immediately following the ECG R-wave, (2) a slowly varying or static frequency of 60-100 Hz beginning midway through the isovolumic contraction period, and (3) broadband peaks occurring at the time of the Ia and Ib high frequency components. The binomial transform provided much better resolution than the spectrograph or spectrogram. By revealing the onset and dynamics of first heart sound frequencies, time-frequency transforms may allow mechanical assessment of individual cardiac structures.<<ETX>>
Adaptive coding of moving objects for very low bit rates

This paper describes an object-based video coding system with new ideas in both the motion analysis and source encoding procedures. The moving objects in a video are extracted by means of a joint motion estimation and segmentation algorithm based on the Markov random field (MRF) model. The two important features of the presented technique are the temporal linking of the objects, and the guidance of the motion segmentation with spatial color information. This facilitates several aspects of an object-based coder. First, a new temporal updating scheme greatly reduces the bit rate to code the object boundaries without resorting to crude lossy approximations. Next, the uncovered regions can be extracted and encoded in an efficient manner by observing their revealed contents. The objects are classified adaptively as P objects or I objects and encoded accordingly. Subband/wavelet coding is applied in encoding the object interiors. Simulations at very low bit rates yielded comparable performance in terms of reconstructed PSNR to the H.263 coder. The object-based coder produced visually more pleasing video with less blurriness and devoid of block artifacts, thus confirming the advantages of object-based coding at very low bit-rates.