nils-herrmann/clean_scirepeval huggingface dataset:
Energy Efficient Data Collection via Supervised In-Network Classification of Sensor Data

In wireless sensor networks, data collection (or gathering) is the task of transmitting rounds of measurements of physical phenomena from the sensor nodes to a sink node. We study how to increase the efficiency of data collection via supervised in-network classification of rounds of measurements. We assume that the end users of the data are interested only in rounds characterized by certain patterns. Hence the wireless sensor network uses classification to select the rounds of measurements that are transmitted to the base station. The energy consumption is potentially reduced by avoiding the transmission of rounds of measurements that are not of interest to the end users. In-network classification requires distributed feature extraction and transmission. Such tasks can be less or more energy expensive than the transmission of measurements without classification. We provide analytical results and simulations on real data to show requirements and key trade-offs for the design of in-network data classification systems that can improve the collection efficiency. Besides, we study the impact of spatial subsampling of the sensor data (a way to further decrease energy consumption) on the classification performance.
Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection

A boosted convolutional neural network (BCNN) system is proposed to enhance the pedestrian detection performance in this work. Being inspired by the classic boosting idea, we develop a weighted loss function that emphasizes challenging samples in training a convolutional neural network (CNN). Two types of samples are considered challenging: 1) samples with detection scores falling in the decision boundary, and 2) temporally associated samples with inconsistent scores. A weighting scheme is designed for each of them. Finally, we train a boosted fusion layer to benefit from the integration of these two weighting schemes. We use the Fast-RCNN as the baseline, and test the corresponding BCNN on the Caltech pedestrian dataset in the experiment, and show a significant performance gain of the BCNN over its baseline.
Environmental sound recognition: A survey

Although research in audio recognition has traditionally focused on speech and music signals, the problem of environmental sound recognition (ESR) has received more attention in recent years. Research on ESR has significantly increased in the past decade. Recent work has focused on the appraisal of non-stationary aspects of environmental sounds, and several new features predicated on non-stationary characteristics have been proposed. These features strive to maximize their information content pertaining to signal's temporal and spectral characteristics. Furthermore, sequential learning methods have been used to capture the long-term variation of environmental sounds. In this survey, we will offer a qualitative and elucidatory survey on recent developments. It includes three parts: i) basic environmental sound processing schemes, ii) stationary ESR techniques and iii) non-stationary ESR techniques. Finally, concluding remarks and future research and development trends in the ESR field will be given.
Coding artifact removal with multiscale postprocessing

A multiscale post processing algorithm based on constrained optimization with the Huber Markov random field (HMRF) model is investigated. The decoded image is enhanced from coarse to fine scales, where postprocessing at the coarse scale improves the global appearance of the image and reduces long range artifacts such as ringing while postprocessing at the fine scale keeps the sharpness of edges. The efficiency of the proposed algorithm is supported by experimental results.
Texture analysis and classification with tree-structured wavelet transform

A multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, it is possible to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramid-structured wavelet transform performs further decomposition in low-frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed. The performance of the present method is compared with that of several other methods.
Fast H.264 Motion Estimation with Block-Size Adaptive Referencing (BAR)

A fast multiple reference assignment scheme is proposed to reduce the motion search complexity in the H.264 video standard in this work. The relationship between the temporal search range and the block size is exploited to develop a new algorithm, called the block-size adaptive referencing (BAR) scheme, that assigns a different number of references in a block-size adaptive manner. The expected rate-distortion (RD) coding loss due to the use of fewer references in the BAR scheme is first analyzed. Furthermore, it is found that the RD coding loss of BAR is related to the RD gradient through a model. Thus, a proper BAR scheme can be chosen to minimize the reference frame numbers while keeping the expected RD loss under a target level. Finally, fast motion search is performed within selected references in a block-size adaptive manner. Experimental results show that the proposed BAR scheme can save approximately 25-70% of the encoding complexity in H.264 (JM10.1) motion estimation with negligible degradation in the RD performance.
Joint spatial-spectral indexing for image retrieval

Processing images directly in the compressed domain is very beneficial from the standpoint of lowering the computational complexity. In this paper we present a method of extracting features directly from the DCT coefficients of JPEG compressed imagery for the purpose of aiding image retrieval from a database. Our approach is characterized by a quadtree-based spatial structuring of the image followed by calculation of DCT coefficient features at the various nodes. Features based on DC and energy of AC coefficients are shown to be effective for queries concerning color, edge and texture properties.
Adaptive resource allocation for multimedia services in wireless communication networks

Next-generation wireless communication networks aim at supporting multimedia services. It is a challenging problem to efficiently allocate limited and varying network resources in the wireless communication networks to a variety of multimedia services with different characteristics and requirements. In this paper, we extensively investigate the adaptive resource allocation for multimedia services in a wireless network environment. Both user mobility and the nature of multimedia traffic are taken into consideration to design our adaptive resource allocation scheme, which consists of call admission control, resource reservation and bandwidth adaptation for real-time video/audio applications, as well as scheduling for non-real-time data services. Simulation results show the strength of our proposed adaptive scheme.
Optimal Variable Length Markov Chain (VLMC) Modeling of Fading Channels

Channel characterization and modeling is essential to the wireless communication system design. A model that optimally represents a fading channel with a variable length Markov chain (VLMC) is proposed in this work. VLMC offers a general class of Markov chains whose structure has a variable order and a parsimonious number of transition probabilities. The proposed model consists of two main components: 1) the optimal fading partition under the constraint of a transmission policy and 2) the derivation of the best VLMC representation. The fading partition is used to discretize a continuous fading channel gain. The optimal discretization criterion is developed based on the cost function of fading channel statistics and the transmission policy used in the system. Once a continuous fading channel gain is discretized, a VLMC is then used to model the channel. To obtain the optimal VLMC representation, we use the Kullback-Liebler (K-L) distance as the optimization criterion. Finally, we show simulation results that demonstrate the accuracy of the proposed fading channel representation in modeling the Rayleigh fading as well as the log-normal fading.
Texture segmentation with tree-structured wavelet transform

A tree-structured wavelet transform has previously been developed and used for texture classification by the authors. The transform is able to zoom into dominant frequency channels containing significant texture information for further wavelet decomposition, and can be interpreted as the decomposition of a 2-D function with the wavelet packet basis function. The texture segmentation problem is addressed here. A texture segmentation algorithm based on the tree-structured wavelet transform and a hierarchical fuzzy clustering technique is proposed. Numerical experimental results are given.<<ETX>>
Image Quality Assessment Based on Local Linear Information and Distortion-Specific Compensation

Image quality assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper, we make two main contributions to the IQA field. First, a novel IQA method is developed based on a local linear model that examines the distortion between the reference and the distorted images for better alignment with human visual experience. Second, a distortion-specific compensation strategy is proposed to offset the negative effect on IQA modeling caused by different image distortion types. These score offsets are learned from several known distortion types. Furthermore, for an image with an unknown distortion type, a convolutional neural network-based method is proposed to compute the score offset automatically. Finally, an integrated IQA metric is proposed by combining the aforementioned two ideas. Extensive experiments are performed to verify the proposed IQA metric, which demonstrate that the local linear model is useful in human perception modeling, especially for individual image distortion, and the overall IQA method outperforms several state-of-the-art IQA approaches.
Joint Rate-Distortion-Complexity Optimization for H.264 Motion Search

A joint rate-distortion-complexity H.264 motion search framework is proposed to balance the encoder's coding efficiency and complexity in an embedded system environment. Under our framework, the complexity of H.264 motion search is primarily measured by the execution time of the sum of absolute differences (SAD) calculation. Two Lagrange parameters are used to terminate the complexity-inefficient motion search rounds and skip redundant motion search of small block modes, respectively. Then, the relationship between the weighted complexity and the Lagrange parameters is explored to allocate the complexity cost among different coding units. It is demonstrated by experimental results that the proposed method can reduce the complexity without much sacrifice in coding efficiency
Progressive multichannel audio codec (PMAC) with rich features

MPEG Advanced Audio Coding (AAC) is one of the most distinguished multichannel digital audio compression systems. Based on AAC, we develop a progressive syntax-rich multichannel audio codec in this work. It not only supports fine grain bit rate scalability for multichannel audio bitstreams but also provides several desirable functionalities. MPEG-4 version-2 audio coding supports fine grain bit rate scalablility in its Generic Audio Coder with the Bit-Sliced Arithmetic Coding (BSAC) tool that provides scalability for mono or stereo audio, but not for multichannel audio. A formal subjective listening test shows that, the proposed algorithm achieves a better performance at several different bit rates when compared with MPEG-4 BSAC for mono audio sources.
Robust video transmission using MPEG Markup Language and adaptive error correction codes

An error-resilient MPEG video coding scheme for wireless transmission using the MPEG Markup Language (MPML) and adaptive error correction codes (ECCs) is proposed. MPML is a portable XML description for MPEG video. MPML can provide a "selective" description of MPEG video bitstreams in terms of their basic components, such as headers, motion vectors, and residual information. We use efficient compression tools to ensure that the overhead of MPML tags is greatly reduced, and then adopt adaptive ECCs to protect video components unequally according to their importance. Experiments demonstrate that the proposed MPML protection can achieve good error resilience performance comparable with state-of-the-art error-resilient techniques.
An improved method for 2-D self-similar image synthesis

We propose a new method called incremental Fourier synthesis to generate 2-D self-similar images based on a 2D fractional Brownian motion (fBm) model. With this method, the stationary increments of fBm are created by a Fourier synthesis method and the increments are added up to generate the nonstationary 2D fBm process. Since the new method takes advantage of the FFT, its computational complexity is only O(N(2)log(2)(N)), and its memory requirement is only O(N(2)) for a self-similar image of size NxN.
Real-time streaming video with adaptive bandwidth control and DCT-based error concealment

This work deals with dropped streaming video packets from the viewpoint of minimizing future dropped packets, as well as restoring data lost in packets already dropped, specifically geared toward real-time transmission of H.263+ bitstream. Minimization of future dropped packets is achieved by an adaptive least mean squares bandwidth (BW) controller predicting the available bandwidth supported by the Internet with relatively little loss. Restoration of lost video data is performed by a combination of error concealment (EC) techniques, including interpolation of border pixels in DCT domain. All algorithms presented are computationally inexpensive and thus suited for real-time video applications.
Where am I? Scene Recognition for Mobile Robots using Audio Features

Automatic recognition of unstructured environments is an important problem for mobile robots. We focus on using audio features to recognize different auditory environments, where they are characterized by different types of sounds. The use of audio information provides a complementary means of scene recognition that can effectively augment visual information. In particular, audio can be used toward both the analysis and characterization of the environment at a higher level of abstraction. We begin our investigation of recognizing different auditory environments with the audio information. In this paper, we utilize low-level audio features from a mobile robot and investigate using high-level features based on spectral analysis for scene characterization, and a recognition system was built to discriminate between different environments based on these audio features found
Block-based image steganalysis: Algorithm and performance evaluation

Traditional image steganalysis techniques are conducted with respect to the entire image. In this work, we aim to differentiate a stego image from its cover image based on steganalysis results of decomposed image blocks. As a natural image often consists of heterogeneous regions, its decomposition will lead to smaller image blocks, each of which is more homogeneous. We classify these image blocks into multiple classes and find a classifier for each class to decide whether a block is from a cover or stego image. Consequently, the steganalysis of the whole image can be conducted by fusing steganalysis results of all image blocks through a voting process. Experimental results will be given to show the advantage of the proposed block-based image steganalysis approach.
Environmental sound recognition with CELP-based features

In this work, we propose the use of a set of new features based on CELP (Code Excited Linear Prediction) to enhance the performance of the environmental sound recognition (ESR) problem. Traditionally, Mel Frequency Cepstral Coefficients (MFCC) have been used for the recognition of structured data like speech and music. However, their performance for the ESR problem is limited. An audio signal can be well preserved by its highly compressed CELP bit streams, which motivates us to study the CELP-based features for the audio scene recognition problem. We present a way to extract a set of features from the CELP bit streams and compare the performance of ESR using different feature sets with the Bayesian network classifier. It is shown by experimental results that the CELP-based features outperform the MFCC features in the ESR problem by a significant 9% margin in average and the integrated MFCC and CELP-based feature set can even reach a correct classification rate of 95.2% using the Bayesian network classifier.
Musical beat tracking via Kalman filtering and noisy measurements selection

We study the problem of automatic musical beat tracking from acoustic data, i.e., finding locations of beats of a music piece by computers on-the-fly, in this work. An on-line musical beat tracking algorithm based on Kalman filtering (KF) with an enhanced probability data association (EPDA) method is proposed. The beat tracking algorithm is built upon a linear dynamic model of beat progression, to which the Kalman filtering technique can be conveniently applied. The beat tracking performance can be seriously degraded by noisy measurements in the Kalman filtering process. Three methods are presented for noisy measurements selection. They are the local maximum (LM) method, the probabilistic data association (PDA) method and the enhanced PDA (EPDA) method. We see that the performance of EPDA outperforms that of LM and PDA significantly.
Internet voice playout scheduling for integrated delay and error concealment with time-scale modification

Packet delay and loss are two essential problems to real-time voice transmission over the Internet. Some effort has been involved in packet-level delay jitter concealment and error control. To minimize the impact of packet delay jitter dropping at the receiver, a packet-based Synchronized OverLap-and-Add (SOLA) algorithm was proposed in  to play out packets adaptively. By employing the forward error correction (FEC) for network loss recovery, this paper further extends the applicability of audio time-scale modification to an integrated delay/error concealment Performance improvement of the proposed adaptive playout is provided by comparing with the silenced-based, FEC-integrated adaptive playout. Overhead of the proposed concealment is discussed in terms of delay and complexity. We also investigate the impact of stretching-ratio transition on the perceived quality.
Structural analysis of genomic sequences with matched filtering

A pattern filtering approach is developed to analyze genomic sequences in this work. With this approach, the distance of a certain pattern is first translated into a "gap sequence" consisting of integer numbers. Different patterns result in different gap sequences, and the similarity measure of two genomic sequences can be made based upon the processing of gap sequences generated by a set of pre-selected patterns. A matched filtering approach is applied to gap sequences. Furthermore, several post-processing techniques are applied to the filtered result for signal enhancement. For example, the modified Butterworth window (MBW) is used to remove the edge effect of the matched filter output, and the uncertain region is beleaguered by the advanced similarity test (AST) algorithm. The match between gap sequences is called a "frame match". The actual match of two genomic sequences demands both frame match and stuffing match. The proposed approach is useful for sequence analysis based on the frame match with desirable patterns. Extensive experimental results are presented to demonstrate the performance of the proposed method.
A dynamic error concealment for video transmission over noisy channels

A dynamic mode-weighted error concealment method is proposed for video packets transmitted over noisy channels. We first introduce two error concealment approaches. One is to reconstruct lost pixels by interpolating candidate pixels indicated by neighboring motion vectors. The other is to estimate the motion vector by a side matching algorithm. Based on the two error concealment approaches, four corrupted block reconstruction modes are described. Then, the value of an erroneous pixel is replaced by a weighted sum of those reconstructed by two modes. The property of the weighted sum is analyzed. It is shown that the optimal weighting coefficients can be expressed as a formula in terms of the error variance and the correlation coefficients associated with the reconstruction modes. Furthermore, based on the decoder-based error tracking model, these weighting coefficients are dynamically updated to minimize the instant propagation and concealment error variance. Extensive simulations are provided to demonstrate that the proposed method can lead to a satisfying performance in an error-prone environment.
Lifetime maximization for amplify-and-forward cooperative networks

Joint relay-selection and power-allocation strategies are devised to prolong the lifetime of amplify-and-forward (AF) cooperative networks. Lifetime is defined as the time duration within which the desired signal-to-noise ratio (SNR) at the destination is met with a certain probability. Based on selective relaying, we propose three strategies that take into account the local channel state information (CSI) and the local residual energy information (REI) at each relay to prolong the network lifetime. With a finite number of power levels, the energy dissipation process can be modeled as a finite-state Markov chain and the optimal lifetime maximization strategy can be derived using dynamic programming. We demonstrate that the network lifetime can be extended considerably by exploiting both CSI and REI via numerical simulation. The performance of the proposed strategies that utilize only local CSI and REI is shown to be comparable to that of the optimal strategy that demands global CSI and REI.
On optimal prefiltering for wavelet coefficient computation

The authors use an algorithm proposed by M.J. Shensa (1992) for computing the coefficients of the wavelet series transform (WST) or continuous wavelet transform (CWT) to filter a sampled discrete-time signal and then apply S. Mallat's (1989) discrete wavelet transform (DWT) algorithm to the filtered sequence. The prefiltering is used to reduce the approximation error between the computed and desired coefficients. The design of optimal causal and noncausal FIR prefilters which reduce the approximation error as much as possible is considered. Numerical experiments are provided to demonstrate the performance of these optimal prefilters.<<ETX>>
WLC23-6: Performance Analysis of Ultra-Wide Band Impulse Radio (UWB-IR) with Super-Orthogonal Turbo Codes (SOTC)

Low-complexity low-rate super-orthogonal turbo codes (SOTC) are proposed in this work to replace the implicit repetition code (RC) in an ultra-wide band impulse radio (UWB-IR) system to improve BER, transmission range and system throughput. Various receivers including matched-filter and RAKE receivers are adopted for data detection in the additive white Gaussian noise (AWGN) channel and the indoor IEEE 802.15.3a channel. The performance of SOTC-encoded UWB is analyzed and validated by computer simulation. It is shown that the SOTC-based UWB-IR system can achieve significant performance improvement over the conventional DS- UWB system encoded by RC.