nils-herrmann/clean_scirepeval huggingface dataset:
HeartBEAT: Heart beat estimation through adaptive tracking

In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart rate from wrist-type photo-plethysmography (PPG) signals and simultaneously recorded three-axis acceleration data. HeartBEAT contains three major parts: spectrum estimation of PPG signals and acceleration data, elimination of motion artifacts in PPG signals using recursive least Square (RLS) adaptive filters, and auxiliary heuristics. We tested HeartBEAT on the 22 datasets provided in the 2015  The first ten datasets were recorded from subjects performing forearm and upper-arm exercises, jumping, or pushing-up. The last twelve datasets were recorded from subjects running on tread mills. The experimental results were compared to the ground truth heart rate, which comes from simultaneously recorded electrocardiogram (ECG) signals. Compared to state-of-the-art algorithms, HeartBEAT not only produces comparable Pearson's correlation and mean absolute error, but also higher Spearman's ρ and Kendall's τ.
Technology and Tools to Enhance Distributed Engineering Education

While the ongoing information technology (IT) revolution is providing us with tremendous educational opportunities, educators and IT researchers face numerous obstacles and pedagogical questions. The Georgia Institute of Technology (Georgia Tech) has a long history in engineering education research and has developed and designed many different tools for instructor authoring, course capturing, indexing, and retrieval. Special attention has been applied to the design and deployment of distributed learning environments. This paper describes the environment and challenges facing Georgia Tech as it expands its campus worldwide while maintaining integration of faculty and students across these campuses. The focus is primarily on the problems of synchronous delivery to multiple sites with a description of how technology is currently being deployed in Georgia Tech's distance-learning (DL) classrooms, as well as technology that is under development for the DL classroom of the future.
Cooperative MAC and routing protocols for wireless ad hoc networks

Cooperative diversity techniques exploit the spatial characteristics of the network to create transmit-diversity, in which the same information can be forwarded through multiple paths towards a single destination or a set of destination nodes. In this paper, we study the integration of cooperative diversity into wireless routing protocols by developing distributed cooperative MAC (C-MAC) and routing protocols. The proposed protocols employ efficient relay selection-coordination and power allocation techniques to maximize the cooperation benefits in the network. Simulation results show that the energy-saving performance of the minimum-energy routing protocols can be significantly improved when they are implemented together with the proposed C-MAC protocol (%50). We also show that the performance of the C-MAC protocol can be further enhanced when the initial path is selected using the cooperation characteristics of the network (%11 more energy-savings compared to the previous case, i.e., C-MAC with minimum-energy routing)
Arm Movement Prediction Using Neural Networks

Whether interacting with a collaborative virtual environment, or CVE, locally or one networked across the Internet, any delay in the system can lead to a reduced sense of immersion. Input sensor delay and network delay are two common problems in CVE design that can be overcome with the application of prediction algorithms to the system. The purpose of this experiment was to assess the quality of feed forward back propagation neural networks in predicting natural avatar arm movement typically used in a CVE. In addition the experiment attempts to find the bounds for precise neural network prediction. The results show many different combinations of back propagation neural network topologies are capable of predicting up to 400 ms of human arm movements relatively accurately.
Coding of 3D videos based on visual discomfort

We propose a rate-distortion optimization method for 3D videos based on visual discomfort estimation. We calculate visual discomfort in the encoded depth maps using two indexes: temporal outliers (TO) and spatial outliers (SO). These two indexes are used to measure the difference between the processed depth map and the ground truth depth map. These indexes implicitly depend on the amount of edge information within a frame and on the amount of motion between frames. Moreover, we fuse these indexes considering the temporal and spatial complexities of the content. We test the proposed method on a number of videos and compare the results with the default rate-distortion algorithms in the H.264/AVC codec. We evaluate rate-distortion algorithms by comparing achieved bit-rates, visual degradations in the depth sequences and the fidelity of the depth videos measured by SSIM and PSNR.
Subsurface Exploration: Recent Advances in Geo-Signal Processing, Interpretation, and Learning [From the Guest Editors]

The articles in this special section focus on subsurface exploration using new geo-signaling technologies. For centuries, humans have been exploring the subsurface structure of planet Earth. Several Earth geophysical applications, such as mining, earthquake studies, and oil and gas exploration, have driven research that produced, over the years, ground-breaking theories and innovative technologies that image Earth’s subsurface. The pursuit is ongoing with an increasing desire to have higher-resolution subsurface models and images. Signal processing, data interpretation, and modeling have been the cornerstones of such innovations. In recent years, there have been advances in technologies and requirements that demand the utilization of advanced signal processing and machine-learning theories and algorithms. For example, the wide- and full-azimuth acquisition technologies have proven to be instrumental in providing high-resolution subsurface images.
Effectiveness of 3VQM in capturing depth inconsistencies

The 3D video quality metric (3VQM) was proposed to evaluate the temporal and spatial variation of the depth errors for the depth values that would lead to inconsistencies between left and right views, fast changing disparities, and geometric distortions. Previously, we evaluated 3VQM against subjective scores. In this paper, we show the effectiveness of 3VQM in capturing errors and inconsistencies that exist in the rendered depth-based 3D videos. We further investigate how 3VQM could measure excessive disparities, fast changing disparities, geometric distortions, temporal flickering and/or spatial noise in the form of depth cues inconsistency. Results show that 3VQM best captures the depth inconsistencies based on errors in the reference views. However, the metric is not sensitive to depth map mild errors such as those resulting from blur. We also performed a subjective quality test and showed that 3VQM performs better than PSNR, weighted PSNR and SSIM in terms of accuracy, coherency and consistency.
From Admiration, Celebration, and Guessing to Innovation [From the Editor]

At the time of this writing (February 2010), I, just like many other colleagues, am finalizing my travel arrangements to attend the 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) in Dallas, Texas,  Of course, you are reading this editorial in May 2010, a few weeks or more after ICASSP. This sequence of events will hopefully (the best one can aim for!) make this editorial resonate. Over the past few days, I have been going through the various Web pages on the ICASSP Web site and the activities planned during that week in March; it is all exciting, very exciting. Of course, those who have papers at ICASSP will attend to share their findings and results with other colleagues, and will conduct oral presentations or posters. But besides this main activity of presenting papers, I am looking forward to meeting colleagues and friends, discussing and exchanging ideas, listening to a well-planned series of plenary talks, attending several technical and special sessions that are most related to my own research, learning about the new trends and ideas, attending the IEEE Thematic Meetings on Signal Processing (THEMES), meeting new people, touring the show and tell event, and of course participating in various social activities, including the reception and the banquet. I feel ICASSP is our Society's annual celebration, perhaps the digital signal processing (DSP) Davos?
Packetized media streaming over multiple wireless channels

This paper addresses the problem of streaming packetized media through a proxy server to a mobile client over multiple wireless channels. We follow a rate-distortion optimized streaming approach. A real-time sender-driven streaming framework is employed at the proxy server to maximize the playback quality at the client. The experimental results show that the proposed algorithm improves the video quality by 1.3-3dB as compared to the conventional system which simply transmits the video data in the order they are displayed.
Energy-Efficient Cluster-based Distributed Estimation in Wireless Sensor Networks

In this paper, we consider the cluster-based distributed estimation in wireless sensor networks where energy is a constrained resource. First, we present a hybrid cluster-based estimator and show its potential to save energy while keep the estimation performance. Next the optimal tradeoff between the cluster size and the number of clusters is analyzed for a special network ring network. Then, we propose two greedy algorithms to cluster the general sensor networks to minimize the total energy cost of cluster-based estimation scheme by modelling the network as a directed or undirected graph. Simulation results not only show that the energy cost is reduced by cluster-based estimation scheme compared with the parallel estimation scheme while keeping the estimation performance, but also show that more energy is saved by our proposed clustering methods compared with the k-mean clustering method
A no-reference quality measure for DIBR-based 3D videos

In this paper we present a no-reference objective quality measure for stereoscopic 3D videos generated by depth image-based rendering (DIBR). At first we will derive an ideal depth estimate for each pixel value. The ideal depth estimate will then be used to calculate three distortion measures: temporal outliers (TO), temporal inconsistencies (TI), and spatial outliers (SO). The combination of the three measures constitute the proposed no-reference measure. Its performance is verified using subjective DMOS scores and compared to the full reference version of the proposed algorithm. The subjective results show that the proposed measure highly correlates with subjective scores and is close in performance to the full reference version of the measure.
Depth-less 3D rendering

We propose a new view synthesis without using a depth map for stereoscopic images with small baseline. The new technique utilizes the linear relationship between local luminance changes and small horizontal shifts in an image. In this paper, we prove the linear relationship between local luminance changes and small horizontal shifts. Then, we will use this relationship to derive a 3D wrapping to render 3D images in absence of a depth map. The rendered images using the proposed technique will be evaluated subjectively and objectively and compared to rendered images using depth maps.
Joint position and amplitude coding in hybrid variable length coding for video compression

Hybrid variable length coding (HVLC) was recently proposed as a novel entropy coding scheme for block-based image and video compression, which divides each transform block into low frequency (LF) region and high frequency (HF) region and codes them differently. To efficiently code LF region, a two-dimensional position and one-dimensional amplitude coding scheme (2DP1DA) was also proposed, which jointly codes the 2D position information, i.e., run of consecutive zero-valued coefficients and run of consecutive nonzero coefficients. To further explore the potential of HVLC concept, we propose a new scheme for coding LF region, which codes the 2D position and amplitude information of each nonzero cluster jointly with manageable complexity. The experimental results show that compared with CAVLC in H.264, about 3.5% bit rate reduction is achieved by the proposed method for a wide range of quantization parameters (QP).
Context-adaptive hybrid variable length coding in H.264/AVC

It is well observed that the nonzero transform coefficients for block-based hybrid video coding is more clustered in the low frequency region while more scattered in the high frequency region. Based on this observation, hybrid variable length coding (HVLC) was recently proposed, which divides each transform block into low frequency (LF) region and high frequency (HF) region by a pre-defined breakpoint and encodes the clustered low frequency coefficients with new coding schemes, such as two dimensional position and one-dimensional amplitude coding (2DP1DA) or three-dimensional position and amplitude coding (3DPA). However, the transition between clustered and scattered nonzero coefficients varies with different blocks and sequences. In this paper, we propose a context-adaptive hybrid variable length coding (CAHVLC) scheme, where multiple code tables are used and the code table is adaptively chosen based on the derivable context information from coded neighboring blocks or from the coded portion of the currently being coded block. The experimental results show that CAHVLC achieves about 6.5% ∼ 8% bit rate reduction for a wide range of quantization parameters (QP) compared with CAVLC in H.264.
Improved DCT coefficient distribution modeling for H.264-like video coders based on block classification

Through extensive experimentation with a large set of video sequences, we show that modeling the statistical distribution of the transform coefficients in H.264-like video coders can be improved significantly in terms of accuracy by classifying the video source into multiple classes and modeling each class with a different statistical distribution. In this paper, we present a simple yet effective classification method and best practical models for each class and show that it is possible to improve the statistical modeling significantly without a significant complexity increase. We propose a two-class based approach in which one class is composed of very low detail blocks, and the other class is composed of high texture blocks and blocks with edges. Our two-class based statistical modeling reduces the approximation error up to 70% over the existing single-class modeling approaches for majority of the video sequences experimented. Furthermore, this approach also fits very well with the context of rate control with human visual system considerations, in which distortion in low detail regions of an image is more noticeable than in high detailed regions. In this work, we consider modeling the transform coefficients all lumped together.
Reduced-reference perceptual quality assessment for video streaming

We propose a perceptual video quality monitoring metric for streaming applications using the optical flow features. This approach is a reduced-reference pixel-based and relies only on the deviation of the optical flow of the corrupted frames. This techniques compares an optical flow descriptor from the corrupted frame against the descriptor obtained from the anchor frame. This approach is suitable for videos with complex motion patterns. Our technique does not make any assumptions on the coding conditions, network loss patterns or error concealment techniques. We validate our proposed metric by testing it on a variety of distorted sequences from the LIVE mobile database. Our results show that our metric estimates the perceptual quality at the sequence level accurately. We report the correlation coefficients with the differential mean opinion scores (DMOS) reported in the database. The results show Spearman's correlations of 0.88 for the tested sequences.
Quality matters, especially in sharing knowledge [From the Editor]

These days, all it takes is a few clicks of the mouse for a person to access massive amounts of information and data through Web pages, blogs, or social-centric Web sites. This easy-to-access and fast-to-find information is what makes the era we live in a special one. But with all this advancements comes the question of quality . Does the shared knowledge have high quality? What sources can one rely on to access quality knowledge? This is where publications such as IEEE Signal Processing Magazine (SPM) play an important role. Signal processing as indicated by our new editor-in-chief, Dr. Li Deng, is going through a transformation from focusing on low-level signals such as waveforms to dealing with semantic and human-centric signals. This signal processing transformation has to be accompanied by a metamorphosis in how the community exchanges high-quality knowledge and how the community members learn from each other.
Vector Quantization in Multiresolution Mesh Compression

Irregularly sampled triangular meshes contain vertices in a geometric space with arbitrary connectivity degrees. From a compression point of view, this letter presents a multiresolution analysis for irregular meshes using progressive vector quantization (VQ). We show that VQ exploits the joint distribution of coordinates in the geometric space, thus improving the compression efficiency. Based on the presented compression algorithm, we further propose an equalization algorithm that properly balances between connectivity downsampling and geometry quantization and achieves the optimal rate-distortion performance under constrained bit rates
No-reference quality assessment of HEVC videos in loss-prone networks

In this paper, we propose a no-reference quality assessment measure for high efficiency video coding (HEVC). We analyze the impact of network losses on HEVC videos and the resulting error propagation. We estimate channel-induced distortion in the video assuming we have access to the decoded video only without access to the bitstream or the decoder. Our model does not make any assumptions on the coding conditions, network loss patterns or error concealment techniques. The proposed approach relies only on the temporal variations of the power spectrum across the decoded frames. We validate our proposed quality measure by testing it on a variety of HEVC coded videos subject to network losses. Our simulation results show that the proposed model accurately captures channel-induced distortions. For the test videos, the correlation coefficients between the proposed measure and the full-reference SSIM values range between 0.70 and 0.80.
Statistical modeling of social networks activities

This paper introduces a new paradigm to characterize and understand the dynamics of a complex social network where we set up a mathematical platform that captures the network dynamics. We propose a novel generic non-parametric model to characterize a general system of social communicators. We divide the network into low-level entities, each of which has some independent features. The different entities are then combined using Bayesian nonparametric statistics, namely Dirichlet processes mixture models (DPMM). This set up was tested using a simulated case study where we show examples of its utility for behavior characterization and predictions.
Joint Framework for Motion Validity and Estimation Using Block Overlap

This paper presents a block-overlap-based validity metric for use as a measure of motion vector (MV) validity and to improve the quality of the motion field. In contrast to other validity metrics in the literature, the proposed metric is not sensitive to image features and does not require the use of neighboring MVs or manual thresholds. Using a hybrid de-interlacer, it is shown that the proposed metric outperforms other block-based validity metrics in the literature. To help regularize the ill-posed nature of motion estimation, the proposed validity metric is also used as a regularizer in an energy minimization framework to determine the optimal MV. Experimental results show that the proposed energy minimization framework outperforms several existing motion estimation methods in the literature in terms of MV and interpolation quality. For interpolation quality, our algorithm outperforms all other block-based methods as well as several complex optical flow methods. In addition, it is one of the fastest implementations at the time of this writing.
Block-overlap-based validity metric for hybrid de-interlacing

This paper presents a block-overlap-based validity metric in the context of hybrid de-interlacing. The proposed metric uses the degree of motion-compensated block overlap as a measure of motion vector (MV) validity. In contrast to other validity methods in the literature, the proposed method is not sensitive to image features and does not require the use of neighboring MVs or manual thresholds. To perform hybrid de-interlacing, the confidence value for each MV is used to weigh the contribution from two de-interlacing methods. Experimental results show that the de-interlaced images produced with the proposed method are of significant higher quality than those produced by existing validity methods.
Generating adaptive and robust filter sets using an unsupervised learning framework

In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated by evaluating their performance in two contrasting applications — image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures. Based on experiments, we show that the filter responses span a set in which a monotonicity-based metric can measure both the perceptual dissimilarity of natural images and the similarity of texture images. In addition, we corrupt the images in the test set and demonstrate that the proposed method leads to robust and reliable retrieval performance compared to existing methods.
Parity streams: a novel FEC scheme with the stream control transmission protocol

Conventional packet-level forward error correction (FEC) is found inapplicable with the stream control transmission protocol (SCTP), because of its partially ordered transport feature. To provide error resilience for respectively ordered data streams, we propose to generate "parity streams" parallel to the data streams. The use of parity streams allows multiple data streams to be protected concurrently and also scalably. For instance, unequal error protection (UEP) can be easily implemented within and/or across data streams, which is attractive to streaming-media applications where preferential treatment on different data units is desired