nils-herrmann/clean_scirepeval huggingface dataset:
H.264 Coarse Grain Scalable (CGS) and Medium Grain Scalable (MGS) Encoded Video: A Trace Based Traffic and Quality Evaluation

The scalable video coding (SVC) extension of the H.264/AVC video coding standard provides two mechanisms, namely coarse grain scalability (CGS) and medium grain scalability (MGS), for quality scalable video encoding, which varies the fidelity (signal-to-noise ratio) of the encoded video stream. As H.264/AVC and its SVC extension are expected to become widely adopted for the network transport of video, it is important to thoroughly study their network traffic characteristics, including the bit rate variability. In this paper, we report on a large-scale study of the rate-distortion (RD) and rate variability-distortion (VD) characteristics of CGS and MGS. We found that CGS achieves low bit rate overheads in the 10–30% range compared to H.264 SVC single-layer encodings only for encodings with a total of up to three quality levels; more quality levels result in substantially higher overheads. The traffic variabilities of CGS are generally lower than for single-layer streams. We found that in the low to mid range of the MGS quality scalability, MGS can achieve the same or even slightly higher RD efficiency than corresponding single-layer encoding; toward the upper end of the MGS quality scalability range the RD efficiency drops off significantly. MGS layer extraction following the hierarchical B frame structure gives nearly as high RD performance as RD-optimized extraction. In the range of high RD efficiency, MGS streams have significantly higher traffic variabilities than single-layer streams at the frame time scale. At the group-of-pictures (GoP) time scale, MGS has similar or lower levels of traffic variability compared to single-layer streams. Generally, MGS layer extraction over the time horizon of individual GoPs gives significantly lower traffic variability than extraction over the time horizon of the full video sequence.
Human Visual System Based No-Reference Objective Image Sharpness Metric

This work is motivated by the fact that existing no-reference sharpness metrics fail in predicting the correct amount of blurriness in images with different contexts. This paper presents a no-reference objective sharpness metric that can be applied to images with different contexts. The metric combines a human visual system (HVS)-based sharpness perception model as well as a local features extractor resulting in a content-invariant metric. The proposed HVS-based sharpness perception model is derived from performed subjective tests. Simulation results and comparison with existing no-reference sharpness metrics show that the proposed HVS-based sharpness metric correlates well with the perceived sharpness and is able to correctly predict the relative amount of blurriness in images with different contexts.
Subjective characteristics for stereoscopic high definition video

Stereoscopic high definition video is one of the promising next-generation video services because 3D video can express more life-like visual experiences. 3D video quality of experience is affected by the employed codec and video format. Therefore, we conducted extensive subjective assessments for side-by-side and frame-sequential video sequences using different codecs. In this paper, we first show that the quality and depth perception of the frame-sequential video sequence are higher than those of the side-by-side video sequence for uncompressed video sequences. Then, using the encoded video sequences, we show that the quality and depth perception depend on the amount of spatio-temporal information and that a high-quality 3D video sequence with a MOS-Q (mean opinion score for quality with a five-grade scales) > 3.5, is required to achieve better depth perception than 2D video. Finally, we show that the naturalness of the encoded 3D video sequences correlates highly with the quality.
Automated characterization of cellular migration phenomena

A new approach for cell migration analysis is presented from an automated image segmentation and ROI-compression. ROI-compression is accomplished through classical edge detection technique, followed by image opening to help identify the cell-occupied image region. That region is then compressed for better archival and analysis, to more efficiently identify migration rate and dispersion. Combining our approach with compression techniques such as offered by JPEG2000 will enhance the analysis of the images by providing the ability to vary parameters, work on compressed domain including proximity-based clustering of cells and cell count, etc.
On-line laboratories for image and two-dimensional signal processing

This paper presents innovative on-line Java-based educational DSP software modules that were developed to render possible on-line laboratories for two-dimensional digital signal processing. The developed software modules provide on-line 2-D DSP capabilities, including 2D signal generation, 2D FIR filter design & implementation, and 2D transforms. On-line image processing capabilities are also provided, including image restoration and enhancement. In order to illustrate 2D concepts graphically, contour (2D) and perspective (3D) plots have also been implemented and incorporated as part of the developed software tools. On-line laboratory exercises have been developed in the aforementioned areas for use in the graduate-level multidimensional signal processing and image processing courses at ASU. Statistical and qualitative evaluations are presented to assess the effectiveness of the developed on-line 2D DSP software and laboratories in improving the learning experiences of the students.
DNA-Residual: A DNA Compression Algorithm using Forward Linear Prediction

This paper presents an efficient lossless DNA compression algorithm, DNA-residual, that significantly decreases the average bit-rate required to losslessly code correlated DNA sequences. The algorithm can be divided into two parts: modeling and coding. The modeling part consists of mapping the DNA bases into a binary representation and, then, a forward linear prediction filter is used to predict the current input from the previous ones. The prediction error is then transformed into a binary error sequence that is coded using an adaptive binary arithmetic coder. Compared to state-of-the-art compressors using benchmark DNA sequences, the proposed algorithm reveals a significantly higher compression ratio whenever correlation between bases is high
ECG data compression using wavelets and higher order statistics methods

This paper evaluates the compression performance and characteristics of two wavelet coding compression schemes of electrocardiogram (ECG) signals suitable for real-time telemedical applications. The two proposed methods, namely the optimal zonal wavelet coding (OZWC) method and the wavelet transform higher order statistics-based coding (WHOSC) method, are used to assess the ECG compression issues. The WHOSC method employs higher order statistics (HOS) and uses multirate processing with the autoregressive HOS model technique to provide increasing robustness to the coding scheme. The OZWC algorithm used is based on the optimal wavelet-based zonal coding method developed for the class of discrete "Lipschitizian" signals. Both methodologies were evaluated using the normalized rms error (NRMSE) and the average compression ratio (CR) and bits per sample criteria, applied on abnormal clinical ECG data samples selected from the MIT-BIH database and the Creighton University Cardiac Center database. Simulation results illustrate that both methods can contribute to and enhance the medical data compression performance suitable for a hybrid mobile telemedical system that integrates these algorithmic approaches for real-time ECG data transmission scenarios with high CRs and low NRMSE ratios, especially in low bandwidth mobile systems.
Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: An educational tool to physicians

Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract and, especially, small intestine. However, reviewing the endoscopic data is time consuming and requires intense labor of highly experienced physicians. These limitations were the motive to propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract. Towards this direction, WCE data are color-rotated in order to boost the chromatic attributes of ulcer regions. Then, texture information is extracted by utilizing the local binary pattern operator that analyses the spatial structure of the images at a very local level. Experimental results demonstrated promising classification accuracy (91.1%) exhibiting high potential towards a complete computer-aided diagnosis system that will not only reduce WCE data reviewing time, but also serve as an assisting tool for the training of inexperienced physicians.
MYOS: an efficient multichannel EMG data acquisition and analysis system

A computer-based data acquisition and analysis system was developed customized to EMG measurement and analysis requirements. The system was designed as an assistive tool for researchers studying human electrophysiology and kinesiology. A user-friendly interface characterized by a great adaptability and compatibility with other similar existing software packages was implemented, enabling multichannel acquisition, data storage and analysis. The application integrates the most frequent processing techniques required by EMG researchers and analysts, as well as a number of advanced signal processing techniques adjusted to the features of the EMG signal. The system comprises flexible signal analysis and display modules, forming a fully operational tool that can be easily configured to serve any similar purposed measurement requirements.
Age Effect in Human Brain Responses to Emotion Arousing Images: The EEG 3D-Vector Field Tomography Modeling Approach

Understanding of the brain responses to emotional stimulation remains a great challenge. Studies on the aging effect in neural activation report controversial results. In this paper, pictures of two classes of facial affect, i.e., anger and fear, were presented to young and elderly participants. High-density 256-channel EEG data were recorded and an innovative methodology was used to map the activated brain state at the N170 event-related potential component. The methodology, namely 3D Vector Field Tomography, reconstructs the electrostatic field within the head volume and requires no prior modeling of the individual's brain. Results showed that the elderly exhibited greater N170 amplitudes, while age-based differences were also observed in the topographic distribution of the EEG recordings at the N170 component. The brain activation analysis was performed by adopting a set of regions of interest. Results on the maximum activation area appeared to be emotion-specific; the anger emotional conditions induced the maximum activation in the inferior frontal gyrus, while fear activated more the superior temporal gyrus. The approach used here shows the potential of the proposed computational model to reveal the age effect on the brain activation upon emotion arousing images, which could be further transferred to the design of assistive clinical applications.
Active and healthy ageing for Parkinson's disease patients' support: A user's perspective within the i-PROGNOSIS framework

In this paper, the user requirements, along with the methodology adopted towards their identification within the i-PROGNOSIS framework (www.i-prognosis.eu), are presented. The latter are placed within the concept of active and healthy ageing (AHA), focusing on the case of Parkinson's Disease (PD) patients' support. The bases for the user requirements identification were face-to-face sessions, focus groups and a large scale Web-survey. Towards the efficient user requirements identification and i-PROGNOSIS components development, exemplified usage scenarios and related business processes the stakeholders of i-PROGNOSIS can perform, are discussed. Overall, 122 functional and non-functional requirements were identified, serving as a basis for the spiral development model of i-PROGNOSIS, revealing the beneficial role of the users in designing solutions within the AHA concept.
Phase-amplitude cross-frequency coupling in EEG-derived cortical time series upon an auditory perception task

Recent evidence suggests that cross-frequency coupling (CFC) plays an essential role in multi-scale communication across the brain. The amplitude of the high frequency oscillations, responsible for local activity, is modulated by the phase of the lower frequency activity, in a task and region-relevant way. In this paper, we examine this phase-amplitude coupling in a two-tone oddball paradigm for the low frequency bands (delta, theta, alpha, and beta) and determine the most prominent CFCs. Data consisted of cortical time series, extracted by applying three-dimensional vector field tomography (3D-VFT) to high density (256 channels) electroencephalography (HD-EEG), and CFC analysis was based on the phase-amplitude coupling metric, namely PAC. Our findings suggest CFC spanning across all brain regions and low frequencies. Stronger coupling was observed in the delta band, that is closely linked to sensory processing. However, theta coupling was reinforced in the target tone response, revealing a task-dependent CFC and its role in brain networks communication.
WED: An efficient wheezing-episode detector based on breath sounds spectrogram analysis

An enhanced method for the detection of wheezes, based on the spectrogram of the breath sound recordings is proposed. The identification of wheezes in the total breath cycle would contribute to the diagnosis of pathologies related to patients with obstructive airway diseases. Fast and quite simple techniques are applied to automatically locate and identify wheezing-episodes. Amplitude criteria are applied to the peaks of the spectrogram in order to discriminate the wheezing from the breath sound, whereas frequency and time continuity criteria are used to improve the results. The proposed detector could be used for long-term wheezing screening in sleep-laboratories, resulting in significant data-volume reduction.
Sign Language Recognition Using Intrinsic-Mode Sample Entropy on sEMG and Accelerometer Data

Sign language forms a communication channel among the deaf; however, automated gesture recognition could further expand their communication with the hearers. In this work, data from five-channel surface electromyogram and 3-D accelerometer from the signer's dominant hand were analyzed using intrinsic-mode entropy (IMEn) for the automated recognition of Greek sign language (GSL) isolated signs. Discriminant analysis was used to identify the effective scales of the intrinsic-mode functions and the window length for the calculation of the IMEn that contributes to the efficient classification of the GSL signs. Experimental results from the IMEn analysis applied to GSL signs corresponding to 60-word lexicon repeated ten times by three native signers have shown more than 93% mean classification accuracy using IMEn as the only source of the classification feature set. This provides a promising bed-set toward the automated GSL gesture recognition.
Performance of three QRS detection algorithms during sleep: a comparative study

A comparison of the performance of three QRS detectors used in the analysis of electrocardiogram (ECG) during sleep is presented in this paper. Two widely used QRS detection algorithms based on digital filtering (DF) are compared with a newly introduced one, based on higher-order statistics (HOS). The percentage of QRS complexes failed detection along with the number of false positives and false negatives are measured for quantitative performance evaluation. Experimental results, when applying the proposed methods to nocturnal ECG recordings from the Sleep Laboratory of the Philipps University of Marburg, Germany, prove that the HOS-based QRS detector exhibits higher overall QRS detection accuracy (99.95%) than the two DF-based ones (99.75% and 99.59%, respectively). Moreover, it has lower noise susceptibility despite the presence of different noise types, such as smooth or abrupt baseline drift, 50 Hz powerline interference, electromyographic intervention or any arrhythmia effect due to sleep apnea.
Gender effect in human brain responses to bottom-up and top-down attention using the EEG 3D-Vector Field Tomography

The effect of gender in rapidly allocating attention to objects, features or locations, as reflected in brain activity, is examined in this study. A visual-attention task, consisting of bottom-up (visual pop-out) and top-down (visual search) conditions during stimuli of four triangles, i.e., a target and three distractors, was engaged. In pop-out condition, both color and orientation of the distractors differed from target, while in search condition they differed only in orientation. During the task, high-density EEG (256 channels) data were recorded and analyzed by means of behavioral, event-related potentials, i.e., the P300 component and brain source localization analysis using 3D-Vector Field Tomography (3D-VFT). Twenty subjects (half female; 32±4.7 years old) participated in the experiments, performing 60 trials for each condition. Behavioral analysis revealed that both female and male outperformed in the pop-out condition compared to the search one, with respect to accuracy and reaction time, whereas no gender-related statistical significant differences were found. Nevertheless, in the search condition, higher P300 amplitudes were detected for females compared to males (p <; 7 · 10-3). Moreover, the findings suggested that the maximum activation in females was located mainly in the left inferior frontal and superior temporal gyri, whereas in males it was found in the right inferior frontal and superior temporal gyri. Overall, the experimental results show that visual attention depends on contributions from different brain lateralization linked to gender, posing important implications in studying developmental disorders, characterized by gender differences.
Obstacle detection and avoidance module for the blind

Assistive technology enables people to achieve independence when performing daily tasks and it enhances their overall quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of sufficient information about their surrounding environment. With recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities in terms of their mobility. In this context we present and describe a wearable system (Blavigator project), whose global objective is to assist visually impaired people in their navigation on indoor and outdoor environments. This paper is focused mainly on the Computer Vision module of the Blavigator prototype. We propose an object collision detection algorithm based on stereo vision. The proposed algorithm uses Peano-Hilbert Ensemble Empirical Mode Decomposition (PH-EEMD) for disparity image processing and a two layer disparity image segmentation to detect nearby objects. Using the adaptive ensemble empirical mode decomposition (EEMD) image analysis real time is not achieved, with PH-EEMD results on a fast implementation suitable for real time applications.
Electronic auscultation based on wavelet transformation in clinical use

Electronic auscultation is an important, non-invasive method for the detection of lung diseases. For computerized lung sound analysis the wavelet transformation (WT) is a good tool to detect and analyze discontinuous and continuous pathological lung sounds. We developed a lung sound analysis system using a WT based combined method of filtering and spectral analysis to extract parameters from lung sound recordings that could detect typical pneumonia lung sounds (crackles, bronchial breathing). The lung sounds of 32 patients with pneumonia were recorded simultaneously with two air-coupled microphones at standardized airflow. We computed parameters to detect crackles and bronchial breathing sound patterns. Our parameters are able to detect typical pneumonia lung sounds at an early stage of the disease in all 32 patients. Our system found automatically a bronchial breathing sound pattern in 16 patients and crackles in 20 patients as confirmed by normal auscultation findings. As the procedure of electronic auscultation is neither invasive nor particularly expensive, it would be suitable as an examination method for critical care patients who have a high risk of getting hospital acquired pneumonia.
Towards an overall 3-D vector field reconstruction via discretization and a linear equations system

A tomographic method that efficiently reconstructs three-dimensional fields, despite the ill-posedness of recovering a vector field from line integrals, is presented in this paper. The analysis takes into consideration the methodology set forth in  for 2-D reconstruction and demonstrates that with the analogous discretization of the 3-D space and scanning lines, data redundancy is achieved and the solution is obtained from a linear equations system solution, using only information from finite boundary measurements. The adequacy of the method is illustrated by means of simulations on electrostatic fields. The motivation behind this work lies in its potential to bring forward an alternative brain mapping model from EEG recordings.
Adaptive extraction of emotion-related EEG segments using multidimensional directed information in time-frequency domain

Emotion discrimination from electroencephalogram (EEG) has gained attention the last decade as a user-friendly and effective approach to EEG-based emotion recognition (EEG-ER) systems. Nevertheless, challenging issues regarding the emotion elicitation procedure, especially its effectiveness, raise. In this work, a novel method, which not only evaluates the degree of emotion elicitation but localizes the emotion information in the time-frequency domain, as well, is proposed. The latter, incorporates multidimensional directed information at the time-frequency EEG representation, extracted using empirical mode decomposition, and introduces an asymmetry index for adaptive emotion-related EEG segment selection. Experimental results derived from 16 subjects visually stimulated with pictures from the valence/arousal space drawn from the International Affective Picture System database, justify the effectiveness of the proposed approach and its potential contribution to the enhancement of EEG-ER systems.
Nonlinear analysis of musical lung sounds using the bicoherence index

A nonlinear analysis of musical lung sounds based on the bicoherence index is presented in this paper. The detection of quadratic phase coupling between any frequency components of musical lung sounds, by means of third-order statistics and spectra, provides a measure of their nonlinear interaction. This harmonic analysis was conducted on pre-classified signals (wheezes, ronchi, striders) selected from international teaching tapes. The derived results have shown a high degree of deviation from Gaussianity along with quadratic self-coupling within the distinct frequencies of the corresponding power spectrum of musical lung sounds. Differences in the degree of phase coupling, the nonlinearity and non Gaussianity of the processed signals, establish a new field in characterization and feature extraction of the pulmonary pathology associated with musical lung sounds.