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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Pulse amplitude and quality01:17

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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
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Qualitative Analysis03:46

Qualitative Analysis

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For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...
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Continuous -time Fourier Transform01:11

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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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Frames: Problem Solving II01:26

Frames: Problem Solving II

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Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.
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Related Experiment Video

Updated: Sep 4, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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From Whole Video to Frames: Weakly-Supervised Domain Adaptive Continuous-Time QoE Evaluation.

Leida Li, Pengfei Chen, Weisi Lin

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 19, 2022
    PubMed
    Summary

    This study introduces a weakly-supervised domain adaptation method for continuous-time video quality-of-experience (QoE) estimation. It efficiently uses limited labeled data and abundant weakly-labeled data to improve QoE prediction accuracy.

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    Area of Science:

    • Computer Science
    • Multimedia Systems
    • Machine Learning

    Background:

    • Streaming video traffic is rapidly increasing, straining delivery infrastructure and causing variable user quality-of-experience (QoE).
    • Continuous-time QoE estimation is crucial for optimizing streaming services but requires extensive, labor-intensive labeling of continuous QoE data.
    • Existing methods face challenges due to the difficulty of obtaining densely annotated continuous-time QoE labels.

    Purpose of the Study:

    • To develop a novel weakly-supervised domain adaptation approach for continuous-time video QoE evaluation.
    • To address the limitations of labor-intensive data annotation in current QoE estimation methods.
    • To enable accurate QoE prediction using a combination of limited continuously labeled data and abundant weakly-labeled data.

    Main Methods:

    • A weakly-supervised domain adaptation framework is proposed, utilizing source domain data with continuous QoE labels and target domain data with retrospective QoE labels.
    • Spatiotemporal segment-level feature representation is learned using a hybrid 2D and 3D convolutional neural network architecture.
    • A multi-task prediction framework integrates continuous-time and retrospective QoE predictions, employing a quality-attentive adaptation mechanism to bridge domain discrepancies.

    Main Results:

    • The proposed method effectively learns spatiotemporal features for video QoE assessment.
    • The quality-attentive adaptation approach successfully mitigates domain discrepancies between source and target datasets.
    • Experiments demonstrate significant improvements in cross-domain QoE prediction performance compared to existing methods.

    Conclusions:

    • The developed weakly-supervised domain adaptation approach offers an efficient solution for continuous-time video QoE estimation.
    • This method overcomes the bottleneck of dense QoE label annotation, making QoE monitoring more practical.
    • The approach shows strong potential for enhancing QoE prediction accuracy in real-world streaming scenarios.