Patient adherence is pivotal in clinical trials for new pharmaceuticals. Ensuring adherence is essential for robust safety and efficacy analyses. Intentional non-adherence, marked by the patient's deceptive action...
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ISBN:
(纸本)9798350344868;9798350344851
Patient adherence is pivotal in clinical trials for new pharmaceuticals. Ensuring adherence is essential for robust safety and efficacy analyses. Intentional non-adherence, marked by the patient's deceptive actions during dosing, complicates the accuracy of measurement. This paper proposes a novel learning-based system combining vision and metadata for detecting potentially deceptive dosing videos. Exploiting neural networks' image understanding, it integrates visual and contextual data through ensemble learning. The system, efficient and adaptable, pioneers real-world deception capture, boosting adherence precision in trials. Our experiments show its remarkable real-world performance (1).
The implementation of China's higher education talent quality enhancement project integrates modern information technology, particularly digital imageprocessing and virtual simulation technology, to promote the c...
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ISBN:
(纸本)9798350375343;9798350375336
The implementation of China's higher education talent quality enhancement project integrates modern information technology, particularly digital imageprocessing and virtual simulation technology, to promote the cultivation of talent in universities under the "Intelligent + Education" model. This paper introduces a virtual simulation experimental system for the "Digital imageprocessing" course based on WebGL and Matlab, which centers around a 3D environment and supports online coding, real-time computation, and virtual display, facilitating task-driven online experimental learning. The system, built with a frontend using Vue, ***, and *** in conjunction with a MySQL database, enables user interaction with the 3D environment, enhancing the real-time and interactive nature of the experiments. It transforms traditional teaching methods and encourages students to engage in active and in-depth learning. The trial run of the system has demonstrated good compatibility, stability, and cross-platform capabilities.
The low-rank approximation of big data matrices and tensors plays a pivotal role in many modern applications. Recently, the randomized subspace iteration has shown to be a powerful tool in approximating large matrices...
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ISBN:
(纸本)9798350349405;9798350349399
The low-rank approximation of big data matrices and tensors plays a pivotal role in many modern applications. Recently, the randomized subspace iteration has shown to be a powerful tool in approximating large matrices. In this paper we present a rank-revealing, two-sided variant of the randomized subspace iteration. Novelty of our work lies in the utilization of the unpivoted QR factorization, rather than the singular value decomposition (SVD), for factorizing the compressed matrix. We provide bounds on the rank-revealingness of our algorithm as well as bounds on the error of the low-rank approximations, in both 2- and Frobenius norm. In addition, we employ the proposed algorithm to efficiently compute the low rank tensor decomposition using the truncated higher-order SVD. We conduct tests on (i) two classes of matrices, and (ii) synthetic data tensor and real dataset to demonstrate the efficacy of the proposed algorithms.
Aiming at technical advantages of quickly discover and real-time tracking focused on targets with UAV video, we propose a multi-object tracking method based on spatial constraints. Utilizing the pre-training model of ...
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Working under fatigue states is not only inefficient but also brings a series of safety concerns and health problems. This article presents a novel fatigue detection algorithm based on facial multifeature fusion, exhi...
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Working under fatigue states is not only inefficient but also brings a series of safety concerns and health problems. This article presents a novel fatigue detection algorithm based on facial multifeature fusion, exhibiting promising properties of immediacy and accuracy. Following the videoprocessing of marking the gray image frames and the histogram equalization using the Dlib toolkit, the facial features are extracted based on the facial marker points and then evaluated to obtain the eye aspect ratio (EAR), mouth aspect ratio (MAR), and head Euler angles (HEAs) in real-time. These evaluation indexes can further contribute to calculating blinking frequency (BF), percentage of eyelid closure (PERCLOS) over the pupil over time, yawning frequency (YF), and nodding frequency (NF), of which the four parameters are normalized to establish the detection model, showing the capability of identifying the fatigue grade with high accuracy and quick response. The actual test verifies the algorithm's reliability, and the results show that the accuracy of detecting the fatigue behaviors reaches more than 94.4%, and the final judgment perfectly matches the actual physiological state based on ensuring the real-time performance of the detection.
High Dynamic Range (HDR) imaging is a digital imageprocessing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables cap...
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ISBN:
(纸本)9798350324471
High Dynamic Range (HDR) imaging is a digital imageprocessing technique used to produce a wider range of brightness and color by using multiple captures of a scene taken with different exposures times. It enables capturing more details and producing a more natural-looking image with less washed-out highlights and deeper, more saturated colors. In medical endoscopy, HDR imaging enhances the visibility and clarity of images captured during endoscopy procedures. It provides enhanced visualization of subtler details in both dark cavities and bright areas, resulting in a uniformly exposed view and improved contrast among various tissue types. Standard HDR imaging methods are often complex and computationally demanding, making them unsuitable for performance-critical applications like endoscopy, where real-time performance is crucial. This paper introduces a more efficient and less complex method for achieving HDR-like image quality in realtime. The method takes a high-pixel-bit-depth frame and generates multiple low-pixel-bit-depth frames and uses them to generate the high quality image. The focus of the paper is to enhance endoscopic image quality using HDR imaging, and the proposed method is demonstrated to be effective in achieving this goal with real-time performance. The method is implemented in the FPGA System-on-a-Chip (SoC) of a bronchoscope video processor system, and its effectiveness is verified through a simulated study using a phantom, which confirms the improved image quality and real-time performance.
videoprocessing is a specific type of signal processing that frequently uses video filters and video files or video streams as both the input and output signals. In real-time applications like Bio-medical application...
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Recently, quality assessment for user-generated content (UGC) videos has become a challenging task due to the absence of reference videos and the presence of complex distortions. Prior methods has highlighted the effe...
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The requirements of multiple Unmanned Aerial Vehicle (UAV)-based video streaming transmission rapidly increase in flying ad-hoc networks (FANET). Due to diverse network features of FANET, tradeoff design in harsh netw...
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This paper proposes a novel edge computing enabled real-timevideo analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of in...
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ISBN:
(纸本)9798350309461
This paper proposes a novel edge computing enabled real-timevideo analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of interesting module (ROGM). TAODM adaptively determines the offloading decision to process each video frame locally with a tracking algorithm or to offload it to the edge server inferred by an object detection model. ROIM determines each offloading frame's resolution and detection model configuration to ensure that the analysis results can return in time. TAODM and ROIM interact jointly to filter the repetitive spatial-temporal semantic information to maximize the processing rate while ensuring high video analysis accuracy. Unlike most existing works, this paper investigates the real-timevideo analysis systems where the intelligent visual device connects to the edge server through a wireless network with fluctuating network conditions. We decompose the real-timevideo analysis problem into the offloading decision and configurations selection sub-problems. To solve these two sub-problems, we introduce a double deep Q network (DDQN) based offloading approach and a contextual multi-armed bandit (CMAB) based adaptive configurations selection approach, respectively. A DDQN-CMAB reinforcement learning (DCRL) training framework is further developed to integrate these two approaches to improve the overall video analyzing performance. Extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.
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