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Sensitive, Real-Time and In-Vivo Oxygen Monitoring for Photodynamic Therapy by Multifunctional Mesoporous Nanosensors
摘要： Real-time monitoring of oxygen consumption is beneficial to predict treatment response and optimize therapeutic protocols for photodynamic therapy (PDT). In this work, we first demonstrate that deformable hollow mesoporous organosilica nanoparticles (HMONs) can be used to load [(Ru(dpp)3)]Cl2 for detecting oxygen (denoted as HMON-[(Ru(dpp)3)]Cl2). This nanoprobe shows significantly improved biocompatibility and high cellular uptake. In-vitro eperiments demonstrate that the HMON-[(Ru(dpp)3)]Cl2 can sensitively detect oxygen changes between 1 %-20 %. On this basis, photosensitizer chlorin e6 (Ce6) and [(Ru(dpp)3)]Cl2 are simultaneously loaded in the HOMNs (denoted as HMON-Ce6-[(Ru(dpp)3)]Cl2) for real-time oxygen monitoring during photodynamic therapy. The HMON-Ce6-[(Ru(dpp)3)]Cl2 can reflects oxygen consumption in solution and cells in photodynamic therapy. Furthermore, the ability of HMON-Ce6-[(Ru(dpp)3)]Cl2 nanosensor to monitor oxygen changes is demonstrated in tumor-bearing nude mice.
关键词： oxygen detection,mesoporous organosilica,photodynamic therapy,in-vivo,real-time
更新于2025-09-04 15:30:14
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Real-time car tracking system based on surveillance videos
摘要： As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.
关键词： Computer vision,Automobile tracking system,Real-time,Index structure,Database
更新于2025-09-04 15:30:14
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Multi-Core DSP Based Parallel Architecture for FMCW SAR Real-Time Imaging
摘要： This paper presents an e?cient parallel processing architecture using multi-core Digital Signal Processor (DSP) to improve the capability of real-time imaging for Frequency Modulated Continuous Wave Synthetic Aperture Radar (FMCW SAR). With the application of the proposed processing architecture, the imaging algorithm is modularized, and each module is e?ciently realized by the proposed processing architecture. In each module, the data processing of di?erent cores is executed in parallel, also the data transmission and data processing of each core are synchronously carried out, so that the processing time for SAR imaging is reduced signi?cantly. Speci?cally, the time of corner turning operation, which is very time-consuming, is ignored under computationally intensive case. The proposed parallel architecture is applied to a compact Ku-band FMCW SAR prototype to achieve real-time imageries with 34 cm × 51 cm (range × azimuth) resolution.
关键词： real-time imaging,FMCW SAR,Parallel processing,multi-core DSP
更新于2025-09-04 15:30:14
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Experimentally Defined Convolutional Neural Network Architecture Variants for Non-Temporal Real-Time Fire Detection
摘要： In this work we investigate the automatic detection of fire pixel regions in video (or still) imagery within real-time bounds without reliance on temporal scene information. As an extension to prior work in the field, we consider the performance of experimentally defined, reduced complexity deep convolutional neural network architectures for this task. Contrary to contemporary trends in the field, our work illustrates maximal accuracy of 0.93 for whole image binary fire detection, with 0.89 accuracy within our superpixel localization framework can be achieved, via a network architecture of significantly reduced complexity. These reduced architectures additionally offer a 3-4 fold increase in computational performance offering up to 17 fps processing on contemporary hardware independent of temporal information. We show the relative performance achieved against prior work using benchmark datasets to illustrate maximally robust real-time fire region detection.
关键词： fire detection,non-stationary visual fire detection,simplified CNN,real-time,non-temporal
更新于2025-09-04 15:30:14
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[ACM Press the 9th ACM Multimedia Systems Conference - Amsterdam, Netherlands (2018.06.12-2018.06.15)] Proceedings of the 9th ACM Multimedia Systems Conference on - MMSys '18 - Skeleton-based continuous extrinsic calibration of multiple RGB-D kinect cameras
摘要： Applications involving 3D scanning and reconstruction & 3D Tele-immersion provide an immersive experience by capturing a scene using multiple RGB-D cameras, such as Kinect. Prior knowledge of intrinsic calibration of each of the cameras, and extrinsic calibration between cameras, is essential to reconstruct the captured data. The intrinsic calibration for a given camera rarely ever changes, so only needs to be estimated once. However, the extrinsic calibration between cameras can change, even with a small nudge to the camera. Calibration accuracy depends on sensor noise, features used, sampling method, etc., resulting in the need for iterative calibration to achieve good calibration. In this paper, we introduce a skeleton based approach to calibrate multiple RGB-D Kinect cameras in a closed setup, automatically without any intervention, within a few seconds. The method uses only the person present in the scene to calibrate, removing the need for manually inserting, detecting and extracting other objects like plane, checker-board, sphere, etc. 3D joints of the extracted skeleton are used as correspondence points between cameras, after undergoing accuracy and orientation checks. Temporal, spatial, and motion constraints are applied during the point selection strategy. Our calibration error checking is inexpensive in terms of computational cost and time and hence is continuously run in the background. Automatic re-calibration of the cameras can be performed when the calibration error goes beyond a threshold due to any possible camera movement. Evaluations show that the method can provide fast, accurate and continuous calibration, as long as a human is moving around in the captured scene.
关键词： 3D Skeleton,Interactive 3D Tele-Immersion,Point-Cloud,3D Calibration,Real-time
更新于2025-09-04 15:30:14
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Towards the use of diffuse reflectance spectroscopy for real-time in vivo detection of breast cancer during surgery
摘要： Background: Breast cancer surgeons struggle with differentiating healthy tissue from cancer at the resection margin during surgery. We report on the feasibility of using diffuse reflectance spectroscopy (DRS) for real?time in vivo tissue characterization. Methods: Evaluating feasibility of the technology requires a setting in which measurements, imaging and pathology have the best possible correlation. For this purpose an optical biopsy needle was used that had integrated optical fibers at the tip of the needle. This approach enabled the best possible correlation between optical measurement volume and tissue histology. With this optical biopsy needle we acquired real?time DRS data of normal tissue and tumor tissue in 27 patients that underwent an ultrasound guided breast biopsy procedure. Five additional patients were measured in continuous mode in which we obtained DRS measurements along the entire biopsy needle trajectory. We developed and compared three different support vector machine based classification models to classify the DRS measurements. Results: With DRS malignant tissue could be discriminated from healthy tissue. The classification model that was based on eight selected wavelengths had the highest accuracy and Matthews Correlation Coefficient (MCC) of 0.93 and 0.87, respectively. In three patients that were measured in continuous mode and had malignant tissue in their biopsy specimen, a clear transition was seen in the classified DRS measurements going from healthy tissue to tumor tissue. This transition was not seen in the other two continuously measured patients that had benign tissue in their biopsy specimen. Conclusions: It was concluded that DRS is feasible for integration in a surgical tool that could assist the breast surgeon in detecting positive resection margins during breast surgery.
关键词： Intraoperative margin assessment,Breast cancer surgery,Optical technology,Real?time
更新于2025-09-04 15:30:14