Intraoperative optical imaging (IOI) is a method to visualize functional activated brain areas during brain surgery using a camera system connected to a standard operating microscope. Three different high-resolution camera systems (Hamamatsu EB-CCD C7190-13W, Hamamatsu C4742-96-12G04, and Zeiss AxioCam MRm) have been evaluated for suitability to detect activated brain areas by detecting stimulation-dependent blood volume changes in the somatosensory cerebral cortex after median nerve stimulation. The image quality of the camera systems was evaluated in 14 patients with tumors around the somatosensory cortex. The intraoperative images of the brain surface were continuously recorded over 9 min. With all three camera systems, the activity maps of the median nerve area could be visualized. The image quality of a highly sensitive electron-bombarded camera was up to 10-fold lower compared with two less sensitive standard cameras. In each IOI-positive case, the activated area was in accordance with the anatomical and neurophysiological location of the corresponding cortex. The technique was found to be very sensitive, and several negative influencing factors were identified. However, all possible artifacts seem to be controllable in the majority of the cases, and the IOI method could be well adapted for routine clinical use. Nevertheless, further systematic studies are needed to demonstrate the reliability and validity of the method.
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Sci Rep
January 2025
Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
With the global population surpassing 8 billion, waste production has skyrocketed, leading to increased pollution that adversely affects both terrestrial and marine ecosystems. Public littering, a significant contributor to this pollution, poses severe threats to marine life due to plastic debris, which can inflict substantial ecological harm. Additionally, this pollution jeopardizes human health through contaminated food and water sources.
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January 2025
Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States.
Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder.
View Article and Find Full Text PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFJ Am Mosq Control Assoc
January 2025
Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, 30602.
Accurate enumeration of mosquito eggs is crucial for various entomologic studies, including investigations into mosquito fecundity, life history traits, and vector control strategies. Traditional manual counting methods are labor intensive and prone to human error, highlighting the need for automated systems. This study presents a stand-alone automated mosquito egg counting system using a Raspberry Pi computer, high-quality camera, light-emitting diode ring light source, and a Python script leveraging the Open Source Computer Vision library.
View Article and Find Full Text PDFSci Rep
January 2025
Computer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed.
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