Discovery of the Fastest Early Optical Emission from Overluminous SN Ia 2020hvf: A Thermonuclear Explosion within a Dense Circumstellar Environment

In this Letter we report a discovery of a prominent flash of a peculiar overluminous Type Ia supernova, SN 2020hvf, in about 5 hr of the supernova explosion by the first wide-field mosaic CMOS sensor imager, the Tomo-e Gozen Camera. The fast evolution of the early flash was captured by intensive intranight observations via the Tomo-e Gozen high-cadence survey. Numerical simulations show that such a prominent and fast early emission is most likely generated from an interaction between 0.01 M ⊙ circumstellar material (CSM) extending to a distance of ∼1013 cm and supernova ejecta soon after the explosion, indicating a confined dense CSM formation at the final evolution stage of the progenitor of SN 2020hvf. Based on the CSM–ejecta interaction-induced early flash, the overluminous light curve, and the high ejecta velocity of SN 2020hvf, we suggest that the SN 2020hvf may originate from a thermonuclear explosion of a super-Chandrasekhar-mass white dwarf (“super-M Ch WD”). Systematical investigations on explosion mechanisms and hydrodynamic simulations of the super-M Ch WD explosion are required to further test the suggested scenario and understand the progenitor of this peculiar supernova.

First Demonstration of Calibrated Color Imaging by the CAOS Camera

The Coded Access Optical Sensor (CAOS) camera is a novel, single unit, full spectrum (UV to short-wave IR bands), linear, high dynamic range (HDR) camera. In this paper, calibrated color target imaging using the CAOS camera and a comparison to a commercial HDR CMOS camera is demonstrated for the first time. The first experiment using a calibrated color check chart indicates that although the CMOS sensor-based camera has an 87 dB manufacturer-specified HDR range, unrestricted usage of this CMOS camera’s output range greatly fails quality color recovery. On the other hand, the intrinsically linear full dynamic range operation CAOS camera color image recovery generally matches the restricted linear-mode commercial CMOS sensor-based camera recovery for the presented 39.5 dB non-HDR target that also matches the near 40 dB linear camera response function (CRF) range of the CMOS camera. Specifically, compared to the color checker chart manufacturer provided XYZ values for the calibrated target, percentage XYZ mean errors of 8.3% and 10.9% are achieved for the restricted linear range CMOS camera and CAOS camera, respectively. An alternate color camera assessment gives CIE ΔE00 mean values of 4.59 and 5.7 for the restricted linear range CMOS camera and CAOS camera, respectively. Unlike the CMOS camera lens optics and its photo-detection electronics, no special linear response optics and photo-detector designs were used for the experimental CAOS camera, nevertheless, a good and equivalent color recovery was achieved. Given the limited HDR linear range capabilities of a CMOS camera and the intrinsically wide linear HDR capability of a CAOS camera, a combined CAOS-CMOS mode of the CAOS smart camera is prudent and can empower HDR color imaging. Applications for such a hybrid camera includes still photography imaging, especially for quantitative imaging of biological samples, valuable artworks and archaeological artefacts that require authentic color data generation for reliable medical decisions as well as forgery preventing verifications.

Development of Inverter Circuits with Dual Control Subchannel Areas of Integral CMOS Sensor Element

The use of an integrated sensor element as an addition of inverter, which converts the resistance of a sensitive element into the level of the output pulse signal, is investigated. Inverter circuits with different control options for sub-channel areas of MOS transistors are modeled in the LTSpice program. Based on the simulation results, dependencies graphs of the output signal amplitude on the resistance of a sensitive element and sensor’s sensitivity are drawn, and the shapes of the output signals are shown.

Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.

Field curvature correction in video endoscope lenses through the parameters of air meniscus

The research presents a method to synthesize lens data of an air meniscus built into the optical layout and acting as a correction element that fixes the field curvature of the entire optical system. The practical application of the proposed method is relevant when creating miniature lenses for medical video endoscopes when a small number of lenses and a flat image field for a CCD/CMOS sensor are important. Analytical dependencies for the lens data of the air meniscus are obtained and significant conditions for the field curvature correction are formed. A numerical example of a front stop lens design is demonstrated and confirms the correctness of the formulated conditions. A comparison of the aberration values of the original lens and an upgraded system is carried out. It is shown that, the values of the field curvature and astigmatism have been compensated as a result of introducing the synthesized parameters of the air meniscus into the optical layout. The correction is achieved while keeping the values of coma, distortion, focal length, and optical system total length at the level of the initial values.

Super-Resolution Electrochemical Impedance Imaging with a 100 × 100 CMOS Sensor Array

This paper presents a 100 × 100 super-resolution integrated sensor array for microscale electrochemical impedance spectroscopy (EIS) imaging. The system is implemented in 180 nm CMOS with 10 μm × 10 μm pixels. Rather than treating each electrode independently, the sensor is designed to measure the mutual capacitance between programmable sets of pixels. Multiple spatially-resolved measurements can then be computationally combined to produce super-resolution impedance images. Experimental measurements of sub-cellular permittivity distributions within single algae cells demonstrate the potential of this new approach.

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.