Motion detection system for AXAF x-ray ground testing

Detection performance of security system using radio waves based on space-time signal processing

Micro-pinhole SPECT system with conventional multiple-head gamma cameras has the advantage of high magnification factor for imaging of small animals such as rodents. However, several geometric factors should be calibrated to obtain the SPECT image with good image quality. We developed a simplified geometric calibration method for rotating triple- head pinhole SPECT system and assessed the effects of the calibration using several phantom and rodent imaging studies. Trionix Triad XLT9 triple-head SPECT scanner with 1.0 mm pinhole apertures were used for the experiments. Approximately centered one point source was scanned to track the angle- dependent positioning errors. The centroid of point source was determined by the center of mass calculation. Axially departed two point sources were scanned to calibrate radius of rotation from pinhole to center of rotation. To verify the improvements by the geometric calibrations, we compared the spatial resolution of the reconstructed image of Tc-99m point source with and without the calibration. SPECT image of micro performance phantom with hot rod inserts was acquired and several animal imaging studies were performed. Exact sphere shape of the point source was obtained by applying the calibration and axial resolution was improved. Lesion detectability and image quality was also much improved by the calibration in the phantom and animal studies. Serious degradation of micro-pinhole SPECT images due to the geometric errors could be corrected using a simplified calibration method using only one or two point sources.

For the last few years, development and optimization of three-dimensional (3D) x-ray breast imaging systems, such as digital breast tomosynthesis (DBT) and computed tomography, have drawn much attention from the medical imaging community, either academia or industry. However, there is still much room for understanding how to best optimize and evaluate the devices over a large space of many different system parameters and geometries. Current evaluation methods, which work well for 2D systems, do not incorporate the depth information from the 3D imaging systems. Therefore, it is critical to develop a statistically sound evaluation method to investigate the usefulness of inclusion of depth and background-variability information into the assessment and optimization of the 3D systems. In this paper, we present a mathematical framework for a statistical assessment of planar and 3D x-ray breast imaging systems. Our method is based on statistical decision theory, in particular, making use of the ideal linear observer called the Hotelling observer. We also present a physical phantom that consists of spheres of different sizes and materials for producing an ensemble of randomly varying backgrounds to be imaged for a given patient class. Lastly, we demonstrate our evaluation method in comparing laboratory mammography and three-angle DBT systems for signal detection tasks using the phantom's projection data. We compare the variable phantom case to that of a phantom of the same dimensions filled with water, which we call the uniform phantom, based on the performance of the Hotelling observer as a function of signal size and intensity. Detectability trends calculated using the variable and uniform phantom methods are different from each other for both mammography and DBT systems. Our results indicate that measuring the system's detection performance with consideration of background variability may lead to differences in system performance estimates and comparisons. For the assessment of 3D systems, to accurately determine trade offs between image quality and radiation dose, it is critical to incorporate randomness arising from the imaging chain including background variability into system performance calculations.

We address the problem of resolving and localizing blurred point sources in intensity images. Telescopic star-field images blurred by atmospheric turbulence or optical aberrations are typical examples of this class of images, a new approach to image restoration is introduced, which is a generalization of 2-D sensor array processing techniques originating from the field of direction of arrival estimation (DOA). It is shown that in the frequency domain, blurred point source images can be modeled with a structure analogous to the response of linear sensor arrays to coherent signal sources. Thus, the problem may be cast into the form of DOA estimation, and eigenvector based subspace decomposition algorithms, such as MUSIC, may be adapted to search for these point sources. For deterministic point images the signal subspace is degenerate, with rank one, so rank enhancement techniques are required before MUSIC or related algorithms may be used. The presence of blur prohibits the use of existing rank enhancement methods. A generalized array smoothing method is introduced for rank enhancement in the presence of blur, and to regularize the ill posed nature of the image restoration. The new algorithm achieves inter-pixel super-resolution and is computationally efficient. Examples of star image deblurring using the algorithm are presented.

The main capacitor bank of the FRX-L (field-reversed configuration experiment) supplies current (1.6 Meg amp) to a single-turn coil that is used to create high-density plasmas for magnetized-target fusion (MTF) experiments. The energy stored in the main bank (200 kJ) is transferred to the load coil via an array of four Maxwell rail-gap switches in parallel. Each switch is capable of transferring approximately a quarter of the main bank charge flow without damage. Therefore, if a single switch pre-fires it is subject to much greater stress than it is able to tolerate. In this paper, we discuss the design and performance of a system that is able to discriminate between a switch pre-fire, trigger noise, and normal operation and initiate a trigger in time to safely prevent damage. This system consists of innovative electric field detectors, fiber-optic isolation of signals and high-speed solid-state circuitry that interfaces with the Maxwell trigger system without modification.

We propose and experimentally demonstrate the Transformable Reflective Telescope (TRT) kit for educational purposes and for performing various optical tests with a single kit. The TRT kit is a portable optical bench setup suitable for interferometry, spectroscopy, measuring stray light, and developing adaptive optics, among other uses. Supplementary modules may be integrated easily due to the modular design of the TRT kit. The kit consists of five units: a primary mirror module, secondary mirror module, mounting base module, baffle module, and alignment module. Precise alignment and focusing are achieved using a precision optical rail on the alignment module. The TRT kit transforms into three telescope configurations: Newtonian, Cassegrain, and Gregorian. Students change telescope configurations by exchanging the secondary mirror. The portable design and the aluminum primary mirror of the TRT kit enable students to perform experiments in various environments. The minimized baffle design utilizes commercial telescope tubes, allowing users to look directly into the optical system while suppressing stray light down to ∼ 10 − 8 point source transmittance. The TRT kit was tested using a point source and field images. Point source measurement of the Newtonian telescope configuration resulted in an 80% encircled energy diameter of 23.8 µm.

The difference between the displacement of the centre of rotation (mechanical shift, MS) and the electronic centring misalignment (electronic shift, ES) in cone beam SPECT is evaluated. A method is proposed to determine both MS and ES using the centroid of a projected point source sampled over 360 degrees and the Marquardt non-linear fitting algorithm. Both shifts are characterized by two orthogonal components. This method is verified using Monte Carlo simulated point source data with different combinations of mechanical and electronic shifts. Both shifts can be determined correctly. The authors have also applied the proposed method to their cone beam SPECT system to determine both shifts as well as the focal length. The determined ES parameters are then used to correct the projections and the MS parameters are incorporated into a reconstruction algorithm. The point source images are reconstructed and the image resolutions with and without the shift corrections are measured. The experimental results demonstrate that the image resolution is improved after shift corrections.

Motion detection in video sequences is a problem that different techniques and algorithms have faced. Several computer vision applications require an efficient detection stage. A reliable motion detection system should work under a wide range of a variety of scenes and various acquisition conditions. In this paper, a new approach to detect moving objects boundaries in an image sequence is presented. The proposed approach is based on the frame differing of modified Haynes technique combined with a biologically inspired spiking neural network for boundaries detection. First, the detection of temporal changes consists in extracting the moving objects between two consecutive frames in a dynamic scene without any estimate of the optical flow, nor any priory on the stage as the background image. The main goal is to get a fast and effective detection. In order to obtain a motion boundaries information, the extracted moving objects are then presented to a spiking neural network based on the Hodgkin-Huxley neuron model whose responses can form a complete closed curve coinciding with the motion object boundaries. Simulation results in some experiments are satisfactory. The performance of the motion boundary detection system is compared against competing boundary detection method using the YouTube Motion Boundaries dataset (YMB) dataset. The comparison results obtained confirm the robustness of the proposed approach. The extracted boundaries objects results can be used in further applications.

The Hubble Space Telescope (HST)/Space Telescope Imaging Spectrograph (STIS) contains the only currently operating coronagraph in space that is not trained on the Sun. In an era of extreme-adaptive-optics-fed coronagraphs, and with the possibility of future space-based coronagraphs, we re-evaluate the contrast performance of the STIS CCD camera. The 50CORON aperture consists of a series of occulting wedges and bars, including the recently commissioned BAR5 occulter. We discuss the latest procedures in obtaining high-contrast imaging of circumstellar disks and faint point sources with STIS. For the first time, we develop a noise model for the coronagraph, including systematic noise due to speckles, which can be used to predict the performance of future coronagraphic observations. Further, we present results from a recent calibration program that demonstrates better than 10 − 6 point-source contrast at 0.6″, ranging to 3 × 10 − 5 point-source contrast at 0.25″. These results are obtained by a combination of subpixel grid dithers, multiple spacecraft orientations, and postprocessing techniques. Some of these same techniques will be employed by future space-based coronagraphic missions. We discuss the unique aspects of STIS coronagraphy relative to ground-based adaptive-optics-fed coronagraphs.

The Point Spread Function (PSF) or star test is one of the main criteria to be considered in the quality of the image formed by a telescope. In a real system the distribution of irradiance in the image of a point source is given by the PSF, a function which is highly sensitive to aberrations. The PSF of a telescope may be determined by measuring the intensity distribution in the image of a star. Alternatively, if we already know the aberrations present in the optical system, then we may use diffraction theory to calculate the function. In this paper we propose a method for determining the wavefront aberrations from the PSF, using Genetic Algorithms to perform an optimization process starting from the PSF instead of the more traditional method of adjusting an aberration polynomial. We show that this method of phase recuperation is immune to noise-induced errors arising during image aquisition and registration. Some practical results are shown.

A large fraction of celestial objects exhibit point shapes in CCD images, such as stars and QSOs, which contain less information due to their few pixels. Point source classification based solely on image data may lead to low accuracy. To address this challenge, this paper proposes a Multi-modal Transfer Learning-based classification method for celestial objects with point shape images. Considering that spectral data possess rich features and that there is a correlation between spectral data and image data, the proposed approach fully utilizes the knowledge gained from celestial spectral data and transfers it to the original image-based classification, enhancing the accuracy of classifying stars and QSOs. Initially, a one-dimensional residual network is employed to extract a 128-dimensional spectral feature vector from the original 3700-dimensional spectral data. This spectral feature vector captures important features of the celestial object. The Generative Adversarial Network is then utilized to generate a simulated spectral vector of 128 dimensions, which corresponds to the celestial object image. By generating simulated spectral vectors, data from two modals (spectral and image) for the same celestial object are available, enriching the input features of the model. In the upcoming multimodal classification model, we only require the images of celestial objects along with their corresponding simulated spectral data, and we no longer need real spectral data. With the assistance of spectral data, the proposed method alleviates the above disadvantages of the original image-based classification method. Remarkably, our method has improved the F1-score from 0.93 to 0.9777, while reducing the error rate in classification by 40%. These enhancements significantly increase the classification accuracy of stars and QSOs, providing strong support for the classification of celestial point sources.

Methane (CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) is one of the main greenhouse gases, whose retrieval is easily affected by atmospheric water (H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O) and surface albedo. In this paper, based on a radiative transfer model, the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) radiance with different H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O and surface albedo are simulated as training data. Back Propagation (BP) feed-forward neural network algorithm in machine learning is used to train the CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> retrieval model, which is applied to quantify the atmospheric CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> concentration. This method can effectively decrease the impact of atmospheric H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O and surface albedo on CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> retrieval. Moreover, this machine learning-based approach separates the processes of model training and prediction. This enables rapid characterization of CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> emission point sources in images as the Matched Filter (MF) method, while also obtaining the column-averaged concentration of CH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> , similar to the Optimal Estimation (OE) method. The research results indicate that the Mean Absolute Percentage Error (MAPE) of the optimal BP model is as low as 0.33%. If necessary, further increases in training data can improve the resolution and applicability of the model.

Novel outline features for pedestrian detection system with thermal images

Optimization of energy window limits for photopeak detection systems

Background Fungal infection detection is essential for optimizing therapeutic strategies, preventing complications, enhancing overall health, and alleviating the financial burden, and preventing transmission. According to the CDC, fungal illness resulted in 75,000 hospital admissions and 9 million outpatient visits annually, with 7,199 deaths predicted in the United States in 2021. The rising prevalence of fungal infections, influenced by factors such as population expansion and evolving treatment strategies poses a growing challenge to achieving an early and accurate diagnosis. There is the crucial need for improved detection methods to promptly identify various fungal infections, including Candida species, spanning from skin issues to potentially fatal systemic diseases, ultimately reducing associated morbidity and mortality. Severe and profound infections with life-threatening potential are common in individuals with compromised immune systems and those who are hospitalized. This risk is notably elevated among patients with organ transplants, immunosuppressive treatments, diabetes, recent broad-spectrum antibiotic usage, catheter utilization, and extended hospitalization periods. Our advancement seeks to introduce innovative methods leveraging carbon-based nanomaterials for the detection of fungal infections, offering distinct advantages including cost-effectiveness, ease of operation, and time efficiency compared to conventional techniques. Methods Our technology utilizes carbon-based nano materials, a promising advancement in nanotechnology to detect fungal infections, especially Candida manifestations. The samples are brought into contact with pre-probed carbon-based nano materials sensors maintained at a specific annealing temperature, facilitating nucleic acid hybridization. Subsequently, alterations in the carbon nanotube's electrical signal are observed, indicating a change in conductivity. This change can allow for the measurement of a voltage difference across a continuous flow of electricity, correlating with the concentration of targets present in the sample, thereby enabling the detection of specific microorganisms. No change was noticed in electrical properties of carbon-based nano material when negative experiments were conducted. Results The initial findings showed the presence of specific microorganisms within the sample in less than 15 minutes with carbon-based nano materials sensors. Comparative analysis of the electrical signal magnitude values obtained from carbon nanotube measurements against the corresponding CT values from quantitative PCR (qPCR) provides insights into the correlation between the electrical readouts and molecular quantification, offering a comprehensive evaluation of the nanotube-based detection system's performance in diagnostics procedure. Conclusions Unlike conventional blood culture and qPCR, our technology, delivers outcomes with heightened sensitivity, cost-effectiveness, and time efficiency. Our team's uncomplicated sample collection method notably reduces the intricacies of sample preparation, enabling the proper disposal of both the sample container and sensor part without requiring disassembly. Enhanced sensitivity, coupled with the economical and rapid detection capabilities of advanced technologies like carbon-based nano materials, is a promising alternative for the accurate and efficient diagnosis of fungal infections. Consequently, the implementation of such technology not only enhances diagnostic efficiency but also contributes to better patient outcomes through prompt and targeted therapeutic interventions.