Exploiting Virtual Array Diversity for Accurate Radar Detection

The HESSI mission proposes to perform high resolution imaging and spectroscopy observations in the soft X-ray, hard X-ray, and gamma-ray regimes, with finer angular resolution (nearly 2 arcseconds) and finer energy resolution (approximately 1 keV) than has been previously possible. This combination of imaging and spectroscopy is achieved with a set of Rotating Modulation Collimators placed in front of an array of cooled germanium and silicon detectors. A set of 12 bi-grid collimators, each of which consists of a pair of identically pitched, widely-separated grids, is used to provide the imaging. Each grid consists of a planar array of equally-spaced, parallel, X-ray opaque slats separated by X-ray transparent slits. If the slits of each grid are parallel to each other and the pitch is identical for the two grids, then the transmission through the grid pair depends on the direction of incidence of the incoming X-rays. For slits and slats of equal width, the transmission varies between zero and 50% depending on whether the shadows of the slats in the top grid fall on the slits or slats of the lower grid. A complete transmission cycle from zero to 50% and back to zero corresponds to a change in source direction that is given by p/L, where L is the separation between the grids. The authors describe a deep etch lithography technique developed to fabricate the grids which have pitches below 100 {micro}m. They use a free standing sheet of PMMA as a base for the process, and use the ALS facility to perform the exposures of the PMMA.

The future space X-ray astronomy imaging missions require very large collecting areas at still fine angular resolution and reasonable weight. The novel space Xray optics substrates such as Silicon wafers and thin thermally formed glass enable wide applications of precise and very light weight (volume densities 2.3 to 2.5 gcm-3) optics. The recent status of novel technologies as well as developed test samples with emphasis on precise optical surfaces based on novel materials and their space applications will be presented and discussed.

Naturally occurring water vapor maser emission at 1.35 cm wavelength provides an accurate probe for the study of accretion disks around highly compact objects, thought to be black holes, in the centers of active galaxies. Because of the exceptionally fine angular resolution, 200 microarcseconds, obtainable with very long baseline interferometry, accompanied by high spectral resolution, <0.1 km s-1, the dynamics and structures of these disks can be probed with exceptional clarity. The data on the galaxy NGC 4258 are discussed here in detail. The mass of the black hole binding the accretion disk is 3·9 × 107 M⊙. Although the accretion disk has a rotational period of about 800 years, the physical motions of the masers have been directly measured with VLBI over a period of a few years. These measurements also allow the distance from the earth to the black hole to be estimated to an accuracy of 4 per cent. The status of the search for other maser/black hole candidates is also discussed.

If a collector is very large - square kilometers in size - it may be easier to fabricate flat optics than curved optics. If the primary objective is a flat diffraction grating, microscopic grooves would reshape the incident wave front rather than the 3D surface of a conventional parabolic reflector. I discuss diffractive optics that potentially can cover areas of square kilometers. The geometry of grazing incidence and evanescence allows for very large grating surfaces in ribbons or aggregated segmented modules. Diffraction grating primaries are attractive in that they are well suited for spectrographic astronomy, particularly the Doppler shift studies that have recently been useful in extra-solar planet detection. I propose a unique static mount for ground-based observations where the only moving part is the earth itself. For space-based installations, a new type of membrane telescope is disclosed that can be orbited as a roll of plastic and unfurled over multiple kilometer lengths. Holographic gratings are capable of very fine angular resolution, and only their extreme chromatic dispersion has inhibited their use. The bug is a feature. When taking spectra, chromatic dispersion is useful.

The future space X-ray astronomy imaging missions require very large collecting areas at still fine angular resolution and reasonable weight. The novel substrates for X-ray mirrors such as Silicon wafers and thin thermally formed glass enable wide applications of precise and very light weight (volume densities 2.3 to 2.5 gcm-3) optics. The recent status of novel technologies as well as developed test samples with emphasis on precise optical surfaces based on novel materials and their space applications is presented and discussed.

Angular resolution study of a combined gamma-neutron coded aperture imager for standoff detection

In a recent publication, an aperture mass statistic for gravitational flexion was derived and shown to be effective, at least with simulated data, in detecting massive structures and substructures within clusters of galaxies. Further, it was suggested that the radius at which the flexion aperture mass signal falls to zero might allow for estimation of the mass or density profile of the structures detected. In this paper, we more fully explore this possibility, considering the behaviour both of the peak signal and the zero-signal contours for two mass models – the singular isothermal sphere and Navarro–Frenk–White profiles – under varying aperture size, filter shape and mass concentration parameter. We demonstrate the effectiveness of the flexion aperture mass statistic in discriminating between mass profiles and concentration parameters, and in providing an accurate estimate of the mass of the lens, to within a factor of 1.5 or better. In addition, we compare the aperture mass method to a direct non-parametric reconstruction of the convergence from flexion measurements. We demonstrate that the aperture mass technique is much better able to constrain the shape of the central density profile, obtains much finer angular resolution in reconstructions, and does not suffer from ambiguity in the normalization of the signal, in contrast to the direct method.

Millimeter-wave (mmWave) radar has emerged as a critical technology for non-contact human sensing in security and healthcare applications. While traditional Doppler radar is limited to moving targets, micro-Doppler signatures enable the detection of stationary humans by capturing physiological signals such as respiration. However, in complex indoor environments, dynamic clutter sources (e.g., oscillating fans and swaying plants) generate periodic micro-Doppler signatures resembling human breathing, leading to frequent false alarms. To address this challenge, this article proposes a robust stationary human detection method based on sparse dictionary reconstruction. A Fourier transform-based dictionary is constructed to facilitate the sparse representation of micro-Doppler features. Leveraging the distinct sparsity characteristics of human respiration compared to environmental interference within this dictionary, the proposed method employs a Compressed Sensing (CS) framework. Specifically, the Orthogonal Matching Pursuit (OMP) algorithm is utilized to reconstruct signal components under strict sparsity constraints and physiological frequency priors. Target classification is then achieved by analyzing the reconstruction error, effectively isolating human signals from interference. Experimental results from real-world scenarios demonstrate that the proposed method achieves an interference suppression rate of 97% and a stationary human detection rate of 94%. Furthermore, in complex scenarios containing both targets and interference, the detection accuracy exceeds 86%. These results confirm that the proposed approach offers superior accuracy and robustness compared to existing methods.

To prevent needlestick injury and leftover instruments, and to perform efficient dental treatment, it is important to know the instruments required during dental treatment. Therefore, we will obtain a dataset for image recognition of dental treatment instruments, develop a system for detecting dental treatment instruments during treatment by image recognition, and evaluate the performance of the system to establish a method for detecting instruments during treatment. We created an image recognition dataset using 23 types of instruments commonly used in the Department of Restorative Dentistry and Endodontology at Osaka University Dental Hospital and a surgeon’s hands as detection targets. Two types of datasets were created: one annotated with only the characteristic parts of the instruments, and the other annotated with the entire parts of instruments. YOLOv4 and YOLOv7 were used as the image recognition system. The performance of the system was evaluated in terms of two metrics: detection accuracy (DA), which indicates the probability of correctly detecting the number of target instruments in an image, and the average precision (AP). When using YOLOv4, the mean DA and AP were 89.3% and 70.9%, respectively, when the characteristic parts of the instruments were annotated and 85.3% and 59.9%, respectively, when the entire parts of the instruments were annotated. When using YOLOv7, the mean DA and AP were 89.7% and 80.8%, respectively, when the characteristic parts of the instruments were annotated and 84.4% and 63.5%, respectively, when the entire parts of the instruments were annotated. The detection of dental instruments can be performed efficiently by targeting the parts characterizing them.

Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different types of sensors, such as camera, radar, ultrasonic, and LiDAR to sense the surrounding environment. Compared with the other types of sensors, millimeter-wave automotive radar has advantages in terms of cost and reliability under bad weather conditions (e.g., snow, rain, fog) and doesn't suffer from light condition variations (e.g., darkness). Typical radar devices used in today's commercial vehicles with ADAS features produce sparse point clouds in low angular resolution with a limited number of antennas. In this paper, we present a machine learning-aided signal processing chain to suppress the radar imaging blur effect introduced by the phase migration in time-division multiplexing multi-input multi-output (MIMO) radar, to generate low-level high-resolution radar bird's eye view (BEV) spectra with rich object's features. Compared with radar point clouds, there is no information loss in radar BEV spectra. We then propose a Temporal-fusion, Distance tolerant single stage object detection Network, termed as, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDRadarNet</i> , and an enhanced version, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDRadarNet+</i> to robustly detect vehicles in both long and short ranges on radar BEVs. We introduce a first-of-its-kind multi-model dataset, containing 14,800 frames of high-resolution low-level radar BEV spectra with synchronized stereo cameras RGB images and three-dimensional (3D) LiDAR point clouds. Our dataset achieves 0.39 m range resolution and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.2^\circ$</tex-math></inline-formula> degree azimuth angular resolution with 100 meters maximum detectable range. Moreover, we create a sub-dataset, the Doppler Unfolding dataset, containing 244,140 beam vectors extracted from the 3D radar data cube. With extensive testing and evaluation, we demonstrate that our Doppler unfolding network achieves 93.46% Doppler unfolding accuracy. Compared to YOLOv7, a state-of-the-art image-based object detection network, TDRadarNet achieves a 70.3% Average Precision (AP) for vehicle detection, demonstrating a 21.0% improvement; TDRadarNet+ achieves a 73.9% AP, showing a 24.6% improvement in performance.

In this study, a temporal shape–based fusion method using a spatially and temporally moving window is proposed to incorporate time lag of fine and coarse resolution observations, and to fully utilize target fine resolution pixel and similar coarse resolution pixels in the process. This method provides high accuracy fused images with Pearson’s r of ~0.95, root mean square error of ~0.04, and bias of ~0.01 for commonly used fine spatial resolution satellites, including Landsat 7 and 8, Sentinel 2, and Gaofen 1, over different heterogeneous regions, such as urban, mountain, forest, and savanna regions. The fused fine resolution Enhanced Vegetation Index (EVI) time series using different fine spatial resolution satellites data as input are all highly correlated with the PhenoCam monitored green chromatic coordinate, with no temporal lag. Compared with commonly used data fusion method, this method provides equivalent and slightly higher accuracy because both neighboring similar pixels and the annual temporal variation are fully considered. This temporal shape–based fusion method does not require each input fine resolution image to be cloud-free; therefore, it can be used at a large spatial scale without further preprocessing and generates continuous datasets over a long-time range with only one input preparation process. The factors that could affect the method accuracy are the cloud detection accuracy of fine resolution data and the temporal continuity of the coarse resolution data. The method may also be used to produce spatially and temporally continuous surface reflectance and other surface reflectance derived indices.

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird's eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

Geometric information constraint 3D object detection from LiDAR point cloud for autonomous vehicles under adverse weather

This paper proposes a high-precision lane detection and correction method based on roadside millimeter-wave radar. After deploying millimeter-wave radars on traffic light poles to collect lane point clouds, spatiotemporal adaptive DBSCAN clustering is employed to extract lane clusters, with adaptive neighborhood and temporal weighting improving clustering accuracy. To compensate for sensor offsets, an ICP algorithm with curvature keypoint selection is introduced for rigid alignment, enhancing correction stability. Lane shapes are fitted using a segmental sliding-window PCA combined with convex hull calculation and B-spline interpolation, achieving high-precision modeling for complex curves and nonlinear lanes. Experimental results demonstrate that the proposed method effectively handles environmental variations and noise, significantly improving the accuracy and robustness of lane detection and correction.

To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer detection on MRI compared to fully supervised models trained with additional expert annotations. We used 1500 MRI scans from the PI-CAI challenge training subset. Positive scans had 220 human and 205 AI-generated annotations. The mtU-Net (proposed teacher-student semi-supervised approach) was compared to supervised (trained using only 220 human annotations) and semi-supervised (trained on human and AI-generated annotations) nnU-Net. The 205 AI-annotated scans were manually annotated, and a fully supervised model was trained. External validation was performed on a newly annotated dataset from the PROMIS study (n = 574,403 lesions) and the Prostate158 dataset (n = 158,126 lesions). Patient-level performance was evaluated using the area under the curve (AUC) and lesion-level detection (overlap > 0.10) using average precision (AP), along with 95% confidence Intervals (in brackets), and the DeLong test to compare AUCs against the supervised and fully supervised models. The fully supervised nnU-Net showed the highest performance on the internal PI-CAI test set (AUC = 0.89 [0.87-0.91], AP = 0.65 [0.60-0.70]) and external validation datasets PROMIS (AUC = 0.68 [0.64-0.72], AP = 0.24 [0.20-0.29]) and Prostate158 (AUC = 0.87 [0.82-0.92], AP = 0.64 [0.56-0.72]), significantly outperforming the supervised baseline (p < 0.05). The proposed semi-supervised mtU-Net demonstrated close external validation performance on PROMIS (AUC = 0.66 [0.62-0.71], AP = 0.20 [0.16-0.25]) and Prostate158 (AUC = 0.86 [0.81-0.92], AP = 0.58 [0.49-0.67]), significantly outperforming the supervised baseline on both datasets (p = 0.047 and p = 0.014, respectively), and showing no significant difference to the fully supervised model (p = 0.199 and p = 0.702, respectively). In prostate MRI tumor detection, fully supervised learning performed best. However, in external validation, the semi-supervised methods demonstrated performance that approached that of the fully supervised model, proving a valuable approach when expert annotations are limited. Question The need for extensive expert voxel-level annotations delays the development of AI-based prostate cancer diagnostic tools and their implementation in clinical practice. Findings The combination of pseudo-labeling with consistency regularization achieved performance comparable to that of fully supervised methods, demonstrating that data diversity matches the impact of expert annotation volume. Clinical relevance Semi-supervised learning reduces dependence on expert annotations while maintaining detection accuracy, enabling the development of scalable, automated diagnostic tools for prostate cancer amid growing clinical workflow demands.