Satellite Detection and Characterization Using Alternate Transformer Architectures for Spectro–Spatial RF Signal Analysis

A number of mathematical methods have been developed to make temporal signal analyses based on time series. However, no effective method for spatial signal analysis, which are as important as temporal signal analyses for geographical systems, has been devised. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring spatial complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to developing an approach for analyzing spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform (FFT) and ordinary least squares (OLS) regression methods are employed to calculate spectral exponents. The results show that the wave-spectrum density distribution of all these urban traffic networks follows scaling law, and that the spectral scaling exponents can be converted into fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world. This study has implications for the further development of fractal-based spatiotemporal signal analysis in the future.

Spatial analysis of gene localization using fluorescent in-situ hybridization (FISH) labeling is potentially a new method for early cancer detection. Current methodology relies heavily upon accurate segmentation of cell nuclei and FISH signals in tissue sections. While automatic FISH signal detection is a relatively simpler task, accurate nuclei segmentation is still a manual process which is fairly time consuming and subjective. Hence to use the methodology as a clinical application, it is necessary to automate all the steps involved in the process of spatial FISH signal analysis using fast, robust and accurate image processing techniques. In this work, we describe an intelligent framework for analyzing the FISH signals by coupling hybrid nuclei segmentation algorithm with pattern recognition algorithms to automatically identify well segmented nuclei. Automatic spatial statistical analysis of the FISH spots was carried out on the output from the image processing and pattern recognition unit. Results are encouraging and show that the method could evolve into a full fledged clinical application for cancer detection.

In recent years, Lamb wave and piezoelectric transducers (PZTs) array based wavenumber filtering technique for damage estimation has been gradually studied. Compared with the time domain and frequency domain analysis of the Lamb wave signals, the wavenumber domain analysis is an effective approach to distinguish wave propagating direction and wave modes. However, the spatial resolution sampled by the PZTs is lower than that sampled by scanning laser Doppler vibrometer. As for the diameter of the PZT, it cannot be very small. In this paper, a new Lamb wave spatial sampling signal optimization method based on Morlet wavelet is proposed. Firstly, the frequency band parameter of the Morlet mother wavelet function is calculated by the Lamb wave excitation signal. Then, the sum of squared errors between the Lamb wave spatial sampling signal and the Morlet wavelet function fitting waveform at each scale factor and time factor is calculated. Finally, the scale factor and time factor corresponding to the least sum of squared errors can be judged to be the best match scale factor and time factor respectively, and the Morlet wavelet function fitting waveform in that scale factor and time factor can be seen as the optimized Lamb wave spatial sampling signal. The validation experiment performed on a glass fiber epoxy composite plate shows that the proposed method can improve the spatial resolution and length of the Lamb wave spatial sampling signal, and the sum of squared errors of this method is no more than 0.2.

An analysis of noise immunity of the searchless digital method of correlation-interferometric direction finding with reconstruction of the spatial analytical signal has been carried out. An analytical estimate of the direction finding error variance consisting of the noise and interference components was obtained. It was shown that the main controllable factors affecting the noise component of the direction finding error variance are as follows: the number of direction-finding channels, the amount of separation between the selected elements of the antenna array, the type of the weight function in spatial spectral analysis and the time of emission analysis. The interference component of the direction finding error variance, unlike the noise component, does not depend on the analysis time but is determined, first of all, by the quality of frequency-spatial selection.In simulation, a family of dependencies of the root mean square deviation of the bearing estimate on the signal-to-noise ratio and the type of the weight function of the spectral analysis window was obtained. Possibility of direction finding with a value of the root mean square deviation of the bearing estimate of 0.03 degrees at an input signal-to-noise ratio of 0 dB has been shown. The estimates of the direction finding error variance obtained analytically and by software simulation practically coincided which confirms the analysis correctness. As a result of simulation, a family of dependences of root-mean square deviation of the bearing estimation on the separation of direction to the signal and interference sources at different signal frequencies was also obtained.It was determined that when the 64-element linear array is used, the resolution of the direction finder depends on the signal frequency. It varies between 6–15 degrees in the range of the direction finder operating frequencies at a signal/interference ratio of 0 dB. The resolution of the direction finder which was found to be high compared to the annular antenna array is an important advantage in conditions of a complex electromagnetic situation.

The subject of research is the process of forming signals in magnetic tracking systems including those used for spatial position calculation within the concepts of Industry 4.0 and Industrial Internet of Things. Such systems are based on calculating the spatial position of objects upon measurements of reference magnetic fields in low-frequency electromagnetic radiation spectrum. The goal is to develop and verify a signal model for spatial position calculating in magnetic tracking systems. The signal model is developed upon experimentally obtained dependencies of the informative signals on the distances and angles between sensor and actuator coils. Objectives: analysis of signals in magnetic tracking systems, development of tools for experimental study, mathematical interpretation of the research results along with development of the signal model, verification and use of the developed model. General scientific methods were used, including experiment, measurement, analysis, synthesis, probabilistic and statistical methods. We have obtained the following results: The structure of a signal chain of the programmable magnetic tracking systems and its implementation on the basis of PSoC of 5LP Family by Cypress Semiconductor has been disclosed. Experimental results obtained at different distances and angles between the actuator and sensor coils have been presented. For spatial positions calculation signal models that describe distribution of magnetic fields and signals of sensor coils are used. We have analyzed typical inaccuracies and ways of their minimization. For verification of the introduced signal model we propose to use the mean square deviation of normalized signals. Conclusions. A signal model for the mutual position of actuators and sensors in magnetic tracking systems has been developed. The model describes functional dependencies whose main parameters are the distances and angles between coils. Further development of the presented results implies the proposed signal model to be used when solving problems of developing and specifying algorithms of spatial position calculation, system debugging and rapid analysis, optimization of calibration procedures.

Spatially explicit ecological research has increased substantially in the past 20 years. Most spatial approaches require the definition of a spatial neighbourhood or the region over which spatial relationships are modelled or assessed. Spatial neighbourhood definitions impact analysis results, and there are benefits in considering neighbourhood definitions that better capture ecological processes. The goal of this research is to present a simple and flexible approach in constraining ecological spatial neighbourhoods using terrain data. Using watershed boundaries, we can restrict spatial neighbourhoods from combining populations or processes that should be separated by terrain effects. We demonstrate the need for ecological constraints by way of a simulation study and highlight our approach with a case study examining mountain pine beetle (Dendroctonus ponderosae, Coleoptera; Hopkins) infestation hot spots. Our results demonstrate how failure to constrain neighbourhoods can lead to errors when the spatial signals from unrelated populations are mixed. Also, unconstrained spatial neighbourhoods can unintentionally detect spatial relationships across many scales. There will be benefits to studies that develop new, ecology-based approaches in defining spatial neighbourhoods that better illuminate ecological function of phenomena under study.

To describe the elastic wave scattering, which reflects the performance of propagation control materials, the approximate directional cloaks of an elastic wave are designed using the zero- and first-order approximation coordinate transformation method. Because the convergence features of fractional Fourier transform (FRFT) are more acute and sensitive to the frequency change than those of short time Fourier transform, the spatial signals in the designed materials are transformed in the FRFT domain. The spatial frequency changes of elastic waves through inhomogeneous materials are quantitatively analyzed under several circumstances. The provided time-frequency analysis method with FRFT can support the design evaluation of the material parameters.

Brain tumor tissue contains different pathological areas, such as tumor cell rich parts, necrotic tissues, and cyst. Furthermore, both neovascularization and edema formation progress along with the tumor progression. In this study we employed diffusion weighted (DW) and magnetization transfer contrast (MTC) imaging to chronologically investigate the biological characteristics of a rat glioma. RG-2 glioma cells were implanted stereotactically into the right hemisphere of male Wistar rats. MR images were taken 1, 2 and 3 weeks after inoculation. Apparent diffusion coefficient (ADC) and MTC values were calculated as follows; ADC = -ln (SI-DW/SI-T2)/1096, MTC = 1-SI-MTon/SI-MToff. Each mapping image was made based on the calculated average values of four pixels. The spatial signal changes and the real values were compared to the histological findings. The apparent increase of ADC was noted in the parenchyma adjacent to tumor suggesting the progression of edema. The tumor itself had similar or slightly increased ADC. Cystic and necrotic components appeared 2 weeks after implantation and they showed significantly higher ADC than those calculated in the contralateral putamen. On the other hand, MTC was slightly decreased in the parenchyma adjacent to the tumor, markedly within the tumor, and maximally in the cystic and necrotic area suggesting accumulation of macromolecules such as growth factors, cytokines, and serum albumin.

In this paper, we demonstrate how the new technology of polarimetric synthetic aperture radar (SAR) interferometry can be used to enhance the detection of targets hidden beneath foliage. The key idea is to note that for random volume scattering, the interferometric coherence is invariant to changes in wave polarization. On the other hand, in the presence of a target the coherence changes with polarization. We show that under general symmetry constraints this change is linear in the complex coherence plane. These observations can be used to devise a filter to suppress the returns from foliage clutter while maintaining the signal from hidden targets. We illustrate the algorithm by applying it to coherent L-band SAR simulations of corner reflectors hidden in a forest. The simulations are performed using a voxel-based vector wave propagation and scattering code coupled to detailed structural models of tree architecture. In this way, the spatial statistics and radar signal fluctuations closely match those observed for natural terrain. We demonstrate significant improvements in the detection of hidden targets, which suggests that this technology has great potential for future foliage penetration (FOPEN) applications.

With the low Earth orbit environment becoming increasingly populated with artificial satellites, rockets, and debris, it is important to understand the effects they have on radio astronomy. In this work, we undertake a multi-frequency, multi-epoch survey with two SKA-Low station prototypes located at the SKA-Low site, to identify and characterise radio frequency emission from orbiting objects and consider their impact on radio astronomy observations. We identified 152 unique satellites across multiple passes in low and medium Earth orbits from 1.6 million full-sky images across 13 selected ${\approx}1$ MHz frequency bands in the SKA-Low frequency range, acquired over almost 20 days of data collection. Our algorithms significantly reduce the rate of satellite misidentification, compared to previous work, validated through simulations to be $ \lt 1\%$ . Notably, multiple satellites were detected transmitting unintended electromagnetic radiation, as well as several decommissioned satellites likely transmitting when the Sun illuminates their solar panels. We test alternative methods of processing data, which will be deployed for a larger, more systematic survey at SKA-Low frequencies in the near future. The current work establishes a baseline for monitoring satellite transmissions, which will be repeated in future years to assess their evolving impact on radio astronomy observations.

This is a pilot project undertaken by the UG and PG students of the Department of Physics, St. Xavier's College (Autonomous), Kolkata under the supervision of Dr. Suparna Roychowd hury and Dr. Shibaji Banerjee and with the assistance of Mr. Bappaditya Manna, Technical Officerof Father Eugene Lafont Observatory (FELO) on building a low-cost high-frequency radiotelescope using Arduino. This paper presents the design of the smallArduino-based radio telescope recently developed by the group to observe radio emission at high frequencies (12-18GHz)and the initialobservations including detection of geostationary andnon-geostationary satellites. Preliminary results demonstrate the effectiveness of this Arduino-based radio telescope in the eraof large radio telescopes that are inaccessible to students. As the radio telescope operates in ahigh-frequency range, it is easier to observe in city areas where Radio Frequency Interference(RFI) is a significant issue. The system can be pointed either manually or using a computerized mount to any radio source and data can be collected from a satellite finder as well asfrom Arduino micro-controller. This system can be used for a basic understanding of radioastronomy and for training purposes for University and College students at a very lowcost. Further, we would also like to extend this to a two-element radio interferometer andundertake interferometric observations of astronomical radio sources in future.

Compared with its counterparts, polarization sensitive optical time-domain reflectometry (P-OTDR) has two typical problems in terms of multi-point disturbance detection and high precision positioning, which restrict its practical applications. In this paper, we propose a novel method by using two-dimensional (2-D) image processing and statistical clustering. In place of the traditional spatial or temporal signal analysis way, the idea employs the temporally and the spatially uneven/inconsistent evolving patterns induced by the multiple individual events, and the proposed method is carried out to accumulate the temporally differentiated OTDR traces and then build up a 2-D temporal-spatial evolving graph, by using edge detection and automatic clustering to distinguish different disturbing points with precise locations. Through series of multi-point disturbance tests, the proposed method has been proved to have better performance than the conventional direct differentiation method and the fast Fourier transform spectrum analysis.

Gait analysis using kinetic data such as pressure distribution underneath the foot has been a topic of interest for assessing falls in elderly and certain pathology such as Parkinson's disease. The disease which affects the central nervous system cannot be ultimately diagnosed by a test. In this letter, we describe a detection algorithm able to classify subjects into Parkinson or normal subjects based on load distribution during gait. This will allow those with the disease to benefit from early detection and thus early treatment. We perform spatial and time signal analyses over vertical ground reaction forces to categorize gaits as balanced or unbalanced, where unbalanced gaits correspond to subjects with Parkinson's disease and balanced gaits could be relevant to both normal and diseased subjects. Then simple features like correlation are used to further differentiate between balanced-normal subjects and balanced-diseased subjects. A 95% overall classification accuracy has been achieved using a linear decision boundary. This letter can be employed to form the basis of designing a portable device for early Parkinson's disease detection on a real-time basis. Moreover, it can be used for evaluation purposes of a rehabilitation program.

The accurate and real-time localization of sound sources is the basis of machine intelligent technology and industrial and civilian positioning applications. It can provide basic help for many applications. In this paper, first, we investigate the applicability of the generalized cross correlation (GCC) delay estimation algorithm. Secondly, we analyze and study the method of acoustic source localization for the microphone array and design a unique spatial structure. Finally, the embedded system and the human-computer interaction interface are designed to collect and analyze the data so as to obtain the accurate and real-time location of the sound source.

Recent attention has been given to the deployment of an adaptable sensor array realized by multi-robotic systems (or swarms). Our group has been studying the collective, autonomous behavior of these such systems and their applications in the area of remote-sensing and emerging threats. To accomplish such tasks, an interdisciplinary research effort at Sandia National Laboratories are conducting tests in the fields of sensor technology, robotics, and multi- agents architectures. Our goal is to coordinate a constellation of point sensors using unmanned robotic vehicles (e.g., RATLERs, Robotic All-Terrain Lunar Exploration Rover- class vehicles) that optimizes spatial coverage and multivariate signal analysis. An overall design methodology evolves complex collective behaviors realized through local interaction (kinetic) physics and artificial intelligence. Learning objectives incorporate real-time operational responses to environmental changes. This paper focuses on our recent work understanding the dynamics of many-body systems according to the physics-based hydrodynamic model of lattice gas automata. Three design features are investigated. One, for single-speed robots, a hexagonal nearest-neighbor interaction topology is necessary to preserve standard hydrodynamic flow. Two, adaptability, defined by the swarm's rate of deformation, can be controlled through the hydrodynamic viscosity term, which, in turn, is defined by the local robotic interaction rules. Three, due to the inherent nonlinearity of the dynamical equations describing large ensembles, stability criteria ensuring convergence to equilibrium states is developed by scaling information flow rates relative to a swarm's hydrodynamic flow rate. An initial test case simulates a swarm of twenty-five robots maneuvering past an obstacle while following a moving target. A genetic algorithm optimizes applied nearest-neighbor forces in each of five spatial regions distributed over the simulation domain. Armed with this knowledge, the swarm adapts by changing state in order to avoid the obstacle. Simulation results are qualitatively similar to a lattice gas.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.