3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements.

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera's hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat's ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency's Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat's suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications.

Hybrid Refractive-Diffractive Lens with Reduced Chromatic and Geometric Aberrations and Learned Image Reconstruction.

In this paper, we present a hybrid refractive-diffractive lens that, when paired with a deep neural network-based image reconstruction, produces high-quality, real-world images with minimal artifacts, reaching a PSNR of 28 dB on the test set. Our diffractive element compensates for the off-axis aberrations of a single refractive element and has reduced chromatic aberrations across the visible light spectrum. We also describe our training set augmentation and novel quality criteria called "false edge level" (FEL), which validates that the neural network produces visually appealing images without artifacts under a wide range of ISO and exposure settings. Our quality criteria (FEL) enabled us to include real scene images without a corresponding ground truth in the training process.

Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data.

This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, dramatically reducing the time needed to spend with the actual hardware to build a high-quality fault detection model. Our fault detection model was validated on a test bench and showed accuracy of more than 99% of correctly recognized hydraulic system states with a 10-s sampling window. This model can be also leveraged to examine the decision boundaries of the classifier in the two-dimensional embedding space.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis.

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

Real-time and Recursive Estimators for Functional MRI Quality Assessment.

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

Human mind has microwave electromagnetic nature and can be recorded and processed.

INTRODUCTION: In 2014 and 2015 Professor of neurology Andrey Bryukhovetskiy published a novel theory of the information-commutation organization of the human brain in Russia, China and the USA. The theory posits the hypothesis that the higher nervous activity (cognitive, intellectual, mnestic) of the humans and their mind are material and have microwave electromagnetic nature. The theory perceives the human mind as a result of dynamic extracortical information-commutation relations of the super-positions of the electromagnetic waves of ultra high frequency emitted by different areas of the human brain in the inter-membrane cerebrospinal fluid space of the human head at a certain period of time. The inter-membrane cerebrospinal fluid space of the human head (the space between the dura, arachnoid and pia mater filled with the cerebrospinal fluid) of about 10mm size, has all morphological attributes to realize the holography. It is a universal natural bioprocessor for processing, analysis and synthesis of the input data and their record or reproduction on the pia as on the biological holographic membrane. The theory suggested that the processes of the mind can be recorded and digitalized with the last generation contemporary microwave receptors of the UHF band. GOAL: The goal is to experimentally test the theory of the information-commutation organization of the human brain, particularly, the postulate that the human mind has material, and, namely, electromagnetic nature represented by the microwave bioelectric activity; it must be detected, recorded and statistically processed, i.e. its existence must be confirmed. METHODS: On their own initiative, the team of mathematicians, radioengineers and neurologists performed the non-invasive research of the electromagnetic radiation of human brain in the broad frequency range varying from 850MHz to 26.5GHz with the last generation specialized measuring equipment with high sensitivity and recording speed, specialized measuring antennas and low noise amplifying equipment in the anechoic chamber of the 1st class of protection according to the Russian system of certification GOST R 50414-92. RESULTS: The previously unknown microwave electromagnetic radiation of the EHF/UHF range (from 1.5GHz to 4.5GHz) with signal strength of -130dBm .. -100dBm (1e-15 .. 1e-13 W) are discovered. The detected electromagnetic waves have zonal variations in the different areas of the human head and are absent in other areas of the human body. The method of recording of the microwave electromagnetic activity of the human brain is patented in the Russian Federation. The microwave electromagnetic activity of the brain is billion-fold different from the bioelectric activity recorded by the encephalography. CONCLUSION: Discovery of the phenomenon of the microwave radiation of the human brain provides evidence to the idea that thinking and mind are material. This phenomenon has the potential to become a new informational channel of the diagnostics of the functional and pathological state of the higher nervous activity of the human brain. It can provide the basis for the development of the equipment for real-time analysis of the microwave bioelectric activity of the brain in norm and pathology, for objective early diagnostics of the functional and emotional conditions as well as of the psychiatric disorders at the preclinical stage, for the biocontrol of the human brain and the artificial simulators of the human brain. It also can provide the foundation for new systems of the artificial intellect, brain-computer interface and systems of the closed-loop biomanagement of the damaged brain.

Real-time fMRI data for testing OpenNFT functionality.

Here, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants (i.e., one neurofeedback run per participant), their structural scans and pre-selected ROIs/masks/weights. The data allows for simulating the neurofeedback experiment without an MR scanner, exploring the software functionality, and measuring data processing times on the local hardware. In accordance with the descriptions in our main article, we provide data of (1) periodically displayed (intermittent) activation-based feedback; (2) intermittent effective connectivity feedback, based on dynamic causal modeling (DCM) estimations; and (3) continuous classification-based feedback based on support-vector-machine (SVM) estimations. The data is available on our public GitHub repository: https://github.com/OpenNFT/OpenNFT_Demo/releases.