Number Of Pixels Research Articles

High range resolution spectral-scanning LiDAR based on optical frequency-domain reflectometry.

Spectral scanning, which utilizes the dispersive effect of light, is a simple and robust method for solid-state beam steering in light detection and ranging (LiDAR) applications. Powered by a tunable laser source, optical frequency-domain reflectometry (OFDR) is a high-precision measurement scheme that is inherently compatible with spectral scanning. Here, we propose a spectral-scanning LiDAR based on OFDR technology and demonstrate that, by connecting the measured spectral reflectivity and group delay of the targets with the dispersion equation, their cloud point data can be obtained. Moreover, compared to the spectral-scanning LiDAR based on the frequency-modulated continuous-wave (FMCW) ranging method, our proposed LiDAR scheme offers a more than tenfold improvement in range resolution with a large number of angular pixels. This enhancement enables high-resolution 3D imaging along both the angular and range axes.

Charge-coupled device readout by digital-correlated double sampling

Charge-coupled devices (CCDs) play crucial roles in astronomy owing to their widespread use in the optical band, offering high sensibility, low noise, high dynamic range, and high spatial resolution. Recent advancements in CCDs have focused on improving the sub-electron noise levels for particle detection and enhancing readout speeds through simulation studies. The main objective of this study is to replace the analog processing of CCD signals using the digital-correlated double sampling (DCDS) technique. DCDS allows post-acquisition noise correction through intermittent sampling of an internal reference voltage to provide compact and flexible performance. In this study, DCDS was evaluated using two digital processing systems, namely, a 2.5 mega-samples per second (MSPS) 24-bit analog-to-digital converter (ADC) board and a 250 MSPS 16-bit ADC field-programmable-gate-array( FPGA)-based buffer memory board. The readout noise value at 74 kpix/s (7.1 e) was a significant improvement over that obtained with analog processing (9.7 e). The DCDS implementation demonstrates optimal signal-to-noise ratio for a wide range of readout speeds. Parameters such as the sample positions per pixel, number of samples, and number of pixels were identified to be essential in achieving an accurate gain value. Finally, hardware implementation of the DCDS IP core algorithm on a Xilinx Zynq-7000 AP system-on-a-chip (SoC) showed a significant improvement in power dissipation (7.1 W) compared to the analog Monsoon correlated double sample (CDS) circuit (13.6 W). The DCDS IP core implementation also showed a background noise reduction of up to 34% compared to DCDS offline readout processing using a Python algorithm.

Abstract 4123639: Tumor Endothelial Marker 1 is Upregulated in Atrial Cardiac Fibroblasts in Atrial Fibrillation and Correlates With Atrial Fibrosis

Background: Tumor endothelial marker 1 (TEM1 or CD248) is a transmembrane protein found in activated mesenchymal cells during embryo development. Its expression level becomes very low or undetectable in adult tissue. Re-expression of TEM1 in fibroblasts has been reported in hepatic, renal, and pulmonary fibrosis. We evaluated atrial TEM1 expression in atrial fibrillation (AF) and its relation with atrial fibrosis. Methods and Results: All data were presented as mean±standard error. Left atrial (LA) tissues were collected from 30 patients (pts) (mean age 64.0±1.6 yrs, male 76.7%) with AF underwent cardiac surgery (mitral and/or tricuspid valve surgery 96.7%) and maze procedure with LA appendage excision. Immunofluorescence (IF) staining showed TEM1 expressed in atrial cardiac fibroblasts (CFs) in the LA tissue of AF pts, but not in normal LA tissue (US Biomax). Primary human CFs were directly isolated from the LA tissue. IF staining showed TEM1 expression in the isolated CFs from AF pts but not in normal human CFs (ScienCell). Western blot could detect TEM1 expression in the LA tissues of all 30 AF pts. Sirius Red staining was used to visualize collagen and atrial fibrosis was quantified using a pixel-based method (Adobe Photoshop) by calculating the total numbers of red pixels and the percentage (%) of red pixels to total pixels. Atrial fibrosis was increased in AF pts compared with normal control (total number of red pixels: 160740±13066 vs. 50863±6357, % of red pixels to total pixels: 11.4±0.8 vs. 3.8±0.2%, all p<0.001). There was a positive correlation between the expression level of TEM1 (ratio of TEM1/actin band density in western blot) with the severity of atrial fibrosis, including the total numbers of red pixels (r = 0.47, p = 0.01) and the % of red pixels to total pixels (r = 0.48, p = 0.01). TEM1 and galectin expression levels had positive correlation (r = 0.60, p < 0.01). There were no correlations of atrial TEM1 expression with the LA diameter and CHA2DS2-VASc score. In vitro study showed TEM1 knockdown in cultured CFs with siRNA reduced cell proliferation (scrambled- vs TEM1-siRNA, BrdU absorbance: 1.84±0.05 vs 1.50±0.07, p<0.05), increased starvation-induced cell apoptosis (cell death ELISA absorbance: 0.69±0.11 vs 2.88±0.05, p<0.05) and decreased cell migration. Conclusions: There is increased TEM1 expression in atrial CFs in AF and its expression level is associated with atrial fibrosis. TEM1 expression changes the cell behaviors of CFs.

Combining Sentinel-2 Data and Risk Maps to Detect Trees Predisposed to and Attacked by European Spruce Bark Beetle

The European spruce bark beetle is a major disturbance agent in Norway spruce forests in Europe, and with a changing climate it is predicted that damage will increase. To prevent the bark beetle population buildup, and to limit further spread during outbreaks, it is crucial to detect attacked trees early. In this study, we utilize Sentinel-2 data in combination with a risk map, created from geodata and forestry data, to detect trees predisposed to and attacked by the European spruce bark beetle. Random forest models were trained over two tiles (90 × 90 km) in southern Sweden for all dates with a sufficient number of cloud-free Sentinel-2 pixels during the period May–September in 2017 and 2018. The pixels were classified into attacked and healthy to study how detection accuracy changed with time after bark beetle swarming and to find which Sentinel-2 bands are more important for detecting bark beetle attacked trees. Random forest models were trained with (1) single-date data, (2) temporal features (1-year difference), (3) single-date and temporal features combined, and (4) Sentinel-2 data and a risk map combined. We also included a spatial variability metric. The results show that detection accuracy was high already before the trees were attacked in May 2018, indicating that the Sentinel-2 data detect predisposed trees and that the early signs of attack are low for trees at high risk of being attacked. For single-date models, the accuracy ranged from 63 to 79% and 84 to 94% for the two tiles. For temporal features, accuracy ranged from 65 to 81% and 81 to 92%. When the single-date and temporal features were combined, the accuracy ranged from 70 to 84% and 90 to 96% for the two tiles, and with the risk map included, the accuracy ranged from 83 to 91% and 92 to 97%, showing that remote sensing data and geodata can be combined to increase detection accuracy. The differences in accuracy between the two tiles indicate that local differences can influence accuracy, suggesting that geographically weighted methods should be applied. For the single-date models, the SWIR, red-edge, and blue bands were generally more important, and the SWIR bands were more important after the attack, suggesting that they are most suitable for detecting the early signs of a bark beetle attack. For the temporal features, the SWIR and blue bands were more important, and for the variability metric, the green band was generally more important.

A plaintext-related and ciphertext feedback mechanism for medical image encryption based on a new one-dimensional chaotic system

Abstract In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB R2021a, OS Windows 11, 2.60GHz CPU and 16GB RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512×512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051 and a unified average changed intensity (UACI) of 33.4452. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.

CunuSHT: GPU Accelerated Spherical Harmonic Transforms on Arbitrary Pixelizations

Abstract We present cunuSHT https://github.com/Sebastian-Belkner/cunuSHT, a general-purpose Python package that wraps a highly efficient CUDA implementation of the nonuniform spin-0 spherical harmonic transform. The method is applicable to arbitrary pixelization schemes, including schemes constructed from equally-spaced iso-latitude rings as well as completely nonuniform ones. The algorithm has an asymptotic scaling of $\mathcal {O}{(\ell _{\rm max}^3)}$ for maximum multipole ℓmax and can be made to achieve machine precision accuracy, considering band-limited transforms for which $N\approx \ell _{\rm max}^2$ (where N is the number of pixels in the map). While cunuSHT is developed for applications in cosmology in mind, it is applicable to various other interpolation problems on the sphere. We outperform the fastest available CPU algorithm at problem sizes ℓmax ∼ 4 · 102 and larger. The speed-up increases with the problem size and reaches a factor of up to 5 for problems with a nonuniform pixelization and ℓmax > 4 · 103 when comparing a single modern GPU to a modern 32-core CPU. This performance is achieved by utilizing the double Fourier sphere method in combination with the nonuniform fast Fourier transform and by avoiding transfers between the host and device. For scenarios without GPU availability, cunuSHT wraps existing CPU libraries. cunuSHT is publicly available and includes tests, documentation, and demonstrations.

Accounting for intensity variation within pixels of Shack-Hartmann wavefront sensors

The accuracy of centroiding algorithms in Shack-Hartmann wavefront sensing is limited by the implicit approximation of uniform pixel illumination. Iterative centroiding algorithms are further limited by the consideration of full pixels to define the image domain for centroiding. Here, we demonstrate two practical and complementary approaches to mitigate both these sources of error. First, we consider partial or ‘fractional’ pixels to maintain centroiding area symmetry around the center of mass. Secondly, we propose methods to perform piece-wise polynomial interpolation to calculate intensity distribution within pixels, which is then used to estimate the centroid within each pixel area. This approach that accounts for intensity non-uniformity across pixels notably reduces centroid errors up to a factor of 5 across lenslet image widths ranging from 1.33 to 3.10 pixels full-width-half-maximum (FWHM). Consequently, wavefront sensing errors decrease from 14% to 4%, on average, for FWHM = 1.35 pixels, demonstrating a substantial benefit when the number of pixels per lenslet is minimized to enhance the signal-to-noise ratio or increase frame rate.

A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation

Background and Objective:Breast cancer is the most common cancer type among women worldwide and a leading cause of female death. Accurately interpreting these complex tumors, involving small size and morphology, requires a significant amount of expertise and time. Developing a breast tumor segmentation model to assist clinicians in treatment, therefore, holds great practical significance. Methods:We propose a multi-scale, multi-task model framework named MTF-UNet. Firstly, we differ from the common approach of using different convolution kernel sizes to extract multi-scale features, and instead use the same convolution kernel size with different numbers of convolutions to obtain multi-scale, multi-level features. Additionally, to better integrate features from different levels and sizes, we extract a new multi-branch feature fusion block (ADF). This block differs from using channel and spatial attention to fuse features, but considers fusion weights between various branches. Secondly, we propose to use the number of pixels predicted to be related to tumors and background to assist segmentation, which is different from the conventional approach of using classification tasks to assist segmentation. Results:We conducted extensive experiments on our proprietary DCE-MRI dataset, as well as two public datasets (BUSI and Kvasir-SEG). In the aforementioned datasets, our model achieved excellent MIoU scores of 90.4516%, 89.8408%, and 92.8431% on the respective test sets. Furthermore, our ablation study has demonstrated the efficacy of each component and the effective integration of our auxiliary prediction branch into other models. Conclusion:Through comprehensive experiments and comparisons with other algorithms, the effectiveness, adaptability, and robustness of our proposed method have been demonstrated. We believe that MTF-UNet has great potential for further development in the field of medical image segmentation. The relevant code and data can be found at https://github.com/LCUDai/MTF-UNet.git.

Development of a method for evaluating fabric surface contamination by particulate matter using fluorescent particles

Functional clothing products are being developed to prevent the attachment of particulate matter (PM). However, currently, PM-blocking performance of such products cannot be quantitatively evaluated. This study developed a method for measuring fabric surface contamination by PM by contaminating textile fabrics with PM and using an image processing technique. Silicate particles exhibiting fluorescence characteristics with a suitable size distribution were selected as PM simulants. The fabric surface contamination was calculated by contaminating textile fabrics under wind speed and PM concentration conditions similar to actual atmospheric environment. The number of pixels was obtained in the black and white binary images converted from the images of the textile fabrics captured under an ultraviolet (UV) light source. Experiments were performed using finished nylon fabric with PM-blocking performance and raw nylon fabric without PM-blocking performance. Comparison of the fabric surface contamination shows that it is possible to evaluate the PM-blocking performance of fabrics.

Optimizing flotation froth image segmentation via parallel branch network and hybrid loss supervision

Flotation is a crucial technology for fine coal separation, and accurately acquiring bubble size information during the flotation process is essential for monitoring flotation conditions and achieving intelligent control. However, existing semantic segmentation models encountered issues with boundary disconnection when segmenting flotation bubbles, resulting in deviations between the extracted bubble sizes and their true values. To address the aforementioned challenges, a semantic segmentation model was proposed to maintain high-resolution feature maps throughout the network by designing a parallel branch network structure. Additionally, a ConvTranspose module was proposed to preserve the detailed feature information of images while gradually enhancing the resolution of feature maps. In the model training phase, a hybrid loss function combining pixel classification loss with shape similarity loss was proposed to alleviate the sample imbalance problem caused by the substantial difference in the number of pixels between bubble boundaries and the interior of bubbles. Moreover, since traditional semantic segmentation evaluation metrics, such as MIoU, lack a mechanism for measuring bubble boundary continuity and cannot effectively penalize the problem of boundary disconnection, this paper proposed a new evaluation method for assessing the segmentation performance of flotation froth images. To comprehensively evaluate the effectiveness of the proposed method, this paper conducted tests using flotation froth images collected from actual production processes. Compared with existing methods, the segmentation model proposed in this paper exhibited clear superiority in mitigating the problem of bubble boundary disconnection. The prediction error for the number of bubbles was 6.38 %, which is significantly better than other methods.

FIELDimagePy: A tool to estimate zonal statistics from an image, bounded by one or multiple polygons

AbstractVegetation indices have become an indispensable tool in remote sensing‐based agricultural research. A recent area of advancement in agricultural remote sensing research is in high‐throughput phenotyping, often conducted on a plot by plot basis. FIELDimageR is a tool used extensively in high‐throughput phenotyping that estimates zonal statistics of vegetation indices per plot. However, being written in R language, FIELDimageR requires high computing time. As a high‐resolution image over a large area means a large number of pixels, FIELDimageR is incapable of using high‐resolution orthomosaicked images without reducing image resolution by aggregating digital numbers of several pixels and treating them as one pixel. This research tool implements FIELDimageR in the Python language as FIELDimagePy. FIELDimagePy follows similar workflows as FIELDimageR and generates equivalent results for zonal statistics of vegetation indices per plot. FIELDimagePy is significantly and substantially faster than FIELDimageR. Computing time by FIELDimagePy are three to four times lower than computing times by FIELDimageR, even when using raw images with 16 times denser pixels. Moreover, FIELDimagePy is useful beyond plot by plot research in agriculture and capable of estimating zonal statistics of any raster bounded by any polygons. With slight modifications, FIELDimagePy can be useful for other disciplines of science, such as geophysics, geography, economics, medical sciences, among others. FIELDimagePy can be accessed from the GitHub repository: https://github.com/SumantraChatterjee/FIELDimagePy.

Image-Based Auto-Focus Microscope System with Visual Servo Control for Micro-Stereolithography.

Micro-stereolithography (μSL) is an advanced additive manufacturing technique that enables the fabrication of highly precise microstructures with fine feature resolution. One of the primary challenges in μSL is achieving and maintaining precise focus throughout the fabrication process. For the successful application of μSL, it is essential to maintain the sample surface within a focal depth of several microns. Despite the growing interest in auto-focus devices, limited attention has been directed towards auto-focus systems in image-based auto-focus microscope systems for precision μSL. To address this challenge, we propose an image-based auto-focus microscope system incorporating visual servo control. In the optical design, a transflective beam splitter is employed, allowing the laser beam to pass through for fabrication while reflecting the focused beam on the sample surface to the microscope and camera. Utilizing captured spot images and the Foucault knife-edge test, a deep learning-based laser spot image processing algorithm is developed to determine the focus position based on spot size and the number of spot pixels on both sides. Experimental results demonstrate that the proposed auto-focus system effectively determines the relative position of the focal point using the laser spot image and achieves auto-focusing through visual servo control.

Binary amplitude holograms for shaping complex light fields with digital micromirror devices

Abstract Digital micromirror devices are a popular type of spatial light modulators for wavefront shaping applications. While they offer several advantages when compared to liquid crystal modulators, such as polarization insensitivity and rapid-switching, they only provide a binary amplitude modulation. Despite this restriction, it is possible to use binary holograms to modulate both the amplitude and phase of the incoming light, thus allowing the creation of complex light fields. Here, a didactic exploration of various types of binary holograms is presented. A particular emphasis is placed on the fact that the finite number of pixels coupled with the binary modulation limits the number of complex values that can be encoded into the holograms. This entails an inevitable trade-off between the number of complex values that can be modulated with the hologram and the number of independent degrees of freedom available to shape light, both of which impact the quality of the shaped field. Nonetheless, it is shown that by appropriately choosing the type of hologram and its parameters, it is possible to find a suitable compromise that allows shaping a wide range of complex fields with high accuracy. In particular, it is shown that choosing the appropriate alignment between the hologram and the micromirror array allows for maximizing the number of complex values. Likewise, the implications of the type of hologram and its parameters on the diffraction efficiency are also considered.

Effects of Diesel Pre-Injection Time on Combustion Process and Emission Characteristics of Diesel/Methanol Dual Fuel Engine

ABSTRACT The traditional diesel engine not only causes the reduction of nonrenewable energy but also aggravates environmental pollution. The utilization of renewable fuels in engine can not only effectively reduce the dependence on oil sources but also effectively reduce emissions. As a renewable oxygen-containing fuel with low production cost and widely sourced, methanol holds vast potential for application in engine. The dual-fuel combustion of diesel and methanol has emerged as an important research issue in the internal combustion engine industry. This paper conducted a study on the combustion and emission characteristics of the diesel/methanol dual fuel based on the optical engine test platform and 3D numerical simulation software. The results show that the change in cylinder pressure is relatively small and the heat release rate increases with the advance of diesel pre-injection time. The pre-injection time of diesel mainly affects the mixture distribution of pre-injection diesel, and the mixture is more evenly mixed with the advance of the pre-injection time. The degree of premixed combustion becomes higher, premixed combustion will lead to more rapid combustion, so the peak heat release rate shows an increasing trend. The pre-injection time of diesel is advanced, resulting in a leaner mixture in the early stage, and the fuel is not easy to burn, so CA10 and CA50 are delayed. The soot formation decreases as the mixture becomes more uniform, and the number of pixels of KL factor in each range also decreases. The average temperature in the cylinder is reduced and the NOX shows a decreasing trend with the advance of the pre-injection time. The change in CO generation is relatively small with the advance of pre-injection time.

Diagnostic Performance for Detection of Glaucomatous Structural Damage Using Pixelwise Analysis of Retinal Thickness Measurements.

To compare the diagnostic accuracy of thickness measurements of individual and combined macular retinal layers to discriminate 188 glaucomatous and 148 glaucoma suspect eyes from 362 healthy control (HC) eyes on a pixel-by-pixel basis. For this retrospective study, we manually corrected the segmentations of posterior pole optical coherence tomography (OCT) scans to determine the thickness of the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), the ganglion cell complex (GCC), and the total neural retina (TR). For each eye, the total number of pixels with thickness values less than the fifth percentile of the HC distribution was used to create a receiver operating characteristic (ROC) curve for each layer and for layer combinations. Using total abnormal pixel count criteria to discriminate glaucoma from HC eyes, the individual layers with the highest area under the ROC curve (AUC) were the NFL and GCL; IPL performance was significantly lower (P < 0.05). GCC had a significant higher AUC (94.3%) than individual the AUC of the NFL (92.3%) (P = 0.0231) but not higher than AUC of the GCL (93.4%) (P = 0.3487). The highest AUC (95.4%) and sensitivity (85.1%) at 95% specificity was found for the Boolean combination of NFL or GCL. The highest AUC is not significantly higher (P = 0.0882) than the AUC of the GCC but the highest sensitivity is significantly higher than the sensitivity of the GCC. This pattern was similar for discriminating between suspect and HC eyes (P = 0.0356). Using pixel-based methods, the diagnostic accuracy of NFL and GCL exceeded that of IPL and TR. GCC had equivalent performance as NFL and GCL. The specific spatial locations within the posterior pole that exhibit best performance vary depending on which layer is being assessed. Recognizing this dependency highlights the importance of considering multiple layers independently, as they offer complementary information for effective and comprehensive diagnosis.

Chlorophyll-a and suspended matter variability in a data-scarce coastal-estuarine ecosystem

Analyzing the variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) in estuarine and coastal environments is crucial for understanding ecosystem health, guiding environmental management decisions, and evaluating climate change impacts. Satellite remote sensing offers a powerful tool for this analysis due to its extensive spatial and temporal coverage. Although several algorithms exist for complex coastal and estuarine waters, long-term datasets such as GlobColor's Ocean Color (OC5) and neural network (NN) algorithms are frequently used for robust variability analysis. This study uses the GlobColor NN algorithm to investigate the seasonal and inter-annual variability of Chl-a and TSM in a data-scare region, namely the Meghna estuary in Bangladesh and its adjacent coastal fringe. The other algorithm (i.e. OC5), while offers the longest time series, cannot be used in this region due to the high number of invalid pixels. Therefore, this study examines different environmental factors (i.e. sea surface temperature (SST), photosynthetically active radiation (PAR), rainfall, zonal (ZWC) and meridional (MWC) wind components, and ocean currents) and climatic indices (i.e., El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) to understand their influence on the seasonal and inter-annual variability of Chl-a and TSM derived from the GlobColor NN algorithm. Empirical orthogonal function analysis identifies the seasonal signal as dominant in the study region. The seasonal cycle of Chl-a is influenced by factors including MWC, TSM, SST, and rainfall. In contrast, TSM seasonal variations are primarily driven by rainfall and MWC. Post-monsoon Chl-a inter-annual fluctuations are mainly linked to TSM inter-annual variability, with secondary influences from monsoon rainfall and the winter ENSO index. Inter-annual changes in TSM are primarily associated with the winter ENSO index and monsoon rainfall. This research elucidates the primary mechanisms influencing Chl-a and TSM variability in the Meghna estuary and its adjacent coast, thus advancing the understanding of the dynamics in the study region. The information obtained through this study is valuable for scientists, policymakers, and stakeholders involved in the sustainable management of the Meghna estuary and its coastal resources, particularly in the context of climate change.

Right-left sEMG burst synchronization of the lumbar erector spinae muscles of seated violin players

Burst-like activation of postural muscles has been previously described and plays a crucial role in elucidating the strategies for postural control adopted by the central nervous system (CNS). A spatio-temporal descriptor of surface electromyographic (sEMG) bursts (STB) is proposed and applied to statistically quantify the burst-like activity of the right and left (R-L) lumbar erector spinae muscle of nine seated violinists playing for two hours. The STB signal is the number of pixels of the high density sEMG (HDsEMG) maps simultaneously showing sEMG amplitude above a given threshold. Burst activity was present in all nine subjects. Four of them met four stringent criteria allowing analysis of frequency, duration, and synchronization between the R-L bursts after 0, 15, 30, 60, 120 min of playing. Mean square coherence between STBs of the two muscles was > 0.75 within ⁓1 Hz bandwidth between 2.2 Hz and 4.5 Hz depending on subject. Non-parametric statistics was applied to compare, in time and space, the R-L features of the bursts. The mean STB width was significantly associated primarily to side and secondarily to time and ranged from 100 to 250 ms. The right STB signals led the left (p < 0.02) by 0 - 160 ms.The inverted pendulum composed by the upper body of a seated violinist is controlled in an intermittent way. The erector spinae of the selected subjects were active, on average, for less than 50% of the time. These findings demonstrate a CNS strategy of intermittent back muscle activation presumably aimed to reducing fatigue during hours of playing in seated violinists.

Estimating the parameters of trypillia proto-cities using plans and magnetic surveys with the help of neural networks.

The Tryplian culture was considered the largest of all other archaeological cultures that existed on the territory of modern Ukraine. The method for analyzing magnetic images has been described, which allows archaeologists to assess the scale of settlements without excavating them. It is noted that one of the tasks during the analysis of the settlement is to find out its characteristics: counting the number of buildings, calculating the area, etc. However, in the majority of cases, counting the number of structures is currently unfeasible, as Trypillian proto-cities are situated within the cultural layer relatively close to the surface, and any economic activities disturb this cultural layer. The capabilities of the existing system (application) are described, which solved the problem by the method of average values and had differences from the commonly accepted method. It was concluded that a more automated version of this system could be an option where the number of sites in the image will be calculated by the average number of pixels per site, that is, the number of black and gray pixels in the image, divided by the average number of pixels in the site. It was decided to use neural network models. As an example, the largest of the famous Trypil proto-cities in Ukraine - Talyanka with an area of 450 hectares - is considered. Pictures taken between 1971 and 1974 were used because they are in the public domain. A list of actions for image preprocessing is described. A decision was made to train the model to input square images of 15 by 15 pixels, for this the entire image was divided into 748 square images, the number of buildings in each of them was determined manually. A four-point work algorithm is formulated, and it is also presented in the form of a UML class diagram and an activity diagram. The algorithm for creating a training sample for the second neural network from four points is also formulated (for the case when the zone of squares with lost information will run along the entire length of the picture, as it happens in the photo of Talyanok), presented in the form of a UML activity diagram. The neural network will accept 10 values as input, and will output one - the number of sites in the square, information about which is lost. Tensorflow and keras frameworks were used to create all models. The most successful model has almost 2.5 million parameters, the model requires 9.36 MB of RAM. During the tests, it was found that increasing the number of convolution layers does not increase the result. For testing the first model, a picture of the settlement of Maidanetske was submitted, for training a picture of the settlement of Talyanka was used. For the training set, the recognition accuracy was 92%, for testing - 86.5%. The neural network, which implements the algorithm for predicting the number of buildings on lost plots, provides an accuracy of 58% for the Talyanka settlement and 42% for the Maidanetske settlement. This accuracy is much better than a random guess, the probability of which is just over 6%.

Large-field optical sectioning structured illumination microscopy

Nowadays, fringe generation and fast-switching in structured illumination microscopy (SIM) is often performed using spatial light modulators (SLMs) and digital micromirror devices (DMDs). Yet, the field of view (FOV) or the spatial bandwidth product (SBP) is constrained by the limited pixel number of the SLMs or DMDs. In this study, we present a large-field optical-sectioning structured illumination microscopy (LF-OS-SIM) scheme, which features a large FOV and fast phase shifting. LF-OS-SIM utilizes a one-dimensional grating to generate stripe patterns and a spatial light modulator (SLM) to perform phase-shifting in the Fourier domain. Therefore, this technique can improve the spatial bandwidth product (SBP) by a factor of 2.6 compared to the conventional OS-SIM under the same experimental parameters. The superiority of LF-OS-SIM is demonstrated by imaging euro coins and fluorescent samples, and the results imply that this method has the potential to be applied for 3D imaging of industrial micro-devices and biosamples.

Development of an Algorithm for Semantic Segmentation of Earth Remote Sensing Data to Determine Phytoplankton Populations

Introduction. Computer vision is widely used for semantic segmentation of Earth remote sensing (ERS) data. The method allows monitoring ecosystems, including aquatic ones. Algorithms that maintain the quality of semantic segmentation of ERS images are in demand, specifically, to identify areas with phytoplankton, where water blooms— the cause of suffocation — are possible. The objective of the study is to create an algorithm that processes satellite data as input information for the formation and checking of mathematical models of hydrodynamics, which are used to monitor the state of water bodies. Various algorithms for semantic segmentation are described in the literature. New research focuses on enhancing the reliability of recognition — often using neural networks. This approach is modified in the presented work. To develop the direction, a new set of information from open sources and synthetic data are proposed. They are aimed at improving the generalization ability of the model. For the first time, the contour area of the phytoplankton population is compared to the database — and thus the boundary conditions are formed for the implementation of mathematical models and the construction of boundary-adaptive grids.Materials and Methods. The set of remote sensing images was supplemented with the author's augmentation algorithm in Python. Computer vision segmented areas of phytoplankton populations in the images. The U-Net convolutional neural network (CNN) was trained on the basis of NVIDIA Tesla T4 computing accelerators.Results. To automate the detection of phytoplankton distribution areas, a computer vision algorithm based on the U-Net CNN was developed. The model was evaluated by the calculated values of the main quality metrics related to segmentation tasks. The following metric values were obtained: Precision = 0.89, Recall = 0.88, F1 = 0.87, Dice = 0.87, and IoU = 0.79. Graphical visualization of the results of CNN learning on the training and validation sets showed good quality of model learning. This is evidenced by small changes in the loss function at the end of training. The segmentation performed by the model turned out to be close to manual marking, which indicated the high quality of the proposed solution. The area of the segmented region of the phytoplankton population was calculated by the area of one pixel. The result obtained for the original image was 51202.5 (based on information about the number of pixels related to the bloom of blue-green algae). The corresponding result of the modeling was 51312.Discussion and Conclusion. The study expands theoretical and practical knowledge on the use of convolutional neural networks for semantic segmentation of space imagery data. Given the results of the work, it is possible to assess the potential for automating the process of semantic segmentation of remote sensing data to determine the boundaries of phytoplankton populations using artificial intelligence. The use of the proposed computer vision model to obtain contours of water bloom due to phytoplankton will provide for the creation of databases — the basis for environmental monitoring of water resources and predictive modeling of hydrobiological processes.