Resolution Conversion Research Articles

Carbon Footprint Optimization with Data Resolution Conversion via Kalman Filter for Smart Energy Hub

The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. And transitioning to renewable energy sources is essential for achieving sustainability and combating climate change. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, considering the minimization of operational running costs, and the maximization of profit. In this paper, two optimization scenarios were studied to compare the results. In the first scenario, the minimization of carbon emissions was achieved. In the second scenario, the minimization of running costs and the maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then, the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly ones via the Kalman filter rather than by applying the classic polynomial interpolation methods.

NDNet: Spacewise Multiscale Representation Learning via Neighbor Decoupling for Real-Time Driving Scene Parsing.

As a safety-critical application, autonomous driving requires high-quality semantic segmentation and real-time performance for deployment. Existing method commonly suffers from information loss and massive computational burden due to high-resolution input-output and multiscale learning scheme, which runs counter to the real-time requirements. In contrast to channelwise information modeling commonly adopted by modern networks, in this article, we propose a novel real-time driving scene parsing framework named NDNet from a novel perspective of spacewise neighbor decoupling (ND) and neighbor coupling (NC). We first define and implement the reversible operations called ND and NC, which realize lossless resolution conversion for complementary thumbnails sampling and collation to facilitate spatial modeling. Based on ND and NC, we further propose three modules, namely, local capturer and global dependence builder (LCGB), spacewise multiscale feature extractor (SMFE), and high-resolution semantic generator (HSG), which form the whole pipeline of NDNet. The LCGB serves as a stem block to preprocess the large-scale input for fast but lossless resolution reduction and extract initial features with global context. Then the SMFE is used for dense feature extraction and can obtain rich multiscale features in spatial dimension with less computational overhead. As for high-resolution semantic output, the HSG is designed for fast resolution reconstruction and adaptive semantic confusion amending. Experiments show the superiority of the proposed method. NDNet achieves the state-of-the-art performance on the Cityscapes dataset which reports 76.47% mIoU at 240+ frames/s and 78.8% mIoU at 150+ frames/s on the benchmark. Codes are available at https://github.com/LiShuTJ/NDNet.

Mathematical Properties of Pyramid-Transform-Based Resolution Conversion and Its Applications

Mathematical Properties of Pyramid-Transform-Based Resolution Conversion and Its Applications

The road to integrate climate change projections with regional land‐use–biodiversity models

Abstract Current approaches to project spatial biodiversity responses to climate change mainly focus on the direct effects of climate on species while regarding land use and land cover as constant or prescribed by global land‐use scenarios. However, local land‐use decisions are often affected by climate change and biodiversity on top of socioeconomic and policy drivers. To realistically understand and predict climate impacts on biodiversity, it is, therefore, necessary to integrate both direct and indirect effects (via climate‐driven land‐use change) of climate change on biodiversity. In this perspective paper, we outline how biodiversity models could be better integrated with regional, climate‐driven land‐use models. We initially provide a short, non‐exhaustive review of empirical and modelling approaches to land‐use and land‐cover change (LU) and biodiversity (BD) change at regional scales, which forms the base for our perspective about improved integration of LU and BD models. We consider a diversity of approaches, with a special emphasis on mechanistic models. We also look at current levels of integration and at model properties, such as scales, inputs and outputs, to further identify integration challenges and opportunities. We find that LU integration in BD models is more frequent than the other way around and has been achieved at different levels: from overlapping predictions to simultaneously coupled simulations (i.e. bidirectional effects). Of the integrated LU‐BD socio‐ecological models, some studies included climate change effects on LU, but the relative contribution of direct vs. indirect effects of climate change on BD remains a key research challenge. Important research avenues include concerted efforts in harmonizing spatial and temporal resolution, disentangling direct and indirect effects of climate change on biodiversity, explicitly accounting for bidirectional feedbacks, and ultimately feeding socio‐ecological systems back into climate predictions. These avenues can be navigated by matching models, plugins for format and resolution conversion, and increasing the land‐use forecast horizon with adequate uncertainty. Recent developments of coupled models show that such integration is achievable and can lead to novel insights into climate–land use–biodiversity relations. Read the free Plain Language Summary for this article on the Journal blog.

Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component-Guided Adversarial Learning.

With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike traditional image degradation models (IDM), such as rain removal and super resolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI). To this end, a two-stage network is presented. The first stage generates low-resolution face images (LR-FIs), from which heavy rain has been removed from the LRHR-FIs to improve visibility. To realize this, an interpretable IDM-based network is constructed to predict physical parameters, such as rain streaks, transmission maps, and atmospheric light. In addition, the image reconstruction loss is evaluated to enhance the estimates of the physical parameters. For the second stage, which aims to reconstruct the HR-FIs from the LR-FIs outputted in the first stage, facial component-guided adversarial learning (FCGAL) is applied to boost facial structure expressions. To focus on informative facial features and reinforce the authenticity of facial components, such as the eyes and nose, a face parsing-guided generator and facial local discriminators are designed for FCGAL. The experimental results verify that the proposed approach based on a physical-based network design and FCGAL can remove heavy rain and increase the resolution and visibility simultaneously. Moreover, the proposed heavy rain face image restoration outperforms state-of-the-art models of heavy rain removal, image-to-image translation, and super resolution.

Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring

The increasingly widespread use of smartphones as real cameras on drones has allowed an ever-greater development of several algorithms to improve the image’s refinement. Although the latest generations of drone cameras let the user achieve high resolution images, the large number of pixels to be processed and the acquisitions from multiple lengths for stereo-view often fail to guarantee satisfactory results. In particular, high flight altitudes strongly impact the accuracy, and result in images which are undefined or blurry. This is not acceptable in the field of road pavement monitoring. In that case, the conventional algorithms used for the image resolution conversion, such as the bilinear interpolation algorithm, do not allow high frequency information to be retrieved from an undefined capture. This aspect is felt more strongly when using the recorded images to build a 3D scenario, since its geometric accuracy is greater when the resolution of the photos is higher. Super-Resolution algorithms (SRa) are utilized when registering multiple low-resolution images to interpolate sub-pixel information The aim of this work is to assess, at high flight altitudes, the geometric precision of a 3D model by using the the Morpho Super-Resolution™ algorithm for a road pavement distress monitoring case study.

Treatment of Covid 19 Infection with Oselatamivir as First Line Antiviral Therapy for Non-Hospitalized Patients

Covid-19 treatments are evolving, trying to catch up to the evolution of the virus itself as new variants continue to surface. In this article, four cases of two vaccinated and two unvaccinated individuals with mild to moderate Covid-19 are presented that have been treated with Oseltamivir, an influenza neuraminidase enzyme inhibitor. The use of Oseltamivir was the main antiviral drug in a combination regimen containing an antibiotic and a steroid. Oseltamivir has achieved symptom improvement in as little as 48 hours after commencement of treatment, with full resolution and subsequent conversion of a positive Covid-19 PCR to negative within 7-10 days, indicating a possible use of Oseltamivir as an alternative to Ritonavir boosted Nirmatrelvir (Paxlovid).

Comparison of 90Y SIRT predicted and delivered absorbed doses using a PSF conversion method.

The aims of this study were to develop and apply a method to correct for the differences in partial volume effects of pre-therapy Technetium-99m (99mTc)-MAA SPECT and post-therapy Yttrium-90 (90Y) bremsstrahlung SPECT imaging in selective internal radiation therapy, and to use this method to improve quantitative comparison of predicted and delivered 90Y absorbed doses. The spatial resolution of 99mTc SPECT data was converted to that of 90Y SPECT data using a function calculated from 99mTc and 90Y point spread functions. This resolution conversion method (RCM) was first applied to 99mTc and 90Y SPECT phantom data to validate the method, and then to clinical data to assess the power of 99mTc SPECT imaging to predict the therapeutic absorbed dose. The maximum difference between absorbed doses to phantom spheres was 178%. This was reduced to 27% after the RCM was applied. The clinical data demonstrated differences within 38% for mean absorbed doses delivered to the normal liver, which were reduced to 20% after application of the RCM. Analysis of clinical data showed that therapeutic absorbed doses delivered to tumours greater than 100cm3 were predicted to within 52%, although there were differences of up to 210% for smaller tumours, even after the RCM was applied. The RCM was successfully verified using phantom data. Analysis of the clinical data established that the 99mTc pre-therapy imaging was predictive of the 90Y absorbed dose to the normal liver to within 20%, but had poor predictability for tumours smaller than 100cm3.

Resolution Conversion of SAR Target Images Using Conditional GAN

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application. Keywords: 합성 개구면 ë ˆì´ë”, 조건부 ìƒì„±ì  ì ëŒ€ ì‹ ê²½ë§, í‘œì ì˜ìƒ, 해상도 변환 Keywords: Synthetic Aperture Radar, Conditional Generative Adversarial Network, Target Image, Resolution Conversion

Visual outcome of cataract surgery and its influencing factors in China

Objective: To explore the visual outcome of cataract surgery and its influencing factors in China. Methods: A case-control study. The information of 2 078 479 patients who underwent cataract extraction of 31 provinces (municipalities and autonomous regions) recorded in the National Cataract Surgery Information Reporting System during January to December, 2017 were analyzed. The postoperative visual outcomes of 3 days after surgery and 3 months after surgery were analyzed. The effects of age, gender, preoperative corrected visual acuity, past medical history, and surgical methods on the results of cataract surgery were analyzed. Surgery effect was divided into ideal effect, moderate effect and poor effect according to the best corrected visual acuity after operation. Wilcoxon rank sum test and multinomial logistic regression model were used for statistical analysis. Results: There were 1 197 942 female patients (57.64%) and 880 537 male patients (42.36%) in all 2 078 479 cases. The average age was (69±11) years old. The main type of cataract was age-related (1 928 440 cases; 92.78%). There were 1 608 385 cases, 1 126 961 cases and 389 020 cases with complete corrected visual acuity before surgery, 3 days after surgery and 3 months after surgery, respectively. The median difference of corrected visual acuity of preoperative versus three days after surgery, and preoperative versus three months after surgery were -0.48, -0.52 respectively (after logarithm of the minimum angle of resolution conversion). The interquartile ranges were 0.60 and 0.73 respectively. The differences were statistically significant (Z=551 599.30, 206 815.35; both P<0.01). Multivariate analysis showed that postoperative complications (moderate effect: OR=0.50, 95%CI: 0.46 to 0.55; ideal effect: OR=0.31, 95%CI: 0.28 to 0.33), preoperative pupil abnormal (moderate effect: OR=0.52, 95%CI: 0.44 to 0.61; ideal effect: OR=0.55, 95%CI: 0.47 to 0.63), preoperative intraocular pressure abnormalities (moderate effect: OR=0.86, 95%CI: 0.79 to 0.93; ideal effect: OR=0.45, 95%CI: 0.42 to 0.49), history of hypertension (moderate effect: OR=0.90, 95%CI: 0.85 to 0.95; ideal effect: OR=0.88, 95%CI: 0.83 to 0.93) and history of diabetes (moderate effect: OR=0.86, 95%CI: 0.80 to 0.93; ideal effect: OR=0.92, 95%CI: 0.86 to 0.99) were predictors of worse outcome. Good preoperative corrected visual acuity (0.2 to 0.4 for moderate effect: OR=2.20, 95%CI: 2.06 to 2.34; for ideal effect: OR=5.25, 95%CI: 4.94 to 5.59; 0.5 to 0.7 for moderate effect: OR=1.08, 95%CI: 0.96 to 1.21; for ideal effect: OR=6.35, 95%CI: 5.69 to 7.08; above 0.8 for moderate effect: OR=0.73, 95%CI: 0.60 to 0.89; for ideal effect: OR=6.58, 95%CI: 5.51 to 7.86), small incision extracapsular extraction (moderate effect: OR=3.19, 95%CI: 2.35 to 4.33; ideal effect: OR=6.79, 95%CI: 5.13 to 8.97) and phacoemulsification (moderate effect: OR=2.12, 95%CI: 1.60 to 2.81; ideal effect: OR=3.34, 95%CI: 2.59 to 4.32) were predictors of better outcome. Conclusions: Visual acuity of patients in China improve significantly after cataract surgery. However, some patients still suffer moderate visual impairment. The effect of surgery can be affected by many factors, such as preoperative corrected visual acuity, preoperative intraocular pressure, preoperative pupil status, surgical approach and postoperative complications, etc. (Chin J Ophthalmol, 2021, 57: 63-70).

A multiresolution simulation system and simulation development processes

Defense modeling and simulation often should run multiple simulation models together to share information on their combat entity states and battlefields and to interact with each other. However, most simulation models were developed by various parties with different purposes, modeling views, and model resolutions. Therefore, it has been challenging to identify differences between the models, transform one’s information for the other models, and manage interoperation between the models. There have been numerous works on multiresolution modeling (MRM) and simulation. However, we yet should have more systematic and integrated methods for designing, managing, and executing the interoperation processes of different simulation models. In this paper, we propose an integrated multiresolution simulation system for designing, developing, simulating, and managing multiple simulation models with different modeling views and resolutions. We propose an architecture, component functions, and processes, including model resolution conversion and management processes, and their Unified Modeling Language models. We also extend the Distributed Simulation Engineering and Execution Processes standard to incorporate MRM design and development processes. Finally, we present a case of MRM design and development of war game models by using the proposed system.

Global Land Surface Modeling Including Lateral Groundwater Flow

AbstractThe groundwater system is an essential part of Earth's systems. However, most current land surface models (LSMs) for climate modeling do not explicitly account for the lateral groundwater flow process. In this study, schemes describing LSM‐lateral groundwater flow module coupling, model resolution conversion, and parallel simulation were designed and implemented to incorporate a lateral groundwater flow module into the Community Land Model 4.5. The depth to less permeable bedrock also was included in the large‐scale groundwater flow modeling. Model validation was performed using multiple observations from the 20‐year continuous groundwater table depth measurements at 67 stations in 10 countries, 1.6 million worldwide time‐averaged groundwater table depth measurements, previous knowledge about the locations of major aquifer systems, and inversed terrestrial water storage anomalies derived from satellite data. The simulated results show that the groundwater table pattern is a combined reflection of climatic and topographic factors across a range of spatial scales. Lateral groundwater flow significantly modified the equilibrium water table patterns in North Africa, the Arabian Peninsula, central Asia, and southern Australia, deepening the water tables by more than 6 m. The trend of deepening groundwater tables observed between 1970 and 2010, which was found to be 0.025 to 0.125 m/decade, was exacerbated by the lateral flow; however, the seasonal variability of the groundwater table depth was reduced by the buffering effect of the lateral flow.

Electromechanical Sigma–Delta Modulators (&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\Sigma \Delta {\mathrm{ M}}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;) Force Feedback Interfaces for Capacitive MEMS Inertial Sensors: A Review

Analog-to-digital converters based on sigma–delta modulators ( $\Sigma \Delta {\mathrm{ M}}$ ) are a popular choice for high resolution conversion from the analog to the digital domain. With relatively small modifications, they can also be used as electromechanical $\Sigma \Delta {\mathrm{ M}}$ (EM- $\Sigma \Delta {\mathrm{ M}}$ ) force feedback interfaces for capacitive micro-electromechanical systems (MEMSs) inertial sensors. Such interfaces are able to combine the benefits of force feedback and analog-to-digital conversion at relatively modest circuit cost. This paper provides a comprehensive review of EM- $\Sigma \Delta {\mathrm{ M}}$ interfaces for capacitive MEMS inertial sensors. The principle and the design methodology of EM- $\Sigma \Delta {\mathrm{ M}}$ interfaces are introduced. A classification of EM- $\Sigma \Delta {\mathrm{ M}}$ accelerometers and gyroscopes is presented, and a detailed analysis of different EM- $\Sigma \Delta {\mathrm{ M}}$ architectures is given. The most representative EM- $\Sigma \Delta {\mathrm{ M}}$ inertial sensors systems are discussed and compared with regard to their performance characteristics. In particular, the properties of various discrete and continuous-time techniques and a system parameter optimization methodology are illustrated through specific examples. Finally, current challenges and future development trends of EM- $\Sigma \Delta {\mathrm{ M}}$ interfaces for inertial sensors have been identified.

간호학과 학생의 스트레스 대처 양상

This study designed to know subjective structure about type of coping with stress of nursing students using Q-methodology. The statement positions on the sorting continuum were scored from +4(representing the highest level of agreement) through 0(at the neutral position on the continuum) through -4(representing the highest level of disagreement). Factor analysis of the data was undertaken using PC QUANL software to determine if any patterns were discernible. The four types extracted eigenvalues greater than 1.0 and explained approximately 52.14% of the variation in responses(31.73, 10.13, 5.73 and 4.65% respectively).: (1) Positive resolve type, (2) Ignore problem type, (3) Self resolution type and (4) Conversation type. The study results can be utilized in both an effective nursing education and clinical practice.

High-level design of general multi-stage noise band cancellation $$\Upsigma\Updelta$$ Σ Δ ADC optimized for nonlinearly distorted signals

This article presents the high-level design of general multi-stage noise band cancellation (MSNBC) $$\Upsigma\Updelta$$ Σ Δ analog-to-digital converters (ADC). The MSNBC architecture enables to extend the conversion bandwidth of a primary modulator and to provide high resolution conversion. Moreover, this architecture should offer a low power consumption. This type of ADC is very useful for wideband and high resolution application such as the digitization of data for the linearization of base station (BTS) power amplifiers. This article introduces the MSNBC architecture and provides a methodology for the high-level design of such ADC for both discrete-time and continuous-time implementation. The methodology enables the design of stable and high resolution modulators and, the direct synthesis of digital FIR filters that are used for noise cancellation. This methodology is fast and useful to design high performance MSNBC $$\Upsigma\Updelta$$ Σ Δ ADC.

Decrement Signal Generator of High-Precision E Exponential Based on ARM

With the development of electronic technology, the signal generator used in most of the lab employs direct digital synthesis (DDS) technology. The signal generator using digital technology has advantages of high resolution and short conversion time and so on [1, 2]. Through ARM chip STM32F103VET6, we realize the control to the chip AD5791. At the same time, it engender high-accuracy periodic e exponential decremented signal. Doing above, we correct transient electromagnetic system which has been manufactured. The output signal amplitude is 5V. Frequency can be adjusted according to the sampling points and sampling time of the receiving device.

Specification of multi-resolution modeling space for multi-resolution system simulation

Multi-resolution modeling (MRM) represents a real system as a set of models of different resolutions, at different abstraction levels, from the viewpoint of simulation objectives. MRM is a significant technology for complex and large-scale simulation. However, previous research is almost application specific and dependent on a specific simulation algorithm. The concepts separate multi-resolution models from the simulation models, thereby making them adaptable to any existing simulation methodology. This paper proposes a specification of multi-resolution modeling space (MRMS) for multi-resolution system simulation. The proposed specification is based on concepts of decoupling MRM and multi-resolution simulation. This MRMS specification supports MRM in two parts: resolution conversion for dynamically changing simulation model structures and resolution matching interfaces between events in different resolutions. A simple example demonstrates advantages of the proposed specification.

Optimal Feature Selection based on Image Pre-processing using Accelerated Binary Particle Swarm Optimization for Enhanced Face Recognition

Feature Selection is an optimization problem in any Face Recognition technology. This paper proposes a novel method of Binary Particle Swarm Optimization called Accelerated Binary Particle Swarm Optimization (ABPSO) by intelligent acceleration of particles. Together with Image Pre-processing techniques such as Resolution Conversion, Histogram Equalization and Edge Detection, ABPSO is used for feature selection to obtain significantly reduced feature subset and improved recognition rate. The performance of ABPSO is established by computing the recognition rate and the number of selected features on ORL database and Cropped Yale B database.

Low Complexity Pipelined Architecture for Real-Time Generic Video Scaling

A five stage pipelined architecture is proposed to target real-time video resolution conversion in spatial domain. This low complexity design is based on pre-computed memory mapping which computes the geometric position of interpolated pixels and their gray level values based on the scaling factor. The memory map has been conceived using nearest neighbor interpolation technique which simplifies the implementation. The scaling ratio is provided as an input to the hardware architecture before conversion. The design is capable of converting any video frame size in real-time. The memory requirement in this operation has been significantly reduced in comparison with earlier hardware based schemes. The results have been validated on a Xilinx Spartan FPGA running at 100 MHz Conversion times for different scaling ratios have been reported

Emotional reactivity, regulation and childhood stuttering: A behavioral and electrophysiological study

The purpose of this preliminary study was to assess whether behavioral and psychophysiological correlates of emotional reactivity and regulation are associated with developmental stuttering, as well as determine the feasibility of these methods in preschool-age children. Nine preschool-age children who stutter (CWS) and nine preschool-age children who do not stutter (CWNS) listened to brief background conversations conveying happy, neutral, and angry emotions (a resolution conversation followed the angry conversation), then produced narratives based on a text-free storybook. Electroencephalograms (EEG) recorded during listening examined cortical correlates of emotional reactivity and regulation. Speech disfluencies and observed emotion regulation were measured during a narrative immediately after each background conversation. Results indicated that decreased use of regulatory strategies is related to more stuttering in children who stutter. However, no significant differences were found in EEG measurements of emotional reactivity and regulation between CWS and CWNS or between emotion elicitation conditions. Findings were taken to suggest that use of regulatory strategies may relate to the fluency of preschool-age children's speech-language output. Learning outcomes: The reader will be able to (1) describe emotional reactivity and regulation processes, (2) discuss evidence for or against the relations of emotional reactivity, regulation and stuttering, (3) understand how multiple measures can be used to measure emotional reactivity and regulation.