Accuracy Degradation Research Articles

Adversarial Circuit Rewriting against Graph Neural Network-based Operator Detection

Recent work has shown that graph neural networks (GNNs) can be used to recover high-level word operators and their boundaries in gate-level netlists. Unlike formal methods, however, the GNN does not prove functional equivalence. As such, there is a question of whether structural transforms like circuit rewrites that preserve the functionality of the circuit can diminish the performance of GNN-based operator detection. In this work, we explore this problem by performing simple rewrites on benchmark circuits and showing that indeed there is a performance degradation. We propose new features and learning strategies that can alleviate this. Finally, we develop a rewriting framework that aims to optimize GNN accuracy degradation, which translates to a fine-grained adversarial circuit rewriting of sorts. This can have applications in circuit obfuscation or further improving reverse engineering by providing synthetic training data. We show how this methodology beats non-adversarial rewriting under the same area overhead.

Energy‐Efficient Hardware Implementation of Spiking‐Restricted Boltzmann Machines Using Pseudo‐Synaptic Sampling

Stochastic sampling is performed to reduce hardware energy consumption and prevent overfitting by reducing parameters, because not all data are required for learning. In this study, a new approach, pseudo‐synaptic sampling (PS2) method, which approximates the conventional synaptic sampling machine (S2M) method through a hardware‐friendly implementation while demonstrating superior efficiency, is introduced. By sampling in front of neurons rather than at each synapse, the PS2 method improves hardware energy efficiency and ensures scalability. Furthermore, it improves energy and area efficiency by eliminating the additional circuit required by other techniques, such as the random walk (RW) method previously used which requires an additional circuit to frequently charge/discharge the membrane potential. Herein, the average firing rate equation for the S2M method is modified to suit the experimental conditions of this study. Through this numerical simulations, it is confirmed that the activation function of the PS2 method aligns with that of the S2M method and verified that the PS2 method can implement stochasticity for restricted Boltzmann machine (RBM) neurons. Experimental validation of the PS2 method, compared to the RW method, for Modified National Institute of Standards and Technology database (MNIST) training and inference on field‐programmable‐gate‐array‐implemented spiking RBM chips reveals promising results. In an MNIST 100‐handwritten digit experiment, the PS2 method exhibits on‐chip training accuracy (92%) comparable to that of the RW method (93%). Furthermore, in the energy consumption analysis, it is shown that the PS2 method reduces power consumption by 94.94% compared to the RW method, highlighting its enhanced power efficiency due to a reduced number of circuit elements. In the investigation into the impact of increasing the frequency at which random bits are generated, it is shown that the RW method experiences accuracy degradation even with slight increases, whereas the PS2 method maintains accuracy over significantly longer periods. This enables further power reduction by allowing for a longer period during random bit generation. In this study, a foundation is laid for maximizing the energy efficiency of spiking neural network processors by optimizing internal noise generation mechanisms.

Evaluation of reduced-order models for the rapid aerodynamic analysis of supersonic and hypersonic bodies

Abstract This paper presents a comprehensive evaluation of reduced-order models (ROMs) for the determination of pressure coefficient distributions on supersonic and hypersonic bodies. The study investigates the limitations, aerodynamic precision and computational performance associated with various methodologies, ranging from simplistic Newtonian theory-based approaches to more advanced first and second-order shock-expansion theories. Validation is performed by comparing computed results with experimental and computational data for pressure distributions, drag and lift coefficients and centres of pressure for fundamental geometries and authentic vehicle design over a wide range of freestream conditions. The study also includes a comprehensive computational complexity analysis, demonstrating the superiority of finite-element ROM approaches over traditional finite-volume computational fluid dynamics (CFD) simulations. The primary objective of this paper is to scrutinise the extension of these methodological classes to the low supersonic regime. Hence, thermo-chemical reactions within the flow are disregarded, and the ideal gas law is adopted. A value of $\gamma = 1.4$ is chosen for consistency and comparability across the analyses. The proposed ROMs show remarkable potential for reducing high-speed simulation execution times by four orders of magnitude, maintaining accuracy within 20 per cent and as low as 1 per cent. The study unveils three key findings: first, the accuracy degradation of Newtonian-based theories for inclined elements, particularly around 45 degrees, and their reduced dependency on Mach number at large inclination. Secondly, the study presents novel insights into the impact of shock-wave-Mach-wave interactions on pressure distribution calculations, emphasising the Mach number as a crucial metric governing recompression effects. Lastly, the study demonstrates the exceptional accuracy of DeJarnette’s method, providing ${C_P}$ results within 2 per cent for a wide range of conditions, offering an attractive alternative to the Taylor-Maccoll equation.

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost-benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.

Intelligent Fault Diagnosis Method Based on Neural Network Compression for Rolling Bearings

Rolling bearings are often exposed to high speeds and pressures, leading to the symmetry in their rotating structure being disrupted, which can lead to serious failures. Intelligent rolling bearing fault diagnosis is a critical part of ensuring operation of machinery, and it has been facilitated by the growing popularity of convolutional neural networks (CNNs). The outstanding performance of fault diagnosis CNNs results from complex and redundant network structures and parameters, resulting in huge storage and computational requirements, which makes it challenging to implement these models in resource-limited industrial devices. This study aims to address this problem by proposing a comprehensive compression method for CNNs that is applied to intelligent fault diagnosis. It involves several different compression methods, including tensor train decomposition, parameter quantization, and knowledge distillation for deep network compression. This results in a significant decrease in redundancy and speeding up the training of CNN models. Firstly, tensor train decomposition is applied to reduce redundant connections in both convolutional and fully connected layers. The next step is to perform parameter quantization to minimize the bits needed for parameter representation and storage. Finally, knowledge distillation is used to restore accuracy to the compressed model. The effectiveness of the proposed approach is confirmed by an experiment and ablation study with different models on several datasets. The results show that it can significantly reduce redundant information and floating-point operations with little degradation in accuracy. Notably, on the CWRU dataset, with about 60% parameter reduction, there is no degradation in our model’s accuracy. The proposed approach is a new attempt at the intelligent fault diagnosis of rolling bearings in industrial equipment.

Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin

ABSTRACTWildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF‐Hydro) to a suite of fire‐related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire‐related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire‐related perturbations enhanced post‐fire model accuracy across the 11–12 post‐fire months evaluated herein. The most comprehensive fire‐aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire‐enhanced streamflow was predominately attributable to fire‐induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early‐summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8‐days) from the most comprehensive fire‐aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire‐related perturbations reduced post‐fire streamflow anomaly biases across the three study catchments. However, remaining large post‐fire streamflow uncertainties in the fire‐perturbed simulation underscores the importance of additional observationally constrained fire‐disturbance model developments.

LCGSC-YOLO: a lightweight apple leaf diseases detection method based on LCNet and GSConv module under YOLO framework.

In response to the current mainstream deep learning detection methods with a large number of learned parameters and the complexity of apple leaf disease scenarios, the paper proposes a lightweight method and names it LCGSC-YOLO. This method is based on the LCNet(A Lightweight CPU Convolutional Neural Network) and GSConv(Group Shuffle Convolution) module modified YOLO(You Only Look Once) framework. Firstly, the lightweight LCNet is utilized to reconstruct the backbone network, with the purpose of reducing the number of parameters and computations of the model. Secondly, the GSConv module and the VOVGSCSP (Slim-neck by GSConv) module are introduced in the neck network, which makes it possible to minimize the number of model parameters and computations while guaranteeing the fusion capability among the different feature layers. Finally, coordinate attention is embedded in the tail of the backbone and after each VOVGSCSP module to improve the problem of detection accuracy degradation issue caused by model lightweighting. The experimental results show the LCGSC-YOLO can achieve an excellent detection performance with mean average precision of 95.5% and detection speed of 53 frames per second (FPS) on the mixed datasets of Plant Pathology 2021 (FGVC8) and AppleLeaf9. The number of parameters and Floating Point Operations (FLOPs) of the LCGSC-YOLO are much less thanother related comparative experimental algorithms.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

Analysis of the degradation in transmission accuracy of RV reducers considering the wear clearance between cycloidal wheels and pinwheels

To accurately determine the transmission accuracy degradation pattern of Rotate Vector (RV) reducers due to wear, a predictive method is proposed that simultaneously considers the initial clearance and wear clearance between the cycloid wheel and the pinwheel. The initial clearance is derived from a comprehensive gear system analysis, considering assembly errors, manufacturing errors and modifications. Building on this, the wear depth of the cycloid wheel tooth profile is numerically simulated using Archard's wear theory, revealing the wear distribution pattern under various operating conditions. A Gaussian regression model is used to predict the maximum wear amount for different operational durations. Based on the predicted wear amount, a multi-body dynamic simulation model incorporating wear clearance was established to analyze the transmission error under varying clearance conditions. Results indicate that the wear depth along the tooth profile initially increases and then decreases. The depth and region of wear along the tooth profile gradually increase with load and output speed, with the impact of load on the wear region being greater than that of output speed. Under the initial clearance condition, the maximum transmission error is 22.91″. As the wear clearance increases, transmission accuracy declines sharply. When the cumulative wear clearance reaches 0.2 mm, the transmission error exceeds the allowable backlash limit. This study provides a theoretical reference for predicting accuracy degradation and guides the manufacturing design of RV reducers.

Sensor-Aware Data Imputation for Time-Series Machine Learning on Low-Power Wearable Devices

Wearable devices that have low-power sensors, processors, and communication capabilities are gaining wide adoption in several health applications. The machine learning algorithms on these devices assume that data from all sensors are available during runtime. However, data from one or more sensors may be unavailable due to energy or communication challenges. This loss of sensor data can result in accuracy degradation of the application. Prior approaches to handle missing data, such as generative models or training multiple classifiers for each combination of missing sensors are not suitable for low-energy wearable devices due to their high overhead at runtime. In contrast to prior approaches, we present an energy-efficient approach, referred to as Sensor-Aware iMputation (SAM), to accurately impute missing data at runtime and recover application accuracy. SAM first uses unsupervised clustering to obtain clusters of similar sensor data patterns. Next, it learns inter-relationship between clusters to obtain imputation patterns for each combination of clusters using a principled sensor-aware search algorithm. Using sensor data for clustering before choosing imputation patterns ensures that the imputation is aware of sensor data observations. Experiments on seven diverse wearable sensor based time-series datasets demonstrate that SAM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing. We also compare SAM against generative adversarial imputation networks (GAIN), transformers, and k-nearest neighbor methods. Results show that SAM outperforms all three approaches on average by more than 25% when two sensors are missing with negligible overhead compared to the baseline.

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43 % accuracy with 0.34 ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

A robust iterative algorithm for SINS/USBL integrated navigation based on dual hydrophone differential model

Aiming at the complex underwater environment which leads to large measurement errors and outliers in ultrashort baseline, a robust iterative Kalman filtering algorithm based on the differential model of dual hydrophones is proposed in this paper. Firstly, to achieve accurate navigation under large initial misalignment angles, the algorithm builds an inertial navigation error equation based on the right invariant error definition of Lie groups. Then a dual hydrophone differential model is proposed based on SINS/USBL tightly coupled. It successfully improves the accuracy of integrated navigation and significantly mitigating the issue of SINS/USBL positioning accuracy degradation caused by USBL slant distance noise that grows with distance. Finally, a robust iterative Kalman filter based on the dual hydrophone differential model is proposed to counter the threat to the vehicle’s safe operation and the accuracy loss caused by outliers. The algorithm’s superiority and effectiveness are verified through simulation and sea trial.

Non-contact measurement of structural dynamic displacement based on improved edge detection and video interpolation

In structural health monitoring (SHM), smartphones are increasingly used for non-contact dynamic displacement measurement using computer vision. To overcome limitations in capturing high-frame-rate data, a non-contact measurement method of structural dynamic displacement based on an improved edge detection algorithm and video frame interpolation is proposed. Initially, a smartphone is employed to capture the structure’s vibration video and then generates intermediary frames between adjacent frames that match the structure’s motion state by using a video frame interpolation algorithm to obtain a video with a high frame rate. Subsequently, the improved edge detection algorithm is applied to measure the displacement within the interpolated video. In order to address the problem that traditional edge detection may have false edges leading to the degradation of measurement accuracy, a feature point tracking technique is proposed based on the traditional edge detection technique, which effectively excludes the interference of false edges in ROI and improves the accuracy of displacement monitoring. Experimental validation on a cantilever beam and steel frame bridge demonstrates the method’s viability and precision, highlighting its effectiveness in improving displacement measurement accuracy compared to traditional methods.

Full-field three-dimensional system calibration for composite surfaces reconstruction

The accurate calibration of composite surface measurement systems for specular and diffused surface is of great importance in a number of industrial applications. This paper addresses the challenges in calibrating composite surface reconstruction by proposing a comprehensive full-field calibration method. The principal innovation is the construction of a comprehensive reference phase and the pixel-by-pixel calibration of system parameters within the same coordinate system. The details are as follows: (1) construct a complete reference phase for the composite surfaces through external parameter constraints phase fusion and interpolation; (2) calibrate the mapping relationship coefficients, compensate the accuracy degradation of the partial reflection. Experimental results validate the accuracy of the full-field calibration data, demonstrating the effectiveness and feasibility.

A Variation-Aware Binary Neural Network Framework for Process Resilient In-Memory Computations

Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computation-in-memory (CIM), which computes multiplication and accumulations on memory arrays in an analog fashion, namely, analog CIM, we can further improve the energy efficiency to process neural networks. However, analog CIMs are susceptible to process variation, which refers to the variability in manufacturing that causes fluctuations in the electrical properties of transistors, resulting in significant degradation in BNN accuracy. Our Monte Carlo simulations demonstrate that in an SRAM-based analog CIM implementing the VGG-9 BNN model, the classification accuracy on the CIFAR-10 image dataset is degraded to below 50% under process variations in a 28 nm FD-SOI technology. To overcome this problem, we present a variation-aware BNN framework. The proposed framework is developed for SRAM-based BNN CIMs since SRAM is most widely used as on-chip memory; however, it is easily extensible to BNN CIMs based on other memories. Our extensive experimental results demonstrate that under process variation of 28 nm FD-SOI, with an SRAM array size of 128×128, our framework significantly enhances classification accuracies on both the MNIST hand-written digit dataset and the CIFAR-10 image dataset. Specifically, for the CONVNET BNN model on MNIST, accuracy improves from 60.24% to 92.33%, while for the VGG-9 BNN model on CIFAR-10, accuracy increases from 45.23% to 78.22%.

Bidirectional Corrective Model-Contrastive Federated Adversarial Training

When dealing with non-IID data, federated learning confronts issues such as client drift and sluggish convergence. Therefore, we propose a Bidirectional Corrective Model-Contrastive Federated Adversarial Training (BCMCFAT) framework. On the client side, we design a category information correction module to correct biases caused by imbalanced local data by incorporating the local client’s data distribution information. Through local adversarial training, more robust local models are obtained. Secondly, we propose a model-based adaptive correction algorithm in the server that leverages a self-attention mechanism to handle each client’s data distribution information and introduces learnable aggregation tokens. Through the self-attention mechanism, model contrast learning is conducted on each client to obtain aggregation weights of corrected client models, thus addressing the issues of accuracy degradation and slow convergence caused by client drift. Our algorithm achieves the best natural accuracy on the CIFAR-10, CIFAR-100, and SVHN datasets and demonstrates excellent adversarial defense performance against FGSM, BIM, and PGD attacks.

Research on the measurement mechanism of a six-axis force sensor based on a flexible hinge series–parallel hybrid

Abstract Addressing the common limitations of current six-axis force sensors, including the degradation of strain gauge accuracy due to environmental influences and the necessity for a complete replacement when damaged, this paper proposes an innovative six-axis force sensor that integrates a hybrid serial–parallel structure featuring flexible hinges. The sensor’s bracket is designed using a combination of serial–parallel flexible hinges, which confer load distribution capabilities, are lightweight, have high strength, and facilitate replacement. The static force model and the overall stiffness model of the sensor were developed based on the 3-universal–prismatic–universal parallel mechanism theory, providing a theoretical foundation for evaluating the sensor’s performance. To validate the accuracy of the stiffness model, finite element software is utilized for simulation-based verification. Subsequently, a calibration experiment is performed on the sensor, yielding results that conclusively show an error margin of less than 5% between experimental and theoretical values, satisfying the design criteria for sensor measurement.

DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM.

Some effects of limited wall-sensor availability on flow estimation with 3D-GANs

In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Reτ=200\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Re_{\ au }=200$$\\end{document}. The estimation technique is based on a 3D generative adversarial network (3D-GAN). We recently demonstrated that 3D-GANs are capable of estimating fields with good accuracy by employing fully-resolved wall quantities (pressure and streamwise/spanwise wall shear stress on a grid with DNS resolution). However, the practical implementation in an experimental setting is challenging due to the large number of sensors required. In this work, we aim to estimate the flow fields with substantially fewer sensors. The impact of the reduction of the number of sensors on the quality of the flow reconstruction is assessed in terms of accuracy degradation and spectral length-scales involved. It is found that the accuracy degradation is mainly due to the spatial undersampling of scales, rather than the reduction of the number of sensors per se. We explore the performance of the estimator in case only one wall quantity is available. When a large number of sensors is available, pressure measurements provide more accurate flow field estimations. Conversely, the elongated patterns of the streamwise wall shear stress make this quantity the most suitable when only few sensors are available. As a further step towards a real application, the effect of sensor noise is also quantified. It is shown that configurations with fewer sensors are less sensitive to measurement noise.

Optimal filtering of an information process depended on noninformative parameters described by Marcovian chain when radio signals are receiving

In radio navigation and radio location tasks tracking systems on radio signals parameters often operate when object acceleration has stepwise changing. If we synthesize various tracking systems using optimal filtering theory it is necessary to represent the estimated process as a multidimensional Marcovian process. But such representation for stepwise changed acceleration is a rough approximation that lead to estimates accuracy degradation. One of possible approach to such disambiguation is to describe acceleration as Marcovian chain with finite number of states. This approach is developed in the article. Objective. To synthesize an optimal filtering algorithm of information process depended on noninformative parameters described by Marcovian chain using the observation grouping method when receiving radio signals and to implement simulation for an illustrative task.