Adaptive Iterative Algorithm Research Articles

Diagnostic accuracy of ultra-low-dose CT colonography for the detection of colorectal polyps: a feasibility study.

The aim of this feasibility study was to evaluate the diagnostic accuracy of ultra-low-dose CT colonography using iterative reconstruction algorithms with reference to standard colonoscopy. Prior to this study, a phantom study was performed to investigate the optimal protocol for ultra-low-dose CT colonography. A total of 206 patients with average/high risk of colorectal cancer were recruited. After undergoing full bowel preparation, the patients were scanned in the prone and supine positions with the CT conditions set to 120kV, standard deviation 45 to 50, and an adaptive iterative reconstruction algorithm applied. Two expert readers read the images independently. The main outcome measures were the per-patient and per-polyp accuracies for the detection of polyps ≥ 10mm, with colonoscopy results as the reference standard. Two hundred patients (102 females, mean age 67.5years) underwent both ultra-low-dose CT colonography and colonoscopy on the same day. The mean radiation exposure dose was 0.64 ± 0.34mSv. On colonoscopy, 39 patients had 45 polyps ≥ 10mm (non-polypoid morphology 7), including 4 cancers. Per-patient sensitivity, specificity, and accuracy of CT colonography for polyps ≥ 10mm were 0.74, 0.96, and 0.92 for reader one, and 0.74, 0.99, and 0.94 for reader two, respectively. Per-polyp sensitivities for polyps ≥ 10mm were 0.73 for reader one and 0.71 for reader two. On subgroup analysis by morphology, non-polypoid polyps ≥ 10mm were not detected by both readers. Extreme ultra-low-dose CT colonography had an insufficient diagnostic performance for the detection of polyps ≥ 10mm, because it was unable to detect non-polypoid polyps. This study showed that the problem with ultra-low-dose CT colonography was the lack of detectability of small-size polyps, especially non-polypoid polyps. To use ultra-low-dose CT colonography clinically, it is necessary to resolve the problems identified by this study.

Reliability and benefits of single-energy projection-based metallic artifact reduction (SEMAR) in the different orthopedic hardware for the hip.

To evaluate the performance and reliability of the single-energy metal artifact reduction (SEMAR) algorithm in patients with different orthopedic hardware at the hips. A total of 153 patients with hip instrumentation who had undergone CT with adaptive iterative dose reduction (AIDR) 3D and SEMAR algorithms between February 2015 and October 2019 were included retrospectively. Patients were divided into 5 groups by the hardware type. Two readers with 21 and 13years of experience blindly reviewed all image sets and graded the extent of artifacts and imaging quality using 5-point scales. To evaluate reliability, the mean densities and image noises were measured at the urinary bladder, veins, and fat in images with artifacts and the reference images. No significant differences were found in the mean densities of the urinary bladder, veins, and fat between the SEMAR images with artifacts (7.57 ± 9.49, 40.29 ± 23.07, 86.78 ± 38.34) and the reference images (7.77 ± 6.2, 40.27 ± 8.66, 89.10 ± 20.70) (P = .860, .994, .392). Image noises of the urinary bladder in the SEMAR images with artifacts (14.25 ± 4.50) and the SEMAR reference images (9.69 ± 1.29) were significantly higher than those in the AIDR 3D reference images (9.11 ± 1.12) (P < .001; P < .001). All AIDR 3D images were non-diagnostic (overall quality ≤ 3) and less than a quarter of the SEMAR images were non-diagnostic (16.7-23.7%), mainly in patients with prostheses [reader 1: 91.7% (22/24); reader 2: 92.6% (25/27)]. The SEMAR algorithm significantly reduces metal artifacts in CT images, more in patients with internal fixations than in patients with prostheses, and provides reliable attenuation of soft tissues.

Utility of an automatic adaptive iterative metal artifact reduction AiMAR algorithm in improving CT imaging of patients with hip prostheses evaluated for suspected bladder malignancy.

To compare the utility of a novel metal artifact reduction algorithm to standard imaging in improving visualization of key structures, diagnostic confidence, and patient-level confidence in malignancy in patients with suspected bladder cancer. Patients with hip implants undergoing CT urography for suspected bladder malignancy were enrolled. Images were reconstructed using 3 methods: (1) Filtered Back Projection (FBP), (2) Iterative Metal Artifact Reduction (iMAR), and (3) Adaptive Iterative Metal Artifact Reduction (AiMAR) strength 4. In multiple reading sessions, three radiologists graded visualization of critical anatomic structures and artifact severity (6-point scales, lower scores desirable), and diagnostic confidence in blinded fashion. They also graded patient-level confidence in malignancy based on imaging findings in each patient. Thirty-two patients (8 females) with a mean age of 74.5 ± 8.5years were included. The median (range) visualization scores for FBP, iMAR, and AiMAR were 3.6 (1.1-4.9), 1.6 (0.3-2.8), and 1.6 (0.3-2.6), respectively. Both iMAR and AiMAR had anatomicvisualization and artifact scores better than FBP (P < 0.001 for both) and similar to each other (P > 0.05). Structures with the most improvement in visualization score with the use of metal artifact reduction algorithms included theobturator internus muscle, internal and external iliac nodal chains, and vagina. iMAR and AiMAR improved diagnostic confidence (P < 0.001) and patient-level confidence in malignancy (P ≤ 0.24). For patients with hip prostheses and suspected bladder malignancy, the use of iMAR or AiMAR was shown to significantly reduce metal artifacts, thus improving diagnostic confidence and patient-level confidence in malignancy.

Query Optimization for Distributed Spatio-Temporal Sensing Data Processing

The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.

Adaptive Antenna Diagnosis Based on Clustering Block Sparse Bayesian Learning

Massive multiple-input multiple-output systems in military or harsh environments are vulnerable to blockage or damage, changing the geometry of the array and distort the far-field radiation pattern of the array. Therefore, the blocked array elements must be diagnosed in time under a low signal-to-noise ratio. We propose an adaptive iterative diagnostic algorithm based on clustering block sparse Bayesian learning (CBSBL) to address the coupling problem of the direction of arrival (DOA) and antenna blockage estimations. We construct sparse signals by approximating the difference between the radiation pattern of the fault-free reference antenna and the antenna under test with blockage (or damage). Based on the sparse characteristics of the blocked antennas and the correlation characteristics of adjacent coefficients, the structural clustering method is used to deal with the sparse coefficients. The estimation accuracy is improved by encouraging the dependence between the adjacent coefficients by accurately controlling the neighboring hyperparameters. The proposed algorithm provides satisfactory results under the premise of unknown DOA.

Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms

Due to the merits of self-stability and low energy consumption, high-temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes five different neural network models (radial basis function, deep neural network (DNN), convolution neural network, recurrent neural network, long short-term memory neural network (LSTM)) to predict HTS maglev guiding force and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptive iterative algorithms for the parameter matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. According to the results, it is revealed that the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90 000, respectively. Moreover, the K-fold cross-validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.

A Vortex Electromagnetic Imaging Method Based on Two-Dimensional Matrix Compensation for Off-Grid Targets

Vortex electromagnetic (EM) wave has shown great potential in the field of radar imaging. Under the assumption that the target is located at the grid point, the compressed sensing (CS) algorithm is applied to vortex EM imaging and achieves high imaging quality. However, the off-grid target in the actual scene deviates from the grid points, resulting in the basis mismatch problem. This problem will lead to the mismatch between the assumed measurement matrix and the real echo signal and the degradation of the CS imaging performance. In order to solve this problem, a novel two-dimensional (2-D) joint off-grid imaging method is proposed by considering the gridding error as a multiplicative matrix and separating it via series expansion. This method compensates for errors in 2-D matrix directly instead of converting the 2-D matrix into the 1-D vector, which can not only solve the off-grid problem, but also reduce the storage of data acquisition and processing. Furthermore, the 2-D iterative adaptive algorithm (2DIAA) for the 2-D model is adopted to achieve better reconstruction performance and lower computational complexity. Finally, Simulation results show the effectiveness and robustness of the algorithm.

A two-stage location-sensitive and user preference-aware recommendation system

Nowadays, cloud customers use cloud services increasingly to satisfy their demands. Usually, a significant number of customers are immature and inexpert and cannot express their needs accurately and numerically. They usually express their needs verbally and in the form of linguistic terms. On the other hand, the experienced customers express their needs numerically and accurately. In this situation, a recommendation system can be considered as one of the most useful ideas to support all type of customers. However, current recommendation systems (e.g., collaborative filtering based recommendations) meet customer requests that are accurately and numerically expressed. To support all types of customers, the construction of a strong recommendation system to analysis the demands expressed by customers (experienced and inexperienced) and to recommend suitable services is vital. As another important matter, cloud customers and services have been geographically distributed. Identifying the location of customers and services has a significant effect on the quality of services offered to customers. Therefore, the recommendation system should consider the location of customers and services in order to provide better services. In this paper, we introduce an efficient method to construct a powerful recommendation system which can provide suitable services considering the preferences of the customer and their location. The proposed recommendation system comprises two algorithms. The first algorithm is a fuzzy clustering algorithm, named FCA, that can well classify the location of customers and services. The second algorithm is an iterative adaptive neural-fuzzy algorithm, named IANFRA, which receives the preferences of the customer along with their location and identifies suitable services based on the locations clustered by FCA and the demands of customers (experienced and inexperienced). Finally, the feasibility of the proposed method has validated in terms of accuracy and scalability through conducting extensive experiments on a real distributed service quality dataset WS-DREAM. The evaluation results illustrate that both the service recommendation accuracy in the prediction of quality of services and the scalability, when the volume of the dataset is huge, have been improved.

An Adaptive Filtering Algorithm in Pulse-Doppler Radar for Counteracting Range-Velocity Jamming

In this paper, an anti-jamming scheme based on transmitted multiple pulses through random initial phases and adaptive iterative filtering algorithm is considered for counteracting range-velocity deceptive jamming in Pulse-Doppler (PD) radar system. This anti-jamming scheme relies on the fact that the digital radio frequency memory (DRFM) repeated jammer needs more than one pulse repetition interval PRI (at least one PRI) to identify the radar transmitted pulses, and therefore uses the captured pulses of the lag PRIs to reproduce multiple false targets. As a consequence, an adaptive-iterative algorithm to estimate the range-Doppler plane of the real targets in range dimension processing and Doppler dimension processing can appropriately suppress the deceptive false targets, which employed the lagged PRIs for the radar deception. Firstly, the proposed method achieves the optimal estimation of the true targets and false targets in the range dimension. Then, with the estimation information in the range dimension, the presented method estimates the range-Doppler plane of the true targets and false targets. Based on these estimations the deceptive jamming can be easily suppressed with few numbers of iterations and outstanding suppression performance. Finally, we evaluate the performance of the proposed method as well as achieving a good trade-off between anti-jamming performance and computational complexity via numerical simulations.

An adaptive defect detection method for LNG storage tank insulation layer based on visual saliency

As the core equipment of the industrial chain, the LNG storage tank is developing in the direction of large-scale and increasing attention has been paid to its economy and safety. The defects of the insulation layer not only affect the insulation performance of the tank but also easily induce safety accidents. To automatically detect these defects, considering the large temperature difference between the defect area and other areas, an adaptive detection method based on infrared thermal imaging technology and a saliency detection algorithm is proposed. Firstly, the median filtering and the Contrast Limited Adaptive Histogram (CLAHE) are introduced to reduce noise and smooth the image. Then, the Adaptive Simple Linear Iterative Clustering algorithm (A-SLIC) based on the entropy of the image is purposed for super-pixel segmentation. After super-pixel segmentation, the saliency map is obtained by manifold ranking, and the salient areas that contain defects of the insulation layer will be segmented automatically. The proposed method is compared with other traditional algorithms on the MSRA1000 dataset and the infrared thermal imaging dataset of LNG storage tanks. The results of the experiments illustrate the effectiveness of the method proposed.

Time and space-variant system reliability analysis through adaptive Kriging and weighted sampling

The performance of complex engineering systems often changes over time and space, potentially leading to multiple failure modes. Such time and space variance presents a challenge to reliability analysis and design methods. To address this issue, this paper first analyzes three categories of approaches to four types of time-variant reliability problems, and then develops an adaptive uniform design-based Kriging (AUDK) framework with an improved weighted sampling method (IWSM), termed the AUDK-IWSM. In particular, a Kriging response surface of the extreme function for time and space-variant limit states is constructed, and then an existing weighted sampling method is improved for high precision to prepare the sampling points for reliability analysis on the Kriging surface. To construct the AUDK model and update it with the most probable point, an adaptive iterative algorithm is developed based on the IWSM. Thus, through integrating the reliability index of the generated Kriging response surface, the resultant AUDK-IWSM algorithm improves reliability calculations and enhances computational efficiency of the reliability analysis. The AUDK-IWSM is illustrated, and its effectiveness is verified through four case studies for engineering practice, in comparison with a number of state-of-the-art techniques reported in the literature.

Critic-observer-based decentralized force/position approximate optimal control for modular and reconfigurable manipulators with uncertain environmental constraints

A critic-observer decentralized force/position approximate optimal control method is presented to address the joint trajectory and contacted force tracking problem of modular and reconfigurable manipulators (MRMs) with uncertain environmental constraints. The dynamic model of the MRM systems is formulated as an integration of joint subsystems via extensive state observer (ESO) associated with the effect of interconnected dynamic coupling (IDC). A radial basis function neural network (RBF-NN) is developed to deal with the IDC effects among the independent joint subsystems. Based on adaptive dynamic programming (ADP) approach and policy iteration (PI) algorithm, the Hamilton–Jacobi–Bellman (HJB) equation is approximately solved by establishing critic NN structure and then the approximated optimal control policy can be derived. The closed-loop manipulator system is proved to be asymptotic stable by using the Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness and advantages of the proposed control method.

Quantification of airway dimensions using a high-resolution CT scanner: A phantom study.

Small airways with inner diameters less than 2mm are sites of major airflow limitations in patients with chronic obstructive pulmonary disease (COPD) and asthma. The purpose of this study is to investigate the limitations for accurate assessment of small airway dimensions using both high-resolution CT (HRCT) and conventional normal-resolution CT at low dose levels. To model the normal human airways from the 3rd to 20th generations, a cylindrical polyurethane phantom with 14 airway tubes of inner diameters (ID) ranging from 0.3 to 3.4mm and wall thicknesses (WT) ranging from 0.15 to 1.6mm was placed within an Anthropomorphic QRM-Thorax phantom. The Aquilion Precision (Canon Medical Systems Corporation) HRCT scanner was used to acquire images at 80, 100, and 120kV using high resolution mode (HR, 0.25mm×160 detector configuration) and normal-resolution (NR) mode (0.5mm×80 detector configuration). The HR data were reconstructed using a 1024×1024matrix (0.22×0.22×0.25mm voxel size) and the NR data were reconstructed using a 512×512matrix (0.43×0.43×0.50mm). Two reconstruction algorithms (filtered back projection; FBP and an adaptive iterative dose reduction 3D algorithm; AIDR 3D) and three reconstruction kernels (FC30, FC52, and FC56) were investigated. The dose values ranged from 0.2 to 6.2mGy. A refined automated full-width half-maximum (FWHM) method was used for the measurement of airway dimensions, where the density profiles were computed by radial oversampling using a polar coordinate system. Both ID and WT were compared to the known dimensions using a regression model, and the root-mean-square error (RMSE) and average error were computed across all 14 airway tubes. The results indicate that the ID can be measured within a 15% error down to approximately 0.8 and 2.0mm using the HR and NR modes, respectively. The overall RMSE (and average error) of ID measurements for HR and NR were 0.10mm (-0.70%) and 0.31mm (-2.63%), respectively. The RMSE (and average error) of WT measurements using HR and NR were 0.10mm (23.27%) and 0.27mm (53.56%), respectively. The WT measurement using HR yielded a factor of two improvement in accuracy as compared to NR. High-resolution CT can provide more accurate measurements of airway dimensions as compared with NR CT, potentially improving quantitative assessment of pathologies such as COPD and asthma. The HR mode acquired and reconstructed with AIDR3D and the FC52kernel provides most accurate measurement of airway dimensions. Low-dose HR measurements at dose level above 0.9mGy can provide improved accuracy on both inner diameters and wall thicknesses compared to full dose NR airway phantom measurements.

Parameter Estimation of Partially Observed Turbulent Systems Using Conditional Gaussian Path-Wise Sampler

Parameter estimation of complex nonlinear turbulent dynamical systems using only partially observed time series is a challenging topic. The nonlinearity and partial observations often impede using closed analytic formulae to recover the model parameters. In this paper, an exact path-wise sampling method is developed, which is incorporated into a Bayesian Markov chain Monte Carlo (MCMC) algorithm in light of data augmentation to efficiently estimate the parameters in a rich class of nonlinear and non-Gaussian turbulent systems using partial observations. This path-wise sampling method exploits closed analytic formulae to sample the trajectories of the unobserved variables, which avoid the numerical errors in the general sampling approaches and significantly increase the overall parameter estimation efficiency. The unknown parameters and the missing trajectories are estimated in an alternating fashion in an adaptive MCMC iteration algorithm with rapid convergence. It is shown based on the noisy Lorenz 63 model and a stochastically coupled FitzHugh–Nagumo model that the new algorithm is very skillful in estimating the parameters in highly nonlinear turbulent models. The model with the estimated parameters succeeds in recovering the nonlinear and non-Gaussian features of the truth, including capturing the intermittency and extreme events, in both test examples.

The Utility of PET/CT Metabolic Parameters Measured Based on Fixed Percentage Threshold of SUVmax and Adaptive Iterative Algorithm in the New Revised FIGO Staging System for Stage III Cervical Cancer.

Objectives: The main aim of this study was to evaluate the differences in metabolic parameters of positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro-D-glucose integrated with computed tomography (18F-FDG PET/CT) measured based on fixed percentage threshold of maximum standard uptake value (SUVmax) and adaptive iterative algorithm (AT-AIA) in patients with cervical cancer. Metabolic parameters in stage III patients subdivided into five groups according to FIGO and T staging (IIIB-T3B, IIIC1-T2B, IIIC1-T3B, IIIC2-T2B, IIIC2-T3B) were compared.Methods: In total, 142 patients with squamous cell cervical cancer subjected to 18F-FDG-PET/CT before treatment were retrospectively reviewed. SUVmax, mean standard uptake value (SUVmean), maximum glucose homogenization (GNmax), mean glucose homogenization (GNmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and glucose homogenization total lesion glycolysis (GNTLG) values measured based on the above two measurement methods of all 142 patients (IIB-IVB) and 102 patients in the above five groups were compared.Results: MTV measured based on fixed percentage threshold of SUVmax was lower than that based on AT-AIA (p < 0.05). MTV40%, MTV0.5, TLG0.5, GNTLG40%, and GNTLG0.5 values were significantly different among the five groups (p < 0.05) while the rest parameters were comparable (p > 0.05). All metabolic parameters of group IIIB-T3B were comparable to those of the other four groups. MTV40%, MTV0.5, GNTLG40%, and GNTLG0.5 in group IIIC1-T2B relative to IIIC1-T3B and those of group IIIC2-T2B relative to group IIIC2-T3B were significantly different. All metabolic parameters of group IIIC1-T2B relative to IIIC2-T2B and those of group IIIC1-T3B relative to group IIIC2-T3B were not significantly different.Conclusion: Metabolic parameters obtained with the two measurement methods showed a number of differences. Selection of appropriate methods for measurement of 18F-FDG-PET/CT metabolic parameters is important to facilitate advances in laboratory research and clinical applications. When stage III patients had the same T stage, their metabolic parameters of local tumor were not significantly different, regardless of the presence or absence of lymph node metastasis, location of metastatic lymph nodes in the pelvic cavity or para-abdominal aorta. These results support the utility of the revised FIGO system for stage III cervical cancer, although our T-staging of stage III disease is incomplete.

Nonlocal low-rank regularized two-phase approach for mixed noise removal

Removing mixed noise in images is a difficult problem which has been discussed in many recent papers. In this paper, we tackle the problem of having mixed additive Gaussian white noise and impulse noise. We propose to remove this mixed noise through a nonlocal low-rank regularized two-phase approach. In the first phase, we identify and label the pixels that are likely to be corrupted by the impulse noise. In the second phase, the image is restored through the unlabeled observed data. The restored image is achieved via solving an optimization problem whose objective function has an ℓ 1/ℓ 2 combined content-dependent fidelity term and a nonconvex nonlocal low-rank regularization term. Both terms are built on patch matrices formed from similar patches. Each patch matrix is considered to be low-rank according to the prior knowledge of images. We solve this nonconvex optimization through an iterative adaptive nuclear norm minimization algorithm and provide its convergence analysis. Our experiments show the proposed method outperforms the existing state-of-the-art algorithms in terms of three quantitative metrics, namely, the peak signal-to-noise ratio, the structural similarity and the feature similarity, and visual quality of the restored images.

Adaptive Normal State-Space Notch Digital Filters: Algorithm and Frequency-Estimation Bias Analysis

This paper investigates an adaptive notch digital filter that employs normal state-space realization of a single-frequency second-order IIR notch digital filter. An adaptive algorithm is developed to minimize the mean-squared output error of the filter iteratively. This algorithm is based on a simplified form of the gradient-decent method. Stability and frequency estimation bias are analyzed for the adaptive iterative algorithm. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the proposed adaptive notch digital filter and the frequency-estimation bias analyzed for the adaptive iterative algorithm.

A Novel Adaptive Differential Privacy Algorithm for Empirical Risk Minimization

Privacy-preserving empirical risk minimization model is crucial for the increasingly frequent setting of analyzing personal data, such as medical records, financial records, etc. Due to its advantage of a rigorous mathematical definition, differential privacy has been widely used in privacy protection and has received much attention in recent years of privacy protection. With the advantages of iterative algorithms in solving a variety of problems, like empirical risk minimization, there have been various works in the literature that target differentially private iteration algorithms, especially the adaptive iterative algorithm. However, the solution of the final model parameters is imprecise because of the vast privacy budget spending on the step size search. In this paper, we first proposed a novel adaptive differential privacy algorithm that does not require the privacy budget for step size determination. Then, through the theoretical analyses, we prove that our proposed algorithm satisfies differential privacy, and their solutions achieve sufficient accuracy by infinite steps. Furthermore, numerical analysis is performed based on real-world databases. The results indicate that our proposed algorithm outperforms existing algorithms for model fitting in terms of accuracy.

Adaptive sliding mode control for 2D nonlinear Fornasini–Marchesini model subject to quantisation and packet dropouts

This paper aims to solve the sliding mode control issue for the discrete nonlinear two-dimensional (2D) Fornasini–Marchesini second (FMII) model under the influence of quantisation error and stochastic packet loss. Firstly, an innovative stochastic 2D FMII sliding mode control model is constructed by considering quantisation error and Bernoulli packet loss process. And then we study the stability issue and formulate the corresponding stability criterion by virtue of the 2D sliding mode surface and a 2D sliding mode control law with data compensation. Subsequently, the reachability for the 2D sliding surface is verified by a innovative and executable 2D sliding mode control law. Besides, the article also formulates the adaptive intelligent iterative algorithms for the sliding mode surface gain and 2D sliding mode control algorithm, respectively. To conclude the paper, an example is provided to analyse its SMC problem under quantisation and stochastic packet loss. This example also shows the effectiveness of the theorems and algorithms proposed in this paper.

QoS-Guaranteed Adaptive Modulation and Coding for Wireless Scalable Video Multicast

Multicast is a promising solution to address spectrum scarcity in wireless video streaming, while facing tough challenge in provisioning quality of service (QoS) to heterogeneous users. This article presents an adaptive modulation and coding (AMC) scheme to accommodate both spatial diversity and temporal variation on users’ channel conditions for QoS provisioning in wireless scalable video multicast. The QoS-guaranteed AMC policy optimization is formulated as constrained stochastic optimization based on Markov decision processes. An adaptive policy iteration algorithm is developed to find the optimal AMC policy online without any prior knowledge of users’ channel statistics or intensive calculations. This algorithm is attractive due to adaptability to unknown environments and less run-time computation expenditure. Simulations results demonstrate the effectiveness of the proposed scheme.