A Study on Distortion Estimation Based on Image Gradients

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.

Connectivity Estimation Approaches for Internet of Things-Enabled Wireless Sensor Networks

Internet of things (IoT) envisions a network of billions of devices having various hardware and software capabilities communicating through internet infrastructure to achieve common goals. Wireless sensor networks (WSNs) having hundreds or even thousands of sensor nodes are positioned at the communication layer of IoT. In this study, the authors work on the connectivity estimation approaches for IoT-enabled WSNs. They describe the main ideas and explain the operations of connectivity estimation algorithms in this chapter. They categorize the studied algorithms into two divisions as 1-connectivity estimation algorithms (special case for k=1) and k-connectivity estimation algorithms (the generalized version of the connectivity estimation problem). Within the scope of 1-connectivity estimation algorithms, they dissect the exact algorithms for bridge and cut vertex detection. They investigate various algorithmic ideas for k connectivity estimation approaches by illustrating their operations on sample networks. They also discuss possible future studies related to the connectivity estimation problem in IoT.

Cooperative estimation algorithms for multi-sensor networks with imprecise measurements

A cooperative estimation algorithm is proposed for mutli-sensor networks with imprecise measurements caused by electromagnetic interferences, abnormal currents and other faults in the multi-sensor measurement process. Adaptive schemes based on a reference model are introduced to overcome the adverse effects of multiplicative interference on the estimated information. Then, rigorous theoretical proofs are developed to analyze the adaptive estimation algorithm. Finally, numerical simulation results are carried out to verify the effectiveness of the theoretical analysis.

RGB-D-Based Method for Measuring the Angular Range of Hip and Knee Joints during Home Care Rehabilitation

Home-based rehabilitation is becoming a gold standard for patient who have undergone knee arthroplasty or full knee replacement, as it helps healthcare costs to be minimized. Nevertheless, there is a chance of increasing adverse health effects in case of home care, primarily due to the patients’ lack of motivation and the doctors’ difficulty in carrying out rigorous supervision. The development of devices to assess the efficient recovery of the operated joint is highly valued both for the patient, who feels encouraged to perform the proper number of activities, and for the doctor, who can track him/her remotely. Accordingly, this paper introduces an interactive approach to angular range calculation of hip and knee joints based on the use of low-cost devices which can be operated at home. First, the patient’s body posture is estimated using a 2D acquisition method. Subsequently, the 3D posture is evaluated by using the depth information coming from an RGB-D sensor. Preliminary results show that the proposed method effectively overcomes many limitations by fusing the results obtained by the state-of-the-art robust 2D pose estimation algorithms with the 3D data of depth cameras by allowing the patient to be correctly tracked during rehabilitation exercises.

An Algorithm for Estimating the Signal Frequency at the Output of a Channel with a Controlled Information Flow under Phase Noise Conditions

Research in the field of efficient frequency estimation algorithms is of great interest. The reason for this is the redistribution of the role of additive and phase noise in many modern radio-engineering applications. An example is the area of measuring radio devices, which usually operate at high signal-to-noise ratios (SNR). The estimation error is largely determined not by the broadband noise, but by the frequency and phase noise of the local oscillators of the receiving and transmitting devices. In particular, earlier works \\cite{Nikiforov} proposed an efficient computational algorithm for estimating the frequency of a quasi-harmonic signal based on the iterative calculation of the autocorrelation sequence (ACS). In \\cite{Volkov}, this algorithm was improved and its proximity to the Rao-Cramer boundary was shown (the sources of this noise are master oscillators and frequency synthesizers). Possibilities of frequency estimation in radio channels make it possible to significantly expand the functionality of the entire radio network. This can include, for example, the problem of adaptive distribution of information flows of a radio network. This also includes the tasks of synchronization and coherent signal processing. For these reasons, more research is needed on this algorithm, the calculation of theoretical boundaries and their comparison with the simulation results.

A computational scheme to estimate the Leontief model matrix coefficients according to input-output table data for the southern regions of the Tyumen Oblast

Subject. This article deals with the control and management aspects of regional development on the basis of Leontief’s balance model. Objectives. The article aims to develop schemes for stable estimation of aggregate parameters of region balance models based on a shortened sample of input-output statistical data and rules for their subsequent regularization. Methods. For the study, we used multiple forms of regional economic balance model transformation based on the aggregation of data of the selected regional subsystems. Results. The primary estimates of aggregate input-output matrix for the southern regions of the Tyumen Oblast were obtained from the statistical input-output data for 2014–2018. To comply with the productivity conditions, additional information was introduced into the estimation algorithm reflecting the balance dependence for the reference input-output matrix for the Russian Federation and for the southern regions of the Tyumen Oblast in retrospective (2004–2013). Conclusions. The result of regularization of aggregate input-output matrix for the southern regions of the Tyumen Oblast obtained from the statistical input-output data on the basis of the least squares method indicates that the backward estimation technique cannot act as a basic tool for the primary construction of balance models of regional economies. However, backward estimation algorithms with subsequent regularization are effective in correcting the reference input-output matrix using actual data of the region’s socio-economic development.

Estimation of the Conditional Probability Using a Stochastic Gradient Process

The use of conditional probabilities has gained in popularity in various fields such as medicine, finance, and imaging processing. This has occurred especially with the availability of large datasets that allow us to extract the full potential of the available estimation algorithms. Nevertheless, such a large volume of data is often accompanied by a significant need for computational capacity as well as a consequent compilation time. In this article, we propose a low-cost estimation method: we first demonstrate analytically the convergence of our method to the desired probability and then we perform a simulation to support our point.

A Velocity-Independent DOA Estimator of Underwater Acoustic Signals Via An Arbitrary Cross-Linear Nested Array

In this paper, an estimator for underwater DOA estimation is proposed by using a cross-linear nested array with arbitrary cross angle. The estimator excludes the variation acoustic velocity by deriving the geometric relation of the cross-linear array on the proposed algorithm. Therefore, compared with traditional DOA estimation algorithms via linear array, this estimator eliminates systematic errors caused by the uncertainty factor of the acoustic velocity in the underwater environment. Compared with the traditional acoustic velocity independent algorithm, this estimator uses the nested array and improves the performance of DOA estimation. In addition, the estimator is based on arbitrary angle of the cross-linear array, so it is more flexible in practical applications. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance in underwater environments where the actual acoustic velocity is not accurate.

Cross-Category Retailing Management: Substitution and Complementarity

Problem definition: This paper studies pricing and assortment management for cross-category products, a common practice in brick-and-mortar retailing and e-tailing. Academic/practical relevance: We investigate the complementarity effects between the main products and the secondary products, in addition to the substitution effects for products in the same category. Methodology: In this paper, we develop a multistage sequential choice model, under which a consumer first chooses a main product and then selects a secondary product. The new model can alleviate the restriction of the independence of irrelevant alternatives property and allows more flexible substitution patterns and also takes into account complementarity effects. Results: We characterize the impact of the magnitude of complementarity effects on pricing and assortment management. For the problems that are hard to solve optimally, we propose simple heuristics and establish performance guarantee. In addition, we develop easy-to-implement estimation algorithms to calibrate the proposed sequential choice model by using sales data. Managerial implications: We show that ignoring or mis-specifying complementarity effects may lead to substantial losses. The methodologies on modeling, optimization, and estimation have potential to make an impact on cross-category retailing management.

Properties and Applications of the Modified Kies–Lomax Distribution with Estimation Methods

The present study introduces a new three-parameter model called the modified Kies–Lomax (MKL) distribution to extend the Lomax distribution and increase its flexibility in modeling real-life data. The MKL distribution, due to its flexibility, provides left-skewed, symmetrical, right-skewed, and reversed-J shaped densities and increasing, unimodal, decreasing, and bathtub hazard rate shapes. The MKF density can be expressed as a linear mixture of Lomax densities. Some basic mathematical properties of the MKF model are derived. Its parameters are estimated via six estimation algorithms. We explore their performances using detailed simulation results, and the partial and overall ranks are provided for the measures of absolute biases, mean square errors, and mean relative errors to determine the best estimation method. The results show that the maximum product of spacings and maximum likelihood approaches are recommended to estimate the MKL parameters. Finally, the flexibility of the MKL distribution is checked using two real datasets, showing that it can provide close fit to both datasets as compared with other competing Lomax models. The three-parameter MKL model outperforms some four-parameter and five-parameter rival models.