Component-scaled signal reconstruction for enhanced noise filtration

This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

Optimal Linear Estimation (OLE) Modeling Supports Early Holocene (9000–8000 RCYBP) Copper Tool Production in North America

The discovery and development of metal as a tool medium is a topic of global interest. A fundamental research goal involves establishing the timing of human experimentation with naturally occurring copper ore, which is commonly associated with sedentary, agrarian-based societies. However, in North America, there is well-documented millennia-scale exploitation of copper as tool media by small, seasonally mobile hunter-gatherer groups in the western Great Lakes. Archaeologists have suggested that Late Paleoindian groups may have begun using copper as a tool medium almost immediately after they entered the Lake Superior basin. However, only a few radiocarbon dates support such early use of copper. Here, we use optimal linear estimation modeling to infer the origin date for copper tool production in North America. Our results show that the invention of copper as a tool media likely occurred shortly after the first pioneering populations encountered copper ore during the Pleistocene-Holocene transition. The origin dates modeled here (ca. 8100 RCYBP) reveal several important features about the behavior of pioneering hunter-gatherer populations. Moreover, our results suggest that this phenomenon represents the earliest known use of metal for utilitarian copper tool production.

A Comparison of Different Folding Models in Variations of the Map Folding Problem

In this paper, we compare the performance of three different folding models when they are applied to three different map folding settings. Precisely, the three folding models include the simple folding model, the simple folding–unfolding model, and the general folding model. The different map folding settings are discussed by comparing different folded states, i.e., different overlapping orders on the set of the squares of 1 × n maps, the squares of m × n maps, and the squares lying on the boundary of m × n maps. These folding models are abstracts of manual works and robotics. We clarify the relationship between their reachable final folded states under different settings and give proof of all the inclusion relationships between every two of these sets. In addition, there are nine distinct problems with the three folding models applied to three folding settings. We give the optimal linear time solutions to all the unsolved ones: the valid total overlapping order problems of 1 × n maps, m × n maps, as well as the valid boundary overlapping order problems of m × n maps with the three different folding models. Our work gives the conclusion of the research field where the folding models and the overlapping orders of map folding are concerned.

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

In this paper, a neuroadaptive robust trajectory tracking controller is utilized to reduce speed ripples of permanent magnet synchronous machine (PMSM) servo drive under the presence of a fracture or fissure in the rotor and external disturbances. The dynamics equations of PMSM servo drive with the presence of a fracture and unknown frictions are described in detail. Due to inherent nonlinearities in PMSM dynamic model, in addition to internal and external disturbances; a traditional PI controller with fixed parameters cannot correctly regulate the PMSM performance under these scenarios. Hence, a neuroadaptive robust controller (NRC) based on a category of on-line trained artificial neural network is used for this purpose to enhance the robustness and adaptive abilities of traditional PI controller. In this paper, the moth-flame optimization algorithm provides the optimal weight parameters of NRC and three PI controllers (off-line) for a PMSM servo drive. The performance of the NRC is evaluated in the presence of a fracture, unknown frictions, and load disturbances, likewise the result outcomes are contrasted with a traditional optimized PID controller and an optimal linear state feedback method.

Learned Hyperspectral Compression Using a Student’s T Hyperprior

Hyperspectral compression is one of the most common techniques in hyperspectral image processing. Most recent learned image compression methods have exhibited excellent rate-distortion performance for natural images, but they have not been fully explored for hyperspectral compression tasks. In this paper, we propose a trainable network architecture for hyperspectral compression tasks, which not only considers the anisotropic characteristic of hyperspectral images but also embeds an accurate entropy model using the non-Gaussian prior knowledge of hyperspectral images and nonlinear transform. Specifically, we first design a spatial-spectral block, involving a spatial net and a spectral net as the base components of the core autoencoder, which is more consistent with the anisotropic hyperspectral cubes than the existing compression methods based on deep learning. Then, we design a Student’s T hyperprior that merges the statistics of the latents and the side information concepts into a unified neural network to provide an accurate entropy model used for entropy coding. This not only remarkably enhances the flexibility of the entropy model by adjusting various values of the degree of freedom, but also leads to a superior rate-distortion performance. The results illustrate that the proposed compression scheme supersedes the Gaussian hyperprior universally for virtually all learned natural image codecs and the optimal linear transform coding methods for hyperspectral compression. Specifically, the proposed method provides a 1.51% to 59.95% average increase in peak signal-to-noise ratio, a 0.17% to 18.17% average increase in the structural similarity index metric and a 6.15% to 64.60% average reduction in spectral angle mapping over three public hyperspectral datasets compared to the Gaussian hyperprior and the optimal linear transform coding methods.

Nutritional Assessment and Management in Paediatric Chronic Kidney Disease

Nutrition in paediatrics has always been one of the most important factors for optimal growth. Children with chronic kidney disease (CKD) need special consideration for better long-term outcomes, including nutritional status, optimal height, and cognitive function. Nonetheless, there are many obstacles to overcome to attain optimal linear growth and nutritional status in children with CKD. This review highlights the need for tools to assess the growth parameters in CKD. In addition, recommendations for dietary intake play a major role in controlling electrolyte disturbances in patients with CKD. For example, it is still unclear whether it is better to restrict phosphate sources in inorganic, organic, or food additives. The review also summarises different factors such as fluid intake, route of feeding, and essential nutrients that require particular attention in paediatric patients with CKD. In summary, a multidisciplinary team is needed to devise individual nutritional plans to achieve the best outcome and improve the quality of life of patients.

Methodology of determining of the transfer function of engagement kinematics of accelerations of an aircraft and its elliptic coordinates in alternative representation

In this work, simplified expressions have been obtained that describe the kinematics parameters of the aircraft movement and its accelerations. These expressions are needed to obtain an optimal linear control law in order to provide movement of the aircraft using the hyperbola guidance method with a time difference of arrival navigation system. The key feature of the hyperbola navigation method is the ability to reduce the number of navigation positions by one when the on-board navigation equipment operates in a passive mode, carrying out only the reception of navigation information, like consumers of satellite navigation information.