This paper investigates the problem of sliding mode control (SMC) design under the finite-state Markov digital block-fading channels. Before transmitting through the independent digital channels, the output measurements are first quantized through logarithmic quantizers. Then, p independent finite-state hidden Markov models are introduced to model the time-varying effects subject to error-free packet

This paper investigates the decentralized tracking control problem for a class of strict-feedback interconnected nonlinear systems with unknown parameters, where the system states are unmeasurable and the interconnections are unknown. Different from the existing results, where the output is available all the time, we consider the case that the output is only available at the sampled instants, which

The goal of this paper is the presentation of the elementary procedures that normally are done in nonrelativistic Quantum Mechanics in terms of the principles of Machine Learning. In essence, this paper discusses Mitchell’s criteria, whose block fundamental dictates that the universal evolution of any system is composed by three fundamental steps: (i) Task, (ii) Performance and (iii) Experience. In

ABSTRACT The U.S. Department of Agriculture’s Agricultural Research Service (USDA-ARS) maintains seven in situ soil moisture networks throughout the continental United States, some since 2002. These networks are crucial for understanding the spatial and temporal extent of droughts in their historical context, parameterization of hydrologic models, and local agricultural decision support. However, the

ABSTRACT Many users fail to stick to the use of mobile fitness apps. As a key element of gamification mechanisms, effective gamification narrative design has the potential to draw the users in and motivates them to develop stronger motivations to achieve their real-world goals and higher persistent usage intentions of the mobile fitness App. Underpinned by goal priming theory, we conducted two controlled

A robust disturbance rejection control scheme is addressed for the trajectory tracking problem of a flexible-joint robot (FJR). The system is always severely affected by various types of unknown disturbances including model errors, couplings, changing working environments as well as unmodeled dynamics. These disturbances on the link and actuator side will deteriorate the control performance of FJR

Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its

Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst

The inability of a farmer to plant an insured crop by the policy’s final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such as the catastrophic flooding and widespread conditions that prevented active field work in the midwestern region of United States in 2019

Background: Youper is a widely used, commercially available mobile app that uses artificial intelligence therapy for the treatment of anxiety and depression. Objective: Our study examined the acceptability and effectiveness of Youper. Further, we tested the cumulative regulation hypothesis, which posits that cumulative emotion regulation successes with repeated intervention engagement will predict

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively

Background: Considerable effort has been devoted to the development of artificial intelligence, including machine learning–based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high-quality, cost-effective care. Policy analysts, ethicists, and computer scientists have identified

Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal

Knowledge of how to design digital systems that are ergonomically sound, high in usability, and optimized for the user, context, and task has existed for some time. Despite this, there are still too many examples of new digital health care systems that are poorly designed and that could negatively affect both the work environment of health care staff and patient safety. This could be because of a gap

On the Keweenaw Peninsula of Lake Superior, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes (MMT) of tailings (1901-1932) into the coastal zone off the town of Gay. Migrating along the shoreline, ca. 10 MMT of the tailings dammed stream and river outlets, encroached upon wetlands, and contaminated recreational beaches. A nearly equal amount of tailings moved across bay

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine

Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs

With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance

By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution

Mapping building structures is crucial for environmental change and impact assessment, and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. In this regard, the objective of this study is to develop novel and robust methods using time-series data acquired from Sentinel-1 synthetic aperture radar (SAR) to identify and map persistent building structures

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and

With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS)

In the past decades, Bragg scattering has been considered to be an important scattering mechanism of microwave backscattering from sea surfaces. However, as reported in many recent literatures, non-Bragg scattering (which is often attributed to wave breaking) also makes a significant impact on radar scattering, especially for Horizontal–Horizontal (HH) polarized radar signals. To date, we know far

Liquid bulk cargo (LBC) volume analysis has received considerably great attention recently since LBC is a valuable and high-demand cargo. Thus, it is important to establish an analysis system for LBC volume, as it can help inform strategies for port planning and management. Nevertheless, LBC volume analysis is a challenging task for researchers because trends in LBC volume are highly volatile and non-stationary

Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside

Feature selection aims at selecting important features that can enhance learning performance in data mining, pattern recognition, and machine learning. Filter feature selection methods offer computational efficiency and feature evaluation criteria, while feature interaction information, which may greatly help increase classification accuracy, is often ignored. In this work, we instead propose a novel

Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies. New method We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation, excluding

After the invention of Bitcoin as well as other blockchain-based peer-to-peer payment systems, the cryptocurrency market has rapidly gained popularity. Consequently, the volatility of the various cryptocurrency prices attracts substantial attention from both investors and researchers. It is a challenging task to forecast the prices of cryptocurrencies due to the non-stationary prices and the stochastic

Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used as a black box. This study aims to propose a manufacturing cost prediction process for 3D computer-aided design (CAD) models using explainable artificial intelligence. The proposed process can visualize the machining features

There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which

This paper presents a routing optimization model for a wide range of waste collection problems which allows for multiple depots with homogeneous, capacitated vehicles, intermediate stops at multiple processing facilities, and multiple pick-ups per waste collection location. By minimizing collection routing and vehicle investment costs, the model estimates the total transportation costs for a given

Transcription factor binding site (TFBS) is a DNA sequence that binds to transcription factor and regulates the transcription process of the gene. Although deep learning algorithms are superior to traditional methods in predicting transcription factor binding site, they often rely too much on negative sample data, which cannot be verified by experiment. In particular, a training model with such negative

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR)

Software testing is one of the most important methods of analyzing software quality assurance. This process is very time consuming and expensive and accounts for almost 50% of the software production cost. In addition to the cost problem, the nature of the test, which seeks errors in the program, is such that software engineers are not interested in doing the process, so we are looking to use automated

Volatility forecasting plays an integral role in risk management, investments and security valuation for all assets with uncertain future payoffs. We enrich the literature by presenting computationally intensive variations of the heterogeneous autoregressive (HAR) volatility model: the complete subset linear/quantile regression HAR models, HAR-CSLR and HAR-CSQR. Predictions of 1- to 22-day-ahead volatility

Glucose–insulin models, testing glucose sensors and support systems for health care decisions play an important role in synthesis of glucose control algorithms. In this work we propose an online glucose–insulin identification using the Recurrent High Order Neural Network (RHONN). Then, the model obtained is used to predict n-steps forward of glucose levels, also by RHONN. The used data for identification

The need of developing systems that address traffic matters such as traffic control and accidents has led to the development of a number of techniques, methods and tools to address road usability issues. Many automated models have been proposed and applied in different scenarios such as road oddities. Despite of the advantages of these models the major problem of road accidents continue to pose problem

The idea of Innovation Search, which was initially proposed for data clustering, was recently used for outlier detection. In the application of Innovation Search for outlier detection, the directions of innovation were utilized to measure the innovation of the data points. We study the Innovation Values computed by the Innovation Search algorithm under a quadratic cost function and it is proved that

Quantum synchronizable codes (QSCs) are special quantum error-correcting codes that can correct the effects of both quantum noise on qubits and misalignment in block synchronization. In this paper, using the factorizations of cyclotomic polynomials \({{\varPhi }}_{p_{1}p_{2}}(x)\), where p1 and p2 are distinct odd primes, we give a new method to construct QSCs whose synchronization capabilities can

The P-difference between two sets A and B is the set of all points, C, such that the sum of B to any of the points in C is contained in A. Such a set difference plays an important role in robust model predictive control and set-theoretic control. In this paper, we show that an inner approximation of the P-difference between two sets described by collections of polynomial inequalities can be computed

The ϵ-dependent Lyapunov method is an effective technique to analyze both standard and nonstandard singularly perturbed systems, and the constructed ϵ-dependent Lyapunov function has an impact on the conservatism of the obtained results. This paper proposes a novel ϵ-dependent Lyapunov function, which includes three classes of common ϵ-dependent Lyapunov functions in the existing literature and can

Depression is a debilitating disorder associated with poor health outcomes, including increased comorbidity and early mortality. Despite the advent of new digital health interventions, few have been tested among patients with more severe forms of depression. As such, in an intent-to-treat study we examined whether 218 patients with at least moderately severe depressive symptoms (PHQ-9 ≥ 15) experienced

Determinations of alignments and station locations are major tasks in railway design, which jointly and fundamentally influence the construction and operation of the whole project. Currently, although considerable efforts have been invested into the separate optimization of alignments or station locations, the complex interactions between alignments and stations, as well as their concurrent impacts

This paper presents a methodology to control the trajectory of cooperative connected automated vehicles (CAVs) at roundabouts with a mixed fleet of CAVs and human-driven vehicles (HVs). We formulate an optimization program in a two-dimensional space for this purpose. A model predictive control-based solution technique is developed to optimize the trajectories of CAVs at discretized time steps based

In this paper, the optimal output tracking problem for a class of switched Boolean networks is investigated by using a graph-theoretical method. Firstly, by using the semitensor product of matrices, the algebraic equation of switched Boolean networks is formulated, and then by utilizing the concepts of set stabilization and switching invariant set, the trackability of a switched Boolean network is

This paper aims at designing, in the discrete-time setting and using sliding mode techniques, some controllers ensuring the tracking of desired references of lateral and yaw velocities for a ground vehicle with active front steering and rear torque vectoring actuators. The vehicle is described by an enhanced discrete-time model, recently presented in the literature. These three controllers have been

The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept

Recently, the Internet of Things (IoT) receives significant interest due to its rapid development. But IoT applications still face two challenges: heterogeneity and large scale of IoT data. Therefore, how to efficiently integrate and process these complicated data becomes an essential problem. In this article, we focus on the problem that analyzing variable dependencies of data collected from different

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies’ prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in the tropics. However

Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids. However, this frequent data processing may consume a lot of bandwidth, and even put the user’s privacy at risk. Although many privacy-preserving data aggregation schemes have been proposed to solve the problem, they still suffer from some security

In this article, we propose, LeaD, a new vibration-based communication protocol to Learn the unique patterns of vibration to Decode the short messages transmitted to smart IoT devices. Unlike the existing vibration-based communication protocols that decode the short messages symbol-wise, either in binary or multi-ary, the message recipient in LeaD receives vibration signals corresponding to bits-groups

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the