Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Seafloor Stretching in Mariana Trench Arc via the Squeeze and Excitation network with High-resolution Multibeam Bathymetric Survey

Multibeam bathymetry data could represent nearly continuous coverage depth measurements of the seafloor and reveal geomorphological regions. Recent studies have utilized multibeam bathymetry data to provide geological maps, but their delineations were done manually. Manual classification and delineation are inherently subjective and therefore can be inaccurate. In this paper, we try to develop one strategy to explore seafloor stretching in Mariana trench arc via squeeze and excitation network, combining data clustering, slope and gradient. In our experiments, we use the high-resolution multibeam bathymetric data collected by NOAA Office of Ocean Exploration and Research (OER). The geomorphological seabed in the Mariana region is automatically classified into different classes. The experimental results demonstrate that geomorphological seabed classification strategy achieves a robust, automated delineation approach.
An improved Canopy-FFCM clustering algorithm for ocean data analysis

With the further advancement of the research on marine environmental science, more and more attention has been paid to modern data monitoring technology. In order to obtain the current water quality information quickly, this paper combines the advantages of Canopy algorithm and FFCM algorithm, and proposes an improved Canopy-FFCM clustering algorithm. The algorithm firstly uses Canopy algorithm to quickly obtain the best clustering number, and then iterates through FFCM algorithm. Multiple simulations of the sample were performed by using the FCM algorithm, the FFCM algorithm, and the improved Canopy-FFCM algorithm, the experimental results show that compared with the traditional algorithm, the improved algorithm effectively reduces the time required for the analysis process and has good application value.
Network Scanning and Mapping for IIoT Edge Node Device Security

The amount of connected devices in the industrial environment is growing continuously, due to the ongoing demands of new features like predictive maintenance. New business models require more data, collected by IIoT edge node sensors based on inexpensive and low performance Microcontroller Units (MCUs). A negative side effect of this rise of interconnections is the increased attack surface, enabled by a larger network with more network services. Attaching badly documented and cheap devices to industrial networks often without permission of the administrator even further increases the security risk. A decent method to monitor the network and detect “unwanted” devices is network scanning. Typically, this scanning procedure is executed by a computer or server in each sub-network. In this paper, we introduce network scanning and mapping as a building block to scan directly from the Industrial Internet of Things (IIoT) edge node devices. This module scans the network in a pseudo-random periodic manner to discover devices and detect changes in the network structure. Furthermore, we validate our approach in an industrial testbed to show the feasibility of this approach.
Automotive Ethernet Applications Using Scalable Service-Oriented Middleware over IP: Service Discovery

This paper presents a simulated distributed embedded system consisting of two Automotive Ethernet applications based on the SOME/IP-SD (Scalable Service-Oriented Middleware over IP - Service Discovery) concept: angular position control of a DC motor actuator for an automobile's trunk lid and interior lights control based on the vehicle's doors state. The paper also proposes a different in-vehicle E/E (Electric and Electronic) architecture which contains only the scalable Ethernet network. For this reason, the paper motivates the need for Ethernet in Automotive and makes a comparison to conventional busses.
Fault tolerance in networked systems through flexible task assignment

This paper deals with networked control systems in which all subsystems have to fulfill a cooperative task. Based on the capabilities of each subsystem, a coordination unit evaluates how much a particular subsystem can contribute to the cooperative task. In case of a fault, the contribution of the faulty subsystem is re-evaluated and adjusted if necessary. As a result, the proposed method ensures the fulfillment of the cooperative task even in case of faults. A numerical example illustrates the functionality for a transportation system.
Trajectory control of the wheeled mobile robots in dynamic environment

This article deals with the problem of the trajectory control of wheeled mobile robots in presence of moving external objects. A procedure of the controllers design based on stabilisation of geometric manifolds using transformation of the mathematical model to the task-oriented basis without velocities measure is proposed. Efficiency of proposed algorithms is proven by numerical simulation results and experimental implementation on wheeled mobile robot.
Loop Closure Detection Through Environmental Indicators In Underwater SLAM

A key step in Simultaneous Localization and Mapping (SLAM) with autonomous robots is the ability to recognize when the vehicle is revisiting an area. The recognition of salient features allows re-localization to a previously visited place, forming a so-called loop closure. Loop closures enable autonomous underwater vehicles (AUVs) to reduce the unbounded navigation drift experienced when mapping unknown terrain. However, the scarcity of recognizable features in unstructured sub-sea environments makes disambiguation of places a challenging problem. In this work we study the application of water analysis sensors mounted on an AUV to construct descriptors of underwater environments. Since these sensors measure very different properties of the areas compared to terrain sensors, these descriptors could increase robustness against perceptual aliasing on place recognition and thus improve loop closure detection in a SLAM framework. We present a method to create and compare these spatial descriptors, test it with several data sets collected with an AUV in two different geographical locations and analyze their potential use for loop closure detection.
Multiple-Behavior–Change Interventions for Women With Type 2 Diabetes

Type 2 diabetes is a costly chronic illness that is increasing in prevalence and associated with significant health problems, including heart disease.1 Furthermore, type 2 diabetes and heart disease share multiple lifestyle risk factors that tend to co-occur for many adults.2 Research has established the health benefits of adopting Mediterranean-style eating practices,3 engaging in physical activity,4,5 managing stress,6,7 and using social-environmental support to initiate and sustain health-related behaviors.8–10 Nevertheless, multiple–risk-factor intervention studies are rare11-13 and sorely needed, particularly to understand their potential to reach and benefit underserved populations. Multiple-behavior–change interventions appear necessary in light of the co-occurrence of risk factors,2 yet they are complex and demanding for both service providers and patients. Providers must have multiple competencies or the resources to assemble an interdisciplinary team with expertise in nutrition, exercise, smoking cessation, stress management, and motivational strategies. They must weigh the costs and benefits of sequential and simultaneous approaches to changing multiple behaviors, although thus far, those approaches seem to produce comparable effects.14 Patients must comprehend many intervention methods and devote sufficient effort to each. The purpose of this article is to describe the structure and content of the Mediterranean Lifestyle Program (MLP) and its results during 7 years of assessments, as well as a cultural adaptation of the MLP for Latinas (iViva Bien! ) that was offered to members of a large health maintenance organization (HMO) and a community health center in the Denver, Colo. , metropolitan area and its results during 2 years of evaluation. The adaptation of the MLP into iViva Bien! was intended to expand the generalizability of the intervention and to test its application in collaboration with health organizations, two important steps in developing an intervention that can be disseminated …
Efficiency Evaluation of Secure Data Communication Protocols Stack Based on Dynamic Network Topology

One of the key stages in the implementation of network attacks is the interception and analysis of network traffic. The article provides an overview of the main methods of data protection during transmission over the network, their advantages and disadvantages. In addition, the paper presents a new way to protect data transmitted over the network - the stack of secure data communication protocols based on the dynamic network topology, which differs from existing solutions by hiding the fact of interaction of nodes in the network. The method consists in a special approach to traffic switching using encryption, multicast and packet retransmission. The article describes the software implementation of the secure data communication protocol stack. The paper also presents the results of simulation experiments and considers the advantages of the developed solution over the existing ones, as well as the disadvantages of the solution at the current stage of implementation.
Urban Built-up Area Change Detection Using Multi-Band Temporal Texture and One-Class Random Forest

High spatial resolution multi-temporal data provide more spatial details for mapping and monitoring urban land change from unbuilt to built-up areas. Spatio-temporal contextual information has been recognized as useful features in identification of urban land expansion. This paper adopts a multi-band temporal texture measure with pseudo cross multivariate variogram (PCMV) to quantify local spatiotemporal dependence between bi-temporal multispectral images. The multi-band temporal texture features obtained are combined with bi-temporal multispectral images to detect urban built-up area change using direct multi-temporal image classification. One-class random forest (OCRF), a recently proposed one-class classifier, is used in multi-temporal image classification by sampling one specific class of interest, i.e., built-up area expansion. Experimental results demonstrated that the incorporation of multi-band temporal textures outperformed that using spectral features alone, with increases of 10.55% and 1.74% in overall accuracy, for SPOT-5 and Sentinel-2 data, respectively. Similarly, its overall accuracy is 11.08% and 0.24% higher than incorporating single-band temporal textures. The inclusion of PCMV multi-band temporal texture in direct multi-temporal image classification by OCRF is found effective in urban built-up area change detection.
Safety Function of Soft- and Hardware Complex within Aspect of NPP Safety Important Systems

Information technologies play a crucial role in the development, operation, and technical service of critically important infrastructures that have high requirements for reliability and safety. Bugs (faults) or alarms in high operation risk, relating to critically important infrastructures, may lead to destroying or severe damages of expensive cost equipment, considerably harm the environment. Design of nuclear power plants (NPP) safety important systems are based on meeting requirements of regulatory documents and quality assurance of soft- and hardware complexes within the whole life-cycle. An essential problem of the soft- and hardware complexes quality assurance is the procedure of forming requirements to a developed or modified system and their subsequent verification. Increasing the NPP safety important systems requirements at all stages of the life-cycle is concerned with increasing the complexity and functionality if the software and has led to the necessity of developing approaches for the justification of both the safety of the system themselves and the software involved in their make-up. The paper considers an approach based on constructing the “safety functions,” meeting which is verified in the sequel. This approach is applied under the top-level APCS (automated process control systems) hardware verification and can be applied for the analysis of fault tolerance, information, and cyber safety of soft- and hardware complexes.
Assessment and Classification of Mental Workload in the Prefrontal Cortex (PFC) Using Fixed-Value Modified Beer-Lambert Law

Optical-neuro-imaging based functional Near-Infrared Spectroscopy (fNIRS) has been in use for several years in the fields of brain research to measure the functional response of brain activity and apply it in fields such as Neuro-rehabilitation, Brain-Computer Interface (BCI) and Neuroergonomics. In this paper we have enhanced the classification accuracy of a Mental workload task using a novel Fixed-Value Modified Beer-Lambert law (FV-MBLL) method. The hemodynamic changes corresponding to mental workload are measured from the Prefrontal Cortex (PFC) using fNIRS. The concentration changes of oxygenated and deoxygenated hemoglobin ( ${\Delta \text {c}}_{\mathrm {HbO}}\left ({\mathrm {t} }\right) $ and ${\Delta \text {c}}_{\mathrm {HbR}}\left ({\mathrm {t} }\right))$ of 20 participants are recorded for mental workload and rest. The statistical analysis shows that data obtained from fNIRS is statistically significant with p < 0.0001 and t-values from the MBLL data with>1.97 at confidence level of 0.95. The Support Vector Machine (SVM) classifier is used to discriminate mental math (coding) task from rest. Four features, namely mean, peak, slope and variance, are calculated on data processed through two different variants of Beer-lambert Law i.e., MBLL and FV-MBLL for tissue blood flow. The optimal combination of the mean and peak values classified by SVM yielded the highest accuracy, 75%. This accuracy is further enhanced using the same feature combination, to 94% when those features are calculated using the novel algorithm FV-MBLL (with its optical density modelled form the first 4 sec stimulus data). The proposed technique can be effectively used with greater accuracies in the application of fNIRS for functional brain imaging and Brain-Machine Interface.
Inversion with acceleration data: Theoretical development and numerical implementation

New acquisition technologies that measure more than one wavefield component at the streamers are now available. We analyze how the frequency of input data limits the resolution of the final image and introduce a method using acceleration data as input data to improve the resolution for full waveform inversion (FWI). The FWI using acceleration data is achieved by defining of the misfit function and the construction of gradient. Using synthetic data examples, we show that FWI with acceleration data has the potential to improve the resolution of the inverted model with an optimal rate of convengence.
Sensor and Actuator Fault Diagnosis for a Multi-Robot System Based on the Kullback-Leibler Divergence

In this paper, a fault diagnosis method based on information theory is proposed to detect and isolate faulty sensors and actuators in a multi mobile-robot system. The proposed diagnosis method is an observer-based residual generation approach, where residuals are generated using the Kullback-Leibler Divergence (KLD), which is a dissimilarity measure between two probability distributions. Information form of the Kalman Filter is used for multisensor data fusion. Odometers are used to obtain the predicted estimation, while the remaining sensors give the updated estimation. For fault detection and isolation, a bank of filters is used, and residuals are obtained by evaluting the KLD betweeen the predicted and updated estimations of each filter. In order to isolate actuator and odometer faults, closed loop control signals are designed to feed an additional bank of filters. The combination of the two banks of filters within an informational framework allows a better isolation of sensor, actuator and odometer faults. A simulation is performed to demonstrate the efficiency of the proposed method.
Kalman Filter Based Equalization for Underwater Acoustic Communications

For single-carrier underwater acoustic (UWA) communications, a time-domain channel estimation based equalizer (CEE) is usually overlooked in favor of a direct adaptive equalizer (DAE), mainly due to its high complexity despite potentially better performance. The complexity burden of a CEE is mainly caused by the calculation of the equalizer coefficients, which involves matrix inversion operations at a complexity cubic in the channel length L. The value of L for a UWA channel can amount to several tens or even hundreds, making the CEE scheme prohibitively costly. In this work, a CEE scheme with a complexity quadratic in L is proposed. It is designed from the Kalman filter (KF) perspective and referred as the Kalman equalizer (KE). In the KE, an adaptive channel estimator is coupled with a KF based symbol estimator. The estimated channel is used to construct the KF measurement matrix, and there is no need to compute any equalizer coefficients. Moreover, the channel sparsity can be conveniently utilized to reduce the equalization complexity. The proposed KE scheme was verified by experimental data collected in at-sea trials and showed significant performance gain over a DAE.
Research Into Impact of Attachment System of Exoskeleton Link to Human Body on Efficiency of its Application

The article deals with the most essential aspects of creating comfortable conditions for operating an exoskeleton, ensuring optimal human performance and preserving his health when using exoskeletons. In the meantime, due to a number of design features, it is often not possible to provide a coaxial (aligning) arrangement of exoskeleton hinges and human joints that causes discomfort and, every now and again, inadaptability of exoskeletons. Therefore, further improvement of exoskeletons involves the development and creation of an optimal system for securing an exoskeleton on a human body, taking into account the misalignment. The parameters of such attachment system are determined on the basis of modeling and studying of the interaction mechanisms between a human and an exoskeleton when performing various movements. Practical recommendations are given in regard to the creation of a fastening system (human-machine interface) which provides comfortable conditions for the operator.
Underwater objects classifier based on super-resolution spatial spectrum variance

In underwater environment, the low speed target recognition has been a challenge problem. A design for underwater objects identification is suggested in this paper. The construction can be used in harbor surveillance and ocean environment inspection. Utilizing super-resolution beamforming, the distinctions between the low-speed object (such as buoy or low speed AUV) and the beacon can be observed in spatial-temporal spectrum. Corresponding to the features of power differences and spatial spectrum variance in vertical direction, the low-speed object can be separated from beacon. The data from lake experiment is dealt with and illustrate the method.
Similarity Detector on the Student Assignment Document Using Levenshtein Distance Method

The rapid development of technology and information now gives both positive and negative impacts in the daily life. One of the positive impacts is the existence of internet. Internet makes it easier to find the information sources. However, it also gives some negative impacts. Internet is often used for individual interests such as the act of plagiarism. Plagiarism is an act of imitating or stealing another’s writing and work as one’s own without acknowledgment of the writer. This misuse often happens in the academic field especially at higher educations. Based on the result of the survey, 100% subjects is potential for the act of plagiarism. Therefore, it needs a plagiarism detector application to check plagiarism level towards another’s work by using Levenshtein Distance algorithm. The subject in this research was the application of similarity detector on the student assignment document by using Levenshtein Distance method. The data collection methods were literature study and questionnaires. The stages of the system design involved flowchart design, database that saves preprocessing stages data such as case folding, tokenizing, filtering & stemming, process design, interface, implementation and the system testing by using usability and accuracy test. Based on the system testing conducted by using usability, it can be concluded that the application is acceptable or compatible to be used. The accuracy test with different data on Levenshtein Distance algorithm gets a result of accuracy level of 100% and on the final calculation percentage the accuracy level is 100%, the category determination creates different category levels and on the duration of processing time it can be concluded that the more document content that needs to be detected, the longer time the system takes to process it.
A Novel LDPC-BICM-ID Scheme with Rotated Mapping QAM in Underwater Acoustic Communication

Low density parity check (LDPC) code has a flexible low decoding complexity and its performance is close to Shannon limit. In the bit-interleaved coded modulation with iterative decoding (BICM-ID), the iterative information exchange between the soft demapper and decoder benefits the bit error rate (BER) performance improvement. For BICM-ID with 16-quadrature amplitude modulation (16-QAM), the bits experience different protection depending on their positions within the symbol, which is known as unequal error protection (UEP). To further improve the LDPC-BICM-ID performance in underwater acoustic communication, a key-bit protection method is developed and the rotating mapping scheme is adopted in this paper. Simulation results show that in comparison with the LDPC-BICM-ID of Gray mapping 16-QAM, our proposed scheme can procure a gain of about 0.8dB at BER of 10−5.
Influence of Wind and Shipping Noises on the Depth Dependence and Spatial Coherence of Ambient Noise in the Deep Ocean: A Simulation Study

Previous experiments showed that the ambient noise level in deep ocean is smaller below the critical depth than above the critical depth. While the general features of the ambient noise was understood, quantitative studies must consider the acoustic conditions where the data are collected. In this paper, we analyze the depth dependence and vertical coherence of surface-generated noises at low frequencies using simulated data, and study the influence of environmental conditions such as local/distant wind and shipping. The results can be used to interpret the experimental data.
Hybrid Method of Undersampling and Oversampling for Handling Imbalanced Data

Imbalance of data occurs in various kinds of data including natural imbalanced data. If the computation process of the imbalanced data is carried out (for example clustering), the data imbalance has the potential to cause misclassification because the majority data is more dominant on minority data which results in a decrease in accuracy. The combination method of oversampling and undersampling can be one solution in solving imbalance cases. This study aims to address the problem of imbalanced data by combining the oversampling method with the undersampling method to obtain more representative synthetic data. In this study, the undersampling methods used is Neighborhood Cleaning Rules (NCL. While Adaptive Semiunsupervised Weighted Oversampling (A-SUWO) will be used as the oversampling method. After the undersampling and oversampling process is carried out, the data will be classified using the Decision Tree C4.5 and Random Forest algorithm. Performance evaluation will be processed using the calculation of precision, recall, F-measure and accuracy.