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Search results for: CONVOLUTIONAL NEURAL NETWORK, PEDESTRIAN DETECTION, ROBUSTNESS, STYLE-TRANSFER, DATA AUGMENTATION, UNCERTAINTY ESTIMATION
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Accuracy of Differential Phase Delay Estimation for GPS Spoofing Detection
PublicationGPS spoofing is an attack based on transmission of false signals to target receivers, in order to force the computation of incorrect time, position or velocity information. It is a threat which is recently considered with continuously growing awareness. There is a need for effective way of GPS spoofing detection. Many groups of methods are proposed in the literature. Spatial processing methods are considered to be robust in a wide...
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Diagnosing wind turbine condition employing a neural network to the analysis of vibroacoustic signals
PublicationIt is important from the economic point of view to detect damage early in the wind turbines before failures occur. For this purpose, a monitoring device was built that analyzes both acoustic signals acquired from the built-in non-contact acoustic intensity probe, as well as from the accelerometers, mounted on the internal devices in the nacelle. The signals collected in this way are used for long-term training of the autoencoder...
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Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network
PublicationArtificial Intelligence algorithms are being increasingly used in industrial applications. Their important function is to support operation of diagnostic systems. This paper pesents a new approach to the monitoring of a regenerative heat exchanger in a steam power plant, which is based on a specific use of the Recurrent Neural Network (RNN). The proposed approach was tested using real data. This approach can be easily adapted to...
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EPILEPTIC BEHAVIOR WITH A DISTINGUISHED PREICTAL PERIOD IN A LARGE-SCALE NEURAL NETWORK MODEL
PublicationWe present a neural network model capable of reproducing focal epileptic behavior. An important property of our model is the distinguished preictal state. This novel feature may shed light on the pathologi-cal mechanisms of seizure generation and, in perspective, help develop new therapeutic strategies to manage refractory partial epilepsy.
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Adaptive Hyperparameter Tuning within Neural Network-based Efficient Global Optimization
PublicationIn this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian optimization and multiarmed bandit optimization to tune HPs during the sequential sampling process either every iteration (HPO-1itr) or every five iterations (HPO-5itr). Through experiments using...
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Gas Detection Using Resistive Gas Sensors And Radial Basis Function Neural Networks
PublicationWe present a use of Radial Basis Function (RBF) neural networks and Fluctuation Enhanced Sensing (FES) method in gas detection system utilizing a prototype resistive WO3 gas sensing layer with gold nanoparticles. We investigated accuracy of gas detection for three different preprocessing methods: no preprocessing, Principal Component Analysis (PCA) and wavelet transformation. Low frequency noise voltage observed in resistive gas...
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Piotr Szczuko dr hab. inż.
PeoplePiotr Szczuko received his M.Sc. degree in 2002. His thesis was dedicated to examination of correlation phenomena between perception of sound and vision for surround sound and digital image. He finished Ph.D. studies in 2007 and one year later completed a dissertation "Application of Fuzzy Rules in Computer Character Animation" that received award of Prime Minister of Poland. His interests include: processing of audio and video, computer...
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Face with Mask Detection in Thermal Images Using Deep Neural Networks
PublicationAs the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The...
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The application of microscopic models in the study of pedestrian traffic
PublicationCities (especially in Central and Eastern Europe) focus on improving the road network, which aims to improve the efficiency of motor traffic and minimize congestion. Most of existing tools for analysing the effectiveness of urban transport networks do not assume to analyse the impact of walking and cycling on efficiency of transport systems. It is therefore necessary to develop solutions...
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NETWORK-COMPUTATION IN NEURAL SYSTEMS
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Pedestrian Safety in Road Traffic in Poland
PublicationEvery third road accident in Poland involves a pedestrian as a participant or, most of the time, a casualty. Pedestrian accidents are usually the result of complex situations and the outcome of a number of factors related to driver and pedestrian behaviour and road infrastructure. Safety depends largely on how well the traffic condition is perceived and on visibility in traffic. The paper presents the results of analyses of methodologies...
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Sonar Pulse Detection Using Chirp Rate Estimation and CFAR Algorithms
PublicationThis paper presents a new approach to sonar pulse detection. The method uses chirp rate estimators and algorithms for the adaptive threshold, commonly used in radiolocation. The proposed approach allows detection of pulses of unknown parameters, which may be used in passive hydrolocation or jamming detection in underwater communication. Such an analysis is possible thanks to a new kind of imaging, which presents signal energy in...
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Artificial neural network prophecy of ion exchange process for Cu (II) eradication from acid mine drainage
PublicationThe removal of heavy metal ions from wastewater was found to be significant when the cation exchange procedure was used effectively. The model of the cation exchange process was built using an artificial neural network (ANN). The acid mine drainage waste’s Cu(II) ion was removed using Indion 730 cation exchange resin. Experimental data from 252 cycles were recorded. In a column study, 252 experimental observations validated the...
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THE 3D MODEL OF WATER SUPPLY NETWORK WITH APPLICATION OF THE ELEVATION DATA
Publication3D visualization is a key element of research and analysis and as the source used by experts in various fields e.g.: experts from water and sewage systems. The aim of this study was to visualize in three-dimensional space model of water supply network with relief. The path of technological development of GESUT data (Geodezyjna Ewidencja Sieci Uzbrojenia Terenu – geodetic records of public utilities) for water supply and measurement...
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Nonradiative long range energy transfer in donor-acceptor systems with excluded volume
PublicationW pracy analizowano bezpromienisty długozasięgowy transfer energii w układach donor-akceptor z uwzględnieniem objętości wyłączonej. Stwierdzono, że zanik fluorescencji pierwotnie wzbudzonych donorów jest wolniejszy, jeżeli uwzględnia się objętość wyłączoną. Ten efekt jest znacznie wzmocniony, gdy akceptory znajdują się w pewnej objętości niedostępnej dla donorów. Analizę numeryczną przeprowadzono stosując metodę symulacji Monte...
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APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS
PublicationAbnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregu- larity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated...
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Emotion Recognition from Physiological Channels Using Graph Neural Network
PublicationIn recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The...
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DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublicationMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
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Detection and size estimation of crack in plate based on guided wave propagation
PublicationThe paper presents results of the comprehensive theoretical and experimental investigation of crack detection in metallic plate using guided wave propagation. The main aim of the paper is to develop the novel method which would allow for linear crack size estimation with the use of minimal number of the transducers. In general, there exists the relation between length of the propagation path and the wave amplitude value. However,...
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Exploring Neural Networks for Musical Instrument Identification in Polyphonic Audio
PublicationThe purpose of this paper is to introduce neural network-based methods that surpass state-of-the-art (SOTA) models, either by training faster or having simpler architecture, while maintaining comparable effectiveness in musical instrument identification in polyphonic music. Several approaches are presented, including two authors’ proposals, i.e., spiking neural networks (SNN) and a modular deep learning model named FMCNN (Fully...
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1D convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type
PublicationA network architecture that may be employed to sensing and recognition of a type of vehicle on the basis of audio recordings made in the proximity of a road is proposed in the paper. The analyzed road traffic consists of both passenger cars and heavier vehicles. Excerpts from recordings that do not contain vehicles passing sounds are also taken into account and marked as ones containing silence....
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Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training
PublicationIn the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours...
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Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction
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Effects of Road Infrastructure on Pedestrian Safety
PublicationThe objective of the work was to identify risks for pedestrians that involve road infrastructure and roadside and to define how selected elements of geometry and traffic layout affect driver behaviour (speed on approaching pedestrian crossings). The results have helped to formulate recommendations on pedestrian crossing design. The research included an analysis of 2013-2017 statistics to identify the...
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Possibilities of heat transfer augmentation in heat exchangers with minichannels for marine applications
PublicationIn the paper, new trends in development of microchannel heat exchangers are presented. The exchangers developed in this way can be applied in marine applications. Main attention was focused on heat exchangers design with reduced size of passages namely based on microchannels. In our opinion future development of high power heat exchangers will be based on networks micro heat exchangers.
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Fusion-based Representation Learning Model for Multimode User-generated Social Network Content
PublicationAs mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. Due to the multi-source heterogeneous nature of the data, big data fusion offers both promise and drawbacks. With the rise of mobile networks and applications, UGC, which includes...
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Instrument detection and pose estimation with rigid part mixtures model in video-assisted surgeries
PublicationLocalizing instrument parts in video-assisted surgeries is an attractive and open computer vision problem. A working algorithm would immediately find applications in computer-aided interventions in the operating theater. Knowing the location of tool parts could help virtually augment visual faculty of surgeons, assess skills of novice surgeons, and increase autonomy of surgical robots. A surgical tool varies in appearance due to...
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Neural Network-Based Sequential Global Sensitivity Analysis Algorithm
PublicationPerforming global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...
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Development of a tropical disease diagnosis system using artificial neural network and GIS
PublicationExpert systems for diagnosis of tropical diseases have been developed and implemented for over a decade with varying degrees of success. While the recent introduction of artificial neural networks has helped to improve the diagnosis accuracy of such systems, this aspect is still negatively affected by the number of supported diseases. A large number of supported diseases usually corresponds to a high number of overlapping symptoms,...
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Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting
PublicationForecasting ice phenomena in river systems is of great importance because these phenomena are a fundamental part of the hydrological regime. Due to the stochasticity of ice phenomena, their prediction is a difficult process, especially when data sets are sparse or incomplete. In this study, two machine learning models—Multilayer Perceptron Neural Network (MLPNN) and Extreme Gradient Boosting (XGBoost)—were developed to predict...
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Identification of the Contamination Source Location in the Drinking Water Distribution System Based on the Neural Network Classifier
PublicationThe contamination ingression to the Water Distribution System (WDS) may have a major impact on the drinking water consumers health. In the case of the WDS contamination the data from the water quality sensors may be efficiently used for the appropriate disaster management. In this paper the methodology based on the Learning Vector Quantization (LVQ) neural network classifier for the identification of the contamination source location...
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Detection and Direction-of-Arrival Estimation of Weak Spread Spectrum Signals Received with Antenna Array
PublicationThis paper presents a method for the joint detection and direction of arrival (DOA) estimation of low probability of detection (LPD) signals. The proposed approach is based on using the antenna array to receive spread-spectrum signals hidden below the noise floor. Array processing exploits the spatial correlation between phase-delayed copies of the signal and allows us to evaluate the parameter used to make the decision about the...
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Style
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New Two-center Ellipsoidal Basis Function Neural Network for Fault Diagnosis of Analog Electronic Circuits
PublicationIn the paper a new fault diagnosis-oriented neural network and a diagnostic method for localization of parametric faults in Analog Electronic Circuits (AECs) with tolerances is presented. The method belongs to the class of dictionary Simulation Before Test (SBT) methods. It utilizes dictionary fault signatures as a family of identification curves dispersed around nominal positions by component tolerances of the Circuit Under Test...
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Kamila Kokot-Kanikuła mgr
PeopleKamila Kokot-Kanikuła is a digital media senior librarian at Gdańsk University of Technology (GUT) Library. She works in Digital Archive and Multimedia Creation Department and her main areas of interests include early printed books, digital libraries, Open Access and Open Science. In the Pomeranian Digital Library (PDL) Project she is responsible for creating annual digital plans, transferring files on digital platform, and promoting...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublicationIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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Lighting requirements for pedestrian crossings - positive contrast
PublicationFor many years now in Poland there has been a large number of road accidents at pedestrian crossings during night periods [5, 11]. One of the technical solutions that can improve this condition is the use of proper lighting for pedestrian crossings. The designated pedestrian crossing should be visible in different weather conditions and at different times of the day. In case of night vision restrictions use artificial lighting...
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BETWEEN IDEA AND INTERPRETATION - DESIGN PROCESS AUGMENTATION
PublicationThe following paper investigates the idea of reducing the human digital intervention to a minimum during the advanced design process. Augmenting the outcome attributes beyond the designer's capabilities by computational design methods, data collection, data computing and digital fabrication, altogether imitating the human design process. The primary technical goal of the research was verification of restrictions and abilities used...
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Neural networks and deep learning
PublicationIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
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Anna Wałek dr
PeopleDr Anna Wałek, President of IATUL – International Association of University Libraries, director of the Gdańsk University of Technology Library. An experienced library manager, an expert in the field of Open Science, and organization and management of a scientific library. She conducts scientific research in data management in various scientific disciplines, metadata for research data, and data management support services - incl....
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Comparative Evaluation of Multicoil Inductive Power Transfer Approaches Based on Z-source Network
PublicationThis paper describes comparative evaluation between wireless power transfer topologies with utilization of Z-source network. Paper describes components calculation method. List of open-loop, close-loop simulations were conducted to compare both topologies. Spectrum of signals is also researched.
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Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublicationIn recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
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Modified Inductive Multi-Coil Wireless Power Transfer Approach Based On Z-Source Network
PublicationThis article presents a non-conventional approach to a multi-coil wireless power transfer system based on a Z-source network. The novelty of the approach lies in the use of a Z-source as a voltage source for energy transmission through the wireless power transfer coils. The main advantage is in a reduced number of semiconductors. This paper provides the design approach, simulation and experimental study. Feasibility and possible...
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Comparison of the Ability of Neural Network Model and Humans to Detect a Cloned Voice
PublicationThe vulnerability of the speaker identity verification system to attacks using voice cloning was examined. The research project assumed creating a model for verifying the speaker’s identity based on voice biometrics and then testing its resistance to potential attacks using voice cloning. The Deep Speaker Neural Speaker Embedding System was trained, and the Real-Time Voice Cloning system was employed based on the SV2TTS, Tacotron,...
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Design of Microstrip UWB Balun Using Quasi-TEM Approach Aided by the Artificial Neural Network
PublicationThe design procedure for UWB balun realized in the microstrip technology is proposed in the paper. The procedure applies Artificial Neural Network which corrects the dimensions of the approximate design found by appropriate scaling of the dimensions of the prototype. The scale coefficients for longitudinal and transverse dimensions of microstrip lines are determined from electromagnetic modeling based on transmission line equations....
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Pedestrian on footbridges, vertical loads and response
PublicationThe paper is a synthesis of the experimental and theoretical investigations on dynamics of pedestrian bridges under vertical load The subject is divided into four issues.1) The pedestrian as a vertical dynamic load on footbridges, theoretic load function for walking and crouching.2) Crowd walking on the footbridge - pedestrian flow.3) Vertical lock-in effect.4) Response of the footbridge - numeric and side test results, -...
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Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
PublicationThe present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases...
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Identification of Unstable Reference Points and Estimation of Displacements Using Squared Msplit Estimation
PublicationThe article presents a new version of the method for estimating parameters in a split functional model, which enables the determination of displacements of geodetic network points with constrained datum. The main aim of the study is to present theoretical foundations of Msplit CD estimation and its basic properties and possible applications. Particular attention was paid to the efficacy of the method in the context of geodetic...
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OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems
PublicationCurrently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered...
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The Optimal Location of Ground-Based GNSS Augmentation Transceivers
PublicationModern Global Navigation Satellite Systems (GNSS) allow for positioning with accuracies ranging from tens of meters to single millimeters depending on user requirements and available equipment. A major disadvantage of these systems is their unavailability or limited availability when the sky is obstructed. One solution is to use additional range measurements from ground-based nodes located in the vicinity of the receiver. The highest...