Minimum Network Research Articles

Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System

Light Detection and Ranging (LiDAR) remote sensing technology provides a more efficient means to acquire accurate 3D information from large-scale environments. Among the variety of LiDAR sensors, Multi-Beam LiDAR (MBL) sensors are one of the most extensively applied scanner types for mobile applications. Despite the efficiency of these sensors, their observation accuracy is relatively low for effective use in mobile mapping applications, which require measurements at a higher level of accuracy. In addition, measurement instability of MBL demonstrates that frequent re-calibration is necessary to maintain a high level of accuracy. Therefore, frequent in situ calibration prior to data acquisition is an essential step in order to meet the accuracy-level requirements and to implement these scanners for precise mobile applications. In this study, kinematic in situ self-calibration of a backpack-based MBL system was investigated to develop an accurate backpack-based mobile mapping system. First, simulated datasets were generated for the experiments and tested in a controlled environment to inspect the minimum network configuration for self-calibration. For this purpose, our own-developed simulator program was first utilized to generate simulation datasets with various observation settings, network configurations, test sites, and targets. Afterwards, self-calibration was carried out using the simulation datasets. Second, real datasets were captured in a kinematic situation so as to compare the calibration results with the simulation experiments. The results demonstrate that the kinematic self-calibration of the backpack-based MBL system could improve the point cloud accuracy with Root Mean Square Error (RMSE) of planar misclosure up to 81%. Conclusively, in situ self-calibration of the backpack-based MBL system can be performed using on-site datasets, reaching the higher accuracy of point cloud. In addition, this method, by performing automatic calibration using the scan data, has the potential to be adapted to on-line re-calibration.

基于景观生态风险评价的涪江流域景观格局优化

以流域为尺度进行景观生态风险评价以及景观格局优化，有利于为流域生态系统服务的提高和人类活动管控提供科学依据。以涪江流域为研究区域，从"自然-社会-景观格局"3个维度选取10个评价因子建立评价指标体系，采取空间主成分分析法（SPCA）对流域景观生态风险进行综合评价，再基于生态风险评价的结果和生态源地利用最小累积阻力模型（MCR）和网络分析等方法实现流域景观格局优化。研究结果表明：①涪江流域景观生态风险等级在空间分布上呈西北部高于东南部地区，主要是受自然和景观格局因子影响较大。②涪江流域所面临的生态风险问题较为严重，生态风险等级为中度及以上的区域面积总和为25596.51 km²，占研究区总面积比例的65.35%。③生态源地以林地和水域为主，面积为11194.28 km²，占流域总面积比例为25.58%。④构建生态廊道共41条，总长度为5229.04 km，其中原有廊道29条，新添廊道12条，提取生态节点53个；利用网络分析形成了以主廊道为"中轴"，构建的生态廊道为"辅助"，提取的生态节点为"枢纽"的较为完整的网络生态结构。对研究区景观格局优化前后的连通度进行对比，优化后的整体景观格局连通度得到较大幅度提升。;Taking the watershed as the scale to conduct landscape ecological risk assessment and landscape pattern optimization is conducive to providing a scientific basis for the improvement of watershed ecosystem services and the management of human activities. The ecological risks faced by the Fujiang River Basin are affected by multiple factors, and the optimization of landscape pattern is an effective method to deal with ecological risks. In this research, 10 factors from three aspects (e.g. natural, society, and landscape pattern) were selected to establish the index system. The Spatial principal component analysis (SPCA) was used to evaluate the ecological risk of watershed landscape, and then based on the results of ecological risk assessment and ecological sources, the minimum cumulative resistance model (MCR) and network analysis were used to optimize the landscape pattern of the watershed. The results show that ①The spatial principal component analysis method can effectively evaluate the spatial distribution of landscape ecological risks. The level of ecological risk in the northwest was higher than in the southeast, which was mainly affected by the two dimensions of natural and landscape pattern factors. ②The ecological risks problem faced by the Fujiang River basin are severe. The area of ecological risk grads of moderate and above is 25596.51 km², accounting for 65.35% of the total area of the study area. ③The ecological sources are mainly forests and water areas, with a total area of 11194.28km², accounting for 25.58% of the total area basin of the basin. ④The network ecological structure is composed of 15 ecological sources, 53 ecological nodes, 41 auxiliary ecological corridors with a total length of 5229.04 km and 1 main central axis corridor, which can effectively reduce the ecological risk in the study area and promote the flow of material and energy in the study area. By comparing the connectivity of the landscape pattern before and after optimization in the study area, it can be found that the connectivity after optimization has been significantly increased. The research results are helpful to improve the ecological stability of the study area, and provide a scientific basis for the landscape ecological risk assessment and landscape pattern optimization research.

Minimal neural network topology optimization for aesthetic classification

In this article, a minimum neural network topology in terms of units and connections (neurons and weights), making visual aesthetically categorized images, will be identified. For this purpose, an analysis to compact the initial neural network will be conducted by reducing the amount of connections and neurons required for an a esthetic classification function in hidden layers. A collection of photographs and aesthetic evaluations from an online voting site for photographs such as Photo.net was used. Such pictures are defined by ad-hoc metrics used in previous studies. In this way, the input data, the images, are interpreted within a minimum complexity scheme by complexity estimators (internal representations). The results obtained show that the optimized topology found retains both its performance and its ability to generalize. As part of this study, it has been found a relationship concerning the input data and minimum topology necessary for its correct representation, demonstrating statistically a performance comparable to other not minimized topologies.

A Self-Adaptive Quantum Genetic Algorithm for Network Flow Vehicle Scheduling Problem

Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.

Application of various optimization methods in calculating the error of a neural network for diagnosing parasitological diseases of the gastrointestinal tract

Formulation of the problem. This work is devoted to the use of artificial neural networks for diagnosing the functional state of the gastrointestinal tract caused by the influence of parasites in the body. For the experiment, 24 symptoms were selected, the number of which can be increased, and 9 most common diseases. The coincidence of neural network diagnostics with classical medical diagnostics for a specific disease is shown. The purpose of the work is to compare the neural networks in terms of their performance after describing the methods of preprocessing, isolating symptoms and classifying parasitic diseases of the gastrointestinal tract. Computer implementation of the experiment was carried out in the NeuroPro 0.25 software environment and optimization methods were chosen for training the network: "gradient descent" modified by Par Tan, "conjugate gradients", BFGS. Results. The results of forecasting using a multilayer perceptron using the above optimization methods are presented. To compare optimization methods, we used the values of the minimum and maximum network errors. Comparison of optimization methods using network errors makes it possible to draw the correct conclusion that for the task at hand, the best results were obtained when using the "conjugate gradients" optimization method. Practical significance. The proposed approach facilitates the work of the experimenter-doctor in choosing the optimization method when working with neural networks for the problem of diagnosing parasitic diseases of the gastrointestinal tract from the point of view of assessing the network error.

Deployment and Analysis of Cooperative Intelligent Transport System Pilot Service Alerts in Real Environment

The industry is providing vehicles with advanced features and technologies that allows vehicles to connect and communicate with their nearby environment. These technologies' umbrella term, Cooperative Intelligent Transportation Systems (C-ITS) are aimed to enhance road traffic efficiency, safety and assist the drivers in multiple ways. The C-ITS communication system should be able to offer the functional benefits to different sets of use-cases, each having a particular sub-set of requirements. In this paper some of the relevant use-case scenarios utilizing road weather and traffic information are studied in terms of communication technology. The key requirements for C-ITS use-case scenarios are analyzed and an evaluation of the ITS-G5 protocol stack is performed. The performance of ITS-G5 in use-case scenarios is considered testing messages (alerts) on road weather and traffic information in realistic environments. The results indicate that the performance of ITS-G5 in tested use-case scenarios offers 90-98% success rate in the delivery of safety messages at a transmission frequency of 10Hz. Also, ITS-G5 delivers safety alerts with a minimum delay so that it satisfies the C-ITS use-case requirements in real environments. The C-ITS pilot platform also performs efficiently in terms of transmitting packets from a safe distance with minimum network latency and packet loss between vehicles and infrastructure. C-ITS pilot use-cases were tested on the platform developed and tailored by the Finnish Meteorological Institute (FMI).

Discrete Integrity Assuring Slice‐Based Secured Data Aggregation Scheme for Wireless Sensor Network (DIA‐SSDAS)

In a wireless sensor network, data privacy with a minimum network bandwidth usage is addressed using homomorphic‐based data aggregation schemes. Most of the schemes which ensure the end‐to‐end privacy provide collective integrity verification of aggregated data at the receiver end. The presence of corrupted values affects the integrity of the aggregated data and results in the rejection of the whole data by the base station (BS) thereby leading to the wastage of bandwidth and other resources of energy constraint wireless sensor network. In this paper, we propose a secured data aggregation scheme by slicing the data generated by each sensor node deployed in layered topology and enabling en route aggregation. Novel encoding of data and hash slices based on child order is proposed to enable concatenation‐based additive aggregation and smooth extraction of slices from the aggregate by the BS. Elliptic curve‐based homomorphic encryption is adopted to ensure end‐to‐end confidentiality. To the best of our knowledge, the proposed scheme is the first which facilitates the BS to perform node‐wise integrity verification, filter out only the corrupted portion, and implement dynamic query over the received data. Communication‐ and computation‐based performance analysis shows the efficiency of the proposed scheme for varied network sizes. The scheme can resist eavesdropping attack, node compromising attack, replay attack, malleability attack, selective dropping attack, and collusion attack.

Terrestrial Laser Scanner Self-Calibration: Quantitative Evaluation of Minimum Network Configuration

Two approaches available for performing self-calibration are: 1) point-based and 2) feature-based. The reliance on planes in the feature-based approach is a restriction to the aim of finding a suitable approach for on-site calibration. Also, the requirement for a strong network configuration in point-based self-calibration has made it difficult to implement on-site. The aim of this study was to investigate the suitability of lowering the network configuration requirements. Investigations of minimum network configurations were carried out by analysing minimum dimension of real-world plane on which targets are distributed. Through network analysis, standard deviation and plotted error ellipses will be used to determine the reliability of each configuration during minimising network experiment. The outcomes of this study will benefit to TLS practitioner to ensure that the employed instrument reliable to yield quality data.

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

Internet of Things (IoT) is an emerging domain in which different devices communicate with each other through minimum human intervention. IoT devices are usually operated in hostile and unattended environments. Moreover, routing in current IoT architecture becomes inefficient due to malicious and unauthenticated nodes’ existence, minimum network lifetime, insecure routing, etc. This paper proposes a lightweight blockchain based authentication mechanism where ordinary sensors’ credentials are stored. As IoT nodes have a short lifespan due to energy depletion, few credentials are stored in the blockchain to achieve lightweight authentication. Moreover, the route calculation is performed by a genetic algorithm enabled software defined network controller, which is also used for on-demand routing to optimize the energy consumption of the nodes in the IoT network. Furthermore, a route correctness mechanism is proposed to check the existence of malicious nodes in the calculated route. Moreover, a detection mechanism is proposed to restrict the malicious nodes’ activities, while a malicious node’s list is maintained in the blockchain, which is used in the route correctness mechanism. The proposed model is evaluated by performing intensive simulations. The effectiveness of the proposed model is depicted in terms of gas consumption, which shows the optimized utilization of resources. The residual energy of the network shows optimized route calculation, while the malicious node detection method shows the number of packets dropped.

BLACK SEA SUBMARINE VALLEYS – PATTERNS, SYSTEMS, NETWORKS

The article presents a detailed analysis of the underwater morphology of the entire Black Sea basin beyond the shelf break. The focus is on submarine valley systems on continental slope and rise zones, and partially in the abyssal plain area. The present research is among the very few studies that have undertaken a morphological analysis on a regional scale, for an entire marine basin. This achievement was possible by using the publicly available EMODnet bathymetric map of the Black Sea. The Black Sea submarine valleys networks are presented in a map-sketch. It includes 25 valley systems, 5 groups of simple first order channels, and other number of simple, not associated channels. The 25 valley systems are adding up more than 110 main channels and tributaries. Morphological description and analysis of each of the mapped systems is given – shape and plan view morphology, dimensions (length and surface) and slope gradient. Some considerations about the amount of sediments supplied by the valleys from the shelf to the basin floor, forming the deep-sea fans, are presented. For a more detailed and precise image of the Black Sea network of submarine valleys additional work would be necessary to cover the entire basin with a minimum standard network of modern bathymetric mapping and of high resolution seismic survey lines. To take into account the composition and geological structure of different sectors, as factors that influence significantly the morphology of the seabed, would be also particularly useful.

Service Chain Composition With Resource Failures in NFV Systems: A Game-Theoretic Perspective

For systems that are based on network function virtualization (NFV), it remains a key challenge to conduct effective service chain composition with the lowest request latency and the minimum network congestion. In such an NFV system, users are usually non-cooperative, i.e., they compete with each other to optimize their own benefits. However, existing solutions often ignore such non-cooperative behaviors of users. What is more, they may fall short in the face of unexpected resource failures such as breakdown of virtual machines and loss of connections to users. In this article, we formulate the service chain composition problem with resource failures in NFV systems as a non-cooperative game , and show that such a game is a weighted potential game , aiming to search for the optimal Nash equilibrium (NE). By adopting Markov approximation techniques, we devise a distributed scheme called MH-SCCA, which achieves a provably near-optimal NE and adapts to resource failures in a timely manner. For comparison, we also propose two baseline schemes (DRL-SCCA and MCTS-SCCA) for centralized service chain composition that are based on deep reinforcement learning (DRL) and Monte Carlo tree search (MCTS) techniques, respectively. Our simulation results demonstrate the effectiveness of the three proposed schemes in terms of both latency reduction and congestion mitigation, as well as the adaptivity of MH-SCCA when faced with resource failures.

An analysis of socioemotional selectivity theory in the context of older adults’ use of Facebook

ABSTRACT This study investigated the extent to which the postulates of socioemotional selectivity theory can be applied to the social relations of older adults mediated by the use of Facebook. Participants were 130 older adults recruited at public access services to computers and internet in the city of São Paulo (82.3% female; mean age: 67.9 years; mean schooling: 12 ± 5.4 years) who should have at least one year of Facebook use and a minimum network of 30 contacts. Sociodemographic data, pattern of Facebook use (e.g., frequency of use, network size, self-efficacy, quality of contacts) and data related to key socioemotional selectivity theory-related constructs (i.e., future time perspective and life satisfaction scales) were collected. Structural equation modeling for manifest variables (path analysis) was used to explore the relationships with life satisfaction levels. As predicted by the theory, direct and positive associations were found to age and life satisfaction and direct and negative for age and social network size. Future time perspective remained as a mediator between age and life satisfaction. Socioemotional selectivity indicators such as reduction in network size and enhanced emotional closeness were not associated with life satisfaction. The data suggest that social relations mediated by Facebook may favor other strategies that maximize well-being without necessarily downsizing the social networks or selecting partners according to socioemotional criteria.

Cost-effective joint optimisation of BBU placement and fronthaul deployment in brown-field scenarios

In this work, one proposes a model to evaluate the optimal deployment of Centralised Radio Access Network (C-RAN) architecture elements, i.e. Base Band processing Units (BBUs) and fronthaul links, in a brown-field scenario, in which traditional base stations are already deployed and a physical network is present. The proposed optimisation framework jointly optimises BBU placement and accesses network infrastructures deployment. It clusterises the Remote Radio Heads in the scenario through a Multicommodity Flow approach and solves the minimum cost fronthaul network deployment through a Rooted Delay-Constrained Minimum Spanning Tree approach. Optical fibre and microwave links are considered as fronthaul infrastructures. The proposed optimisation framework is validated through a comparison with a theoretical output for a canonical scenario, being afterwards applied to a real scenario. A cost analysis for different scenario configurations is presented, and trade-offs and guidelines for a cost optimal deployment of C-RAN are provided. The analysis of results for the real scenario of the city of Lisbon and its surrounding areas shows that the delay budget in the fronthaul network highly impacts on capital expenditures as well as on operational ones. It is shown that a larger delay budget enables an annual cost reduction up to 72% in urban areas and 54% in rural ones.

Gamma-Ray Burst Triangulation with a Near-Earth Network

Abstract We study the characteristics of near-Earth networks (NENs) of gamma-ray burst (GRB) detectors, with the objective of defining a network with all-sky, full-time localization capability for multimessenger astrophysics. We show that a minimum network consisting of nine identical spacecraft in two orbits with different inclinations provides a good combination of sky coverage with several-degree localization accuracy with detector areas of 100 cm2. In order to achieve this, careful attention must be paid to systematics. This includes accurate photon timing (∼0.1 ms), good energy resolution (∼10%), and reduction of Earth albedo, which are all within current capabilities. Such a network can be scaled in both the number and size of detectors to produce increased accuracy. We introduce a new method of localization that does not rely on on-board trigger systems or on the cross-correlation of time histories, but rather it tests positions in ground processing over the entire sky and assigns probabilities to them to detect and localize events. We demonstrate its capabilities with simulations. If the NEN spacecraft can downlink at least several hundred time- and energy-tagged events per second, and the data can be ground-processed as they are received, it can in principle derive GRB positions in near-real time over the entire sky.

The single-vehicle two-echelon one-commodity pickup and delivery problem

We present in this paper a generalization of the one-commodity pickup and delivery traveling salesman problem where each customer supplies or demands a given amount of a certain product. The objective is to design a minimum cost two-echelon transportation network. The first echelon is the route of a capacitated vehicle that visits some customers, and the second echelon consists in the allocation of the non-visited customers to visited ones. The customers that must be visited by the vehicle and the ones that must be allocated to others are not predefined. We present three mathematical models for the problem, design an exact branch-and-cut algorithm to solve it, and show extensive computational results on benchmark instances.

A clustering detector with graph theory for blind detection of spatial modulation systems

This paper considers the blind detection of spatial modulation systems multiple-input multiple-output systems. In this system, we propose a clustering detection framework with graph theory that conducts signal detection without the training of channel state information. In detail, firstly, by dynamically controlling the size of each cluster, we transform the original optimization problem of the traditional K-means clustering detector into a new optimization problem. In addition, the cluster assignment subproblem of the iterative clustering algorithm for solving the new optimization problem makes it equivalent to a minimum cost flow linear network optimization problem of graph theory, which can be addressed by the breadth-first algorithm. Moreover, a novel clustering detector with the breadth-first algorithm is presented correspondingly. Numerical results show that the proposed detector is efficient in avoiding the undesired local optima and can closely approach the performance of the optimal detector.

Fuzzy based Load Balancing in Sensor Cloud: Multi-Agent Approach

In recent years, sensor cloud showing its impact in the networking field and also it is being used for most of the applications. The major concern for sensor cloud is its lifetime because yet nodes are operated with limited energy, bandwidth and exhaust very soon affecting the whole network. To make reliable communication and optimizing the whole system for improving network lifetime, load balancing concept is included in sensor cloud. The fuzzy optimizer is responsible for finding the deserving cluster head and precise next hop node for information transmission. Fuzzy method is conducted at the sensor cloud server and the information is shared to sink node for physical network setup. Based on the importance of the information coming from physical network, it can be saved into priority or non priority servers using information classification technique leading to load balancing at server also. Agents are triggered into the network for information collection along with truthful information delivery ratio in a minimum time. The proposed method is compared with existing popular methods to check the load balancing capacity, and it is found that the proposed work is far better than existing methods with respect to response time, delay, energy consumption, minimum packets transmission and network lifetime.

Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities.

The deduction of design principles for complex biological functionalities has been a source of constant interest in the fields of systems and synthetic biology. A number of approaches have been adopted, to identify the space of network structures or topologies that can demonstrate a specific desired functionality, ranging from brute force to systems theory-based methodologies. The former approach involves performing a search among all possible combinations of network structures, as well as the parameters underlying the rate kinetics for a given form of network. In contrast to the search-oriented approach in brute force studies, the present chapter introduces a generic approach inspired by systems theory to deduce the network structures for a particular biological functionality. As a first step, depending on the functionality and the type of network in consideration, a measure of goodness of attainment is deduced by defining performance parameters. These parameters are computed for the most ideal case to obtain the necessary condition for the given functionality. The necessary conditions are then mapped as specific requirements on the parameters of the dynamical system underlying the network. Following this, admissible minimal structures are deduced. The proposed methodology does not assume any particular rate kinetics in this case for deducing the admissible network structures notwithstanding a minimum set of assumptions on the rate kinetics. The problem of computing the ideal set of parameter/s or rate constants, unlike the problem of topology identification, depends on the particular rate kinetics assumed for the given network. In this case, instead of a computationally exhaustive brute force search of the parameter space, a topology-functionality specific optimization problem can be solved. The objective function along with the feasible region bounded by the motif specific constraints amounts to solving a non-convex optimization program leading to non-unique parameter sets. To exemplify our approach, we adopt the functionality of adaptation, and demonstrate how network topologies that can achieve adaptation can be identified using such a systems-theoretic approach. The outcomes, in this case, i.e., minimum network structures for adaptation, are in agreement with the brute force results and other studies in literature.

Investigations on spreading of PRRSV among swine herds by improved minimum spanning network analysis

In Hungary, the economic losses caused by porcine reproductive and respiratory syndrome virus (PRRSV) led to the launching of a national PRRSV Eradication Program. An important element of the program was investigating the spread of PRRSV among swine herds and the possible ways of introduction by sequencing of the open reading frame 5 (ORF5) gene. However, the classical phylogenetic tree presentation cannot explain several genetic relationships clearly, while more precise visualization can be represented by network tree diagram. In this paper, we describe a practical and easy-to-follow enriched minimum spanning similarity network application for improved representation of phylogenetic relations among viral strains. This method eliminated the necessity of applying a predefined, arbitrary cut-off or computationally extensive algorithms. The network-based visualization allowed processing and visualizing large amount of data equally for the laboratory, private and official veterinarians, and helped identify the potential connections between different viral sequences that support data-driven decisions in the eradication program. By applying network analysis, previously unknown epidemiological connections between infected herds were identified, and virus spreading was analyzed within short period of time. In our study, we successfully built and applied network analysis tools in the course of the Hungarian PRRSV Eradication Program.

Operation Optimization of DC Distribution Network with BSS Based on GA-WDO Hybrid Algorithm

Background: The high permeability of Distributed Generation (DG) and the development of DC load represented by electric vehicle Battery Swapping Station (BSS) pose new challenges to the reliable and economic operation of DC distribution system. Methods: In order to improve the wind and solar absorption rate and the reliable operation of DC distribution network and coordinate the interests and demands of BSS and DC distribution company, the upper level takes the abandonment rate and the minimum variance of BSS charging and discharging net load as two objective functions, and the lower level takes the minimum operation cost of DC distribution network and BSS as the objective function. Secondly, this paper proposes a method that combines Genetic Algorithm (GA) with Wind-Driven Optimization algorithm (WDO). CPLEX and hybrid GA-WDO are used to solve the upper and lower models, respectively. Results: Finally, an example shows that the proposed optimization model can reduce the operation cost of DC distribution network with BSS and improve the utilization rate of wind and light, which shows the rationality and effectiveness of the optimization model. Conclusion: In this paper, considering the randomness and uncertainty of wind power generation and photovoltaic power generation, this paper establishes the upper objective function with the minimum abandonment rate and load variance and the lower objective function with the minimum operation cost of DC distribution network and BSS operators.