Multiple Deployment Research Articles

A trajectory design method for coupling aircraft radar cross-section characteristics

Fast and safe aircraft penetration is important to a battle mission. Radar is a major threat in the process of aircraft penetration. To cope with the radar threat, a trajectory design method for coupling the aircraft radar cross-section (RCS) characteristics is proposed in this paper. First, the RCS data are calculated by the physical optics (PO) method. The aircraft RCS characteristic model is established by Gaussian filtering and polynomial fitting. Second, a radar detection probability model is established by taking into account the influence of the radar maximum detection range, the aircraft RCS, and the distance between the aircraft and the radar. Third, a trajectory optimization solution framework with the dynamic model, constraint conditions, and optimal objectives is constructed. The hp-adaptive Radau pseudospectral method and the stepwise increasing constraints method are proposed to solve the optimal control problem. Finally, several examples in single and multiple radar unit deployment scenarios are simulated and verified. The simulation results showed that the proposed method is effective and applicable.

Geo-Sensor Framework and Composition Toolbox for Efficient Deployment of Multiple Spatial Context Service Platforms in Sensor Networks

Geo-sensor is the term used for the deployment of a wireless sensor network (WSN) in a real environment, which can be a hideous task due to many influential variables in a given environment. The spatial context of a sensor in a smart environment can be of huge significance and can also play an important role in improving the smart services provision. In this work, we propose a DIY geo-sensor framework and data composition toolbox for the deployment of sensors data in smart IoT environments along with geographical context. A geo-sensor framework is deployed, which enables the registration of multiple geo-sensor networks by providing multiple geo-sensor platforms. The framework’s logic is based on the combination of a geo-sensor service registry, geo-sensor composition toolbox, and geo-sensor platforms. A geo-sensor platform provides the content to the toolbox, enabling relaxed real-time geo-sensor data management. Our proposed work is two-fold. Firstly, we propose the design details for the geo-sensor framework and composition toolbox. The proposed design for the geo-sensor framework aims to provide a DIY platform for multiple geo-sensor networks and services deployment, giving access to multiple users resulting in reuse of resources and reduction in deployment costs by avoiding duplicate deployments. Secondly, we implement the proposed design based on RESTful web services and SOAP web services. Performance comparison analysis is then performed among the two web services to find the best suited implementation for given scenarios. The results of the performance analysis prove that RESTful web services are the better choice for ease of implementation, access, and light-weightiness.

Virtualization of disaggregated optical networks with open data models in support of network slicing

Network slicing has been a major selling point of 5G networks, where a slice is roughly defined as a self-contained logical network on top of a shared infrastructure tailored for a specific service or vertical industry, composed of heterogeneous resources (computing, storage, bandwidths) and typically involving specific application and/or networking functions. The relationship between the network slicing and the underlying network virtualization is an open research topic, allowing multiple deployment models. In particular, a specific model of interest involves the virtualization of the optical infrastructure, where optical virtual networks are instantiated in support of network slicing, so the virtual network underlies and provides connectivity to component functions of a network slice. In this paper, we propose and implement a network virtualization architecture for open optical (partially) disaggregated networks, based on the concept of a device hypervisor relying on OpenROADM data models, in support of 5G network slicing over interconnected network function virtualization infrastructure points of presence (NFVI-PoPs). The architecture is experimentally validated, showing the provisioning of ITU-T flexigrid network media channels across a virtualized network.

Single-session multiple stent deployment using moving cell stent without dilating initial stent mesh to treat malignant hilar biliaryobstruction (with videos).

Malignant hilar biliaryobstruction (MHBO) can be treated with bilateral self-expandable metal stents (SEMS) deployed using side-by-side (SBS) or stent-in-stent (SIS) techniques. Moving cell stents (MCS) are a novel type of SEMS. The present study evaluated the technical feasibility of treating MHBO using bilateral novel uncovered SEMS to insert an SIS technique without dilating the mesh of a first stent within a single session. We retrospectively assessed patients who were complicated with obstructive jaundice due to MHBO between August and December 2018. Technical success was defined as the deployment of a bilateral MCS into two or more biliary tracts using SIS technique without a dilation device. The present study analyzed data from 23 consecutive patients who were complicated with MHBO. Bilateral SIS technique with MCS was deployed in 22 (95.6%) of the 23 patients without dilating the mesh of the first stent. Multiple guidewire insertion failed in one patient with Bismuth-type IV. The median procedural duration was 33.6min. Time to recurrent biliary obstruction was 230days. Severe adverse events were not seen in any patients. In conclusion, uncovered moving cell SEMS might facilitate bilateral stent deployment using SIS technique.

5G Base Station Deployment Perspectives in Millimeter Wave Frequencies Using Meta-Heuristic Algorithms

It can be predicted that the infrastructure of the existing wireless networks will not fill the requirement of the fifth generation (5G) wireless network due to the high data rates and a large number of expected traffic. Thus, a novel deployment method is crucial to satisfy 5G features. Meta-heuristic is expected to be a promising method for the complex deployment optimization problem of the 5G network. This work presents an implementation of a meta-heuristic algorithm based on swarm intelligence, to minimize the number of base stations (BSs) and optimize their placements in millimeter wave (mmWave) frequencies (e.g., 28 GHz and 38 GHz) in the context of the 5G network while satisfying user data rates requirement. Then, an iterative method is applied to remove redundant BSs. We formulate an optimization problem that takes into account multiple 5G network deployment scenarios. Further, a comparative study is conducted with the well-known simulated annealing (SA) using Monte Carlo simulations to assess the performance of the developed model. In our simulation results, we divide the region of interest into two subareas with different user distributions for different network scenarios while considering the intercell interference. The results demonstrate that the proposed approach has better network coverage with low percentage users in outage. In addition, the developed approach has less computational times to reach the desired target network quality of service (QoS).

Sensing Coverage for Wireless Sensor Networks in Shadowed Rician Fading Environments

The effect of shadowing and multipath fading on sensing coverage has been studied in this paper. Rician fading is one of the most widely occurring type of multipath fading. We model the multipath fading by Rician random distribution under LOS propagation conditions with shadowing for the first time. We study the coverage aspect for wireless sensor network in the presence of shadowing and Rician fading. We find that the increasing standard deviation of shadowing and Rician fading has a profound negative impact on the sensing coverage. We note a decreasing trend in the sensing coverage with an increase in both shadowing and multipath Rician fading. The decrease of sensing coverage in our study is compared with the case of shadowing alone. Also, multiple deployment of nodes is demonstrated in which we observe a reduction in the no. of nodes required for sensing coverage.

A New Improved Voltage Stability Assessment Index-centered Integrated Planning Approach for Multiple Asset Placement in Mesh Distribution Systems

This paper offers a new improved voltage stability assessment index (VSAI_B)-centered planning approach, aiming at the attainment of technical and cost related objectives with simultaneous multiple asset deployment in a mesh distribution systems (MDS). The assets such as multiple distributed generation (DG) and distributed static compensator (D-STATCOM) units have been utilized; aiming at voltage stabilization, loss minimization, and associated objectives. The proposed planning approach incorporates expressions of VSAI_B aiming at initial simultaneous assets placement followed by loss minimization conditions (LMC) for appropriate asset sizing, which is further utilized for performance evaluations. The VSAI_B-LMC-based integrated planning approach is applied to configured MDS models such as a 33-bus test distribution system (TDS) for detailed analysis. The performance evaluations with the presented approach have been conducted for different cases along with respective scenarios considering various technical and cost-economic performance metrics. First, three cases referring to multiple DGs sitting and sizing for various power factors have been presented, followed later by two cases of multiple DGs and D-STATCOMs with respective evaluation scenarios. Finally, benchmark analysis is conducted on a 69-bus TDS for validity demonstration of the proposed approach. The comparison of achieved results in comparison with the available literature points out toward the validity and improved performance of the proposed approach.

Strategies for Clinical Decision Support for Electronic Case Reporting

ObjectiveTo discuss how clinical decision support (CDS) for electronic case reporting (eCR) will evolve over time to provide multiple deployment modelsIntroductionAs the knowledge required to support case reporting evolves from unstructured to more structured and standardized formats, it becomes suitable for electronic clinical decision support (CDS). CDS for case reporting confronts two challenges: a) While EHRs are moving toward local CDS capabilities, it will take several years for EHR systems to consistently support this capability; and b) public health-related CDS knowledge, such as Zika infection detection and reporting rules, may differ from jurisdiction to jurisdiction. Therefore, there is an ongoing need to manage reporting rules in a distributed manner. Similarly, there is a need for more decentralized models of CDS execution to overcome some of the disadvantages of centralized deployment and to leverage local CDS capabilities as they become available over the next several years.MethodsThe Reportable Condition Knowledge Management System (RCKMS) is a project funded by the CDC, through the Council of State and Territorial Epidemiologists (CSTE), to develop a tool that allows jurisdictions to author rules that define whether a patient is reportable for certain conditions. RCKMS includes a Decision Support Service (DSS) that runs the jurisdictions’ rules and determines if a patient is reportable, for which condition(s) and to which jurisdiction(s). RCKMS currently plays a significant role in the broader Digital Bridge project that has been working to provide structure and governance around the national planning and implementation effort of eCR. RCKMS is currently a centralized CDS service that can be accessed by EHRs until they all have local CDS capabilities; and a knowledge authoring environment that allows ongoing distributed rule authoring. RCKMS supports the strategy for public health knowledge management, and it will evolve over time to provide the systems and services to satisfy short-, mid-, and long-term public health CDS requirements. In addition, RCKMS will comply with emerging technical standards that support this work.ResultsRCKMS is currently being deployed as a single, central, national service on the APHL Informatics Messaging Services (AIMS) platform, which is operated and maintained by Association of Public Health Laboratories (APHL). The AIMS platform connects directly with reporters and provides a routing and validation service for incoming and outgoing messages. Two distributed CDS scenarios for decentralized eCR models have been identified. In the first scenario, the Decision Support Service component of the RCKMS software is installed within a clinical organization (as it would be in a centralized service) and executed locally. The second distributed CDS scenario for eCR involves the distribution of the Reporting Specification rules without the software. In this scenario, local electronic health record (EHR) implementations would be required to consume the Reporting Specifications and utilize them in a CDS capability within their EHR.ConclusionsIt is expected that given the diversity of organizations, systems, and architectures in the United States that multiple deployment scenarios for CDS for eCR will be simultaneously deployed for the foreseeable future. It cannot be stressed enough, however, that in all scenarios – centralized and distributed – there must be a centralized and uniform authoring of the Reporting Specification rules, since the specifications themselves originate from public health through a centralized process and must be administered nationally through a well-established process. It is also essential that all sites have all the rules available to them, since there may be multiple jurisdictions whose rules and reporting is required, with the determination of jurisdiction(s) based on where a patient lives and where they receive care.

Identifying the distribution of Atlantic cod spawning using multiple fixed and glider-mounted acoustic technologies

Abstract Effective fishery management measures to protect fish spawning aggregations require reliable information on the spatio-temporal distribution of spawning. Spawning closures have been part of a suite of fishery management actions to rebuild the Gulf of Maine stock of Atlantic cod (Gadus morhua), but difficulties remain with managing rebuilding. The objective of this study was to identify the spatial and temporal distribution of cod spawning during winter in Massachusetts Bay to improve our understanding of cod spawning dynamics and inform fisheries management. Spawning was investigated in collaboration with commercial fishermen during three winter spawning seasons (October 2013–March 2016) using acoustic telemetry and passive acoustic monitoring equipment deployed in fixed-station arrays and mounted on mobile autonomous gliders. Tagged cod exhibited spawning site fidelity and spawning primarily occurred from early November through January with a mid-December peak and some inter-annual variability. The spatial distribution of spawning was generally consistent among years with multiple hotspots in areas >50 m depth. Current closures encompass most of spawning, but important areas are recommended for potential modifications. Utilizing multiple complementary technologies and deployment strategies in collaboration with commercial fishermen enabled a comprehensive description of spawning and provides a valuable model for future studies.

EMPATIA

Participatory budgeting (PB) is currently one of the most widely adopted democratic innovations. ICT platforms are key enablers of PB processes, by supporting citizen engagement. They support the establishment of diverse participation channels to build candidate proposals, for the voting process, and for monitoring proposals' implementation. PB platforms differ from electronic voting systems, reflecting the intrinsic nature of PB processes. While vote secrecy and trustworthiness of voting results is still a major requirement, other factors weight in, such as the focus on the whole process and the need for engaging several citizens by providing multiple participation channels. PB processes take place at multiple scales (municipal, national) requiring flexible deployment and dimensioning approaches. EMPATIA is an ICT platform for PB, based on multiple dimensioning and deployment options that reflect the scenarios where PB tools are expected to operate. The evaluation results provide relevant inputs to the design and implementation of similar participatory and/or voting platforms.

On-demand deployment of multiple aerial base stations for traffic offloading and network recovery

Unmanned aerial vehicles (UAVs) are being utilized for a wide spectrum of applications in wireless networks leading to attractive business opportunities. In the case of abrupt disruption to existing cellular network operation or infrastructure, e.g., due to an unexpected surge in user demand or a natural disaster, UAVs can be deployed to provide instant recovery via temporary wireless coverage in designated areas. A major challenge is to determine efficiently how many UAVs are needed and where to position them in a relatively large 3D search space. We first consider a discrete set of possible UAV locations distributed in a given 3D space and formulate the problem as a mixed integer linear program (MILP). Owing to the complexity of the MILP problem, we present an effective greedy approach that mimics the behavior of the MILP for small network scenarios and scales efficiently for large network scenarios. Afterwards, we propose and evaluate a more practical approach for multiple UAV deployment in a continuous 3D space, based on an unsupervised learning technique that relies on the notion of electrostatics with repulsion and attraction forces. We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.

03:36 PM Abstract No. 367 An ex vivo bench and perfused liver assessment and extension of the ablation zone by “EdgeBoost” with IR-Circle: a new bipolar ablation device

The InCircle bipolar ablation device has allowed for the ablation of tumours >3 cm (3-7cm), using a no-touch, ‘outside in’ approach. A limitation of this device is its relative unsuitability for interventional radiology use as the diameter of the probe needle is >2mm in the larger devices. The aim of this study was to create ablations via a rotational technique using a new minimally invasive probe whilst maintaining ablation size and sphericity. The new device entails deployable coils on one side of the probe with a reduced probe needle diameter of 1.6mm. The device ablates using the ‘outside in’ approach larger ablations are created using a rotational technique (e.g., retracting the coils, rotating 180° and redeploying). The devices used had 3×2cm or 3×1.5cm diameter overlapping coils on one side of a 1.6mm diameter probe needle. EdgeBoost was created via multi-dimensional redeployment of the bipolar coils in the same tract by rotating the device’s orientation and deploying coils lateral to the initial ablation area. Ex vivo bovine liver was used for all experiments. Multiple set deployment angles (0°, 180°, 90/270°, 45/315°, 135/225°) were used to test which rotation sequence would yield constant size and sphericity. Grey zone was measured to calculate ablation size. Perfused experiments were performed. Power mode generator was used until impedance was reached and the ablated liver was examined. A total of 37 ablations were performed. An ablation size of 7.5×6×7cm was achieved in 3 consecutive ablations using the 3x2cm diameter coils with a 1.6mm probe needle in an outside in approach via multi-dimensional EdgeBoost at 0/180° and 90/270° placements. Using the 0°, 180°, 45/315°, 135/225° dimensions, the mean ablation size for the 3x1.5cm probe ex-vivo was 4.5x4.2x5cm. When repeated in the perfused liver model, the mean ablation size was 5.1x5.1x5cm with a mean ablation time of 13.2 minutes. Using the 3x2cm probe in the perfused liver model, the mean ablation size was 5.8x5.4x5.7cm. The new probe shows promising potential to produce large ablations. Additionally, the EdgeBoost increases the ablation diameter by over 1 cm whilst maintaining relative sphericity.

A simulation-based heuristic that promotes business profit while increasing the perceived quality of service industries

This paper tackles the problem of minimising the direct server costs of complex multiple-server queueing systems while improving the quality perceived by customers. The problem is solved by finding the server roster, defined as the allocation of active individual servers in each time interval of the working day. As no closed solution is available at this time, it was obtained by simulation modelling. The complex queueing system was solved by a simulation-based heuristic that includes the dynamic arrivals, entity reneging and/or balking, multi-servicing, and the individual service-time distributions of servers. The distributions of the random variables of single or multiple-branch businesses were identified by sampling procedures for multiple company-branch deployment. The beneficial financial impact of the proposed solution is confirmed in numerical real-life banking scenarios. The quality perceived by customers (evaluated through the expected queueing time) was also improved. Finally, additional insights that might improve the system performance are highlighted.

Shading and masking affect the performance of photovoltaic systems—a review

Photovoltaic collectors in the second and in the subsequent rows in a multiple row deployment of PV fields are subject to two effects: Shading and masking both of which reduce the incident solar radiation, and hence reduce the electric energy generated by the PV field. Shading affects the direct beam incident radiation and masking (expressed by the sky view factor) affects the diffuse incident radiation on the PV modules. Both effects depend on field and collector geometric parameters. The result of these effects is uneven distribution of the incident solar radiation on the PV modules, manifested by formation of steps across the I-V characteristic. However, these two effects differ in their nature-shading depends on the movement of the sum and is time dependent whereas masking is position dependent and attains constant values, dependent on geometrical parameters only. Not much attention was paid in the past to the masking phenomenon and its effect on the power loss of PV systems. A series of recent works show that masking in PV fields is an emerging topic of technical significance. Masking may be more detrimental than shading, especially at locations with high percentage of diffuse radiation.

ACE: Availability-Aware CloudSim Extension

In the interconnected globe where service delivery is the success measure, cloud high availability (HA) is an indispensable area for enterprises. An HA-aware cloud system provides different approaches to handle the outages. This includes geo-redundancy, failover schemes, and HA-aware placement solutions. However, using real-cloud platforms to model HA-aware approaches is hindered by the configuration settings. To this end, simulation tools, such as CloudSim, can be used to evaluate HA solutions and a cloud resiliency against failures. CloudSim allows implementing of scheduling policies, but it does not support HA properties. This paper provides availability-aware CloudSim extension (ACE). ACE extends CloudSim with a graphical and textual modeling to ensure simplicity and reusability of cloud scenarios. ACE has added HA-aware modeling (HA metrics and failure/redundancy/interdependency models) and HA-aware scheduling (HA-aware placements, failover, repair, and load balancing policies) into CloudSim. With ACE, the creation of cloud scenarios is facilitated, and multiple HA-aware deployment solutions can be evaluated under different stochastic and deterministic events. ACE can assess the impact of different redundancy/failure models, and other performance policies to extract HA-aware lessons. In this paper, ACE is assessed on a cloud application to evaluate different redundancy/failure models and provide availability analysis of the HA-aware placement solution.

Compressive Sensing for Detecting Ships With Second-Order Cyclostationary Signatures

Amplitude modulation of the broadband propeller noise as a result of the cavitation yields a second-order cyclostationary ship-radiated noise. The spectrum of the modulating signal, consisting of the so-called propeller (or cavitation) tonals, enables the detection and the classification of the submarines or surface ships. However, data acquisition for this purpose causes vast data sizes due to high sampling rates and multiple sensor deployment. To mitigate the negative effects of this acquisition process such as on energy efficiency, hardware complexity, and storage capacity, we propose a scheme for compressive sensing of propeller tonals. We show that the spectral correlation function of cyclostationary propeller noise is sparse and derive a linear relationship between the compressive and Nyquist-rate cyclic modulation spectra, i.e., the approximation of spectral correlation function that allows utilizing matrix representations required in compressive sensing. It also enables use of the cyclic modulation coherence, i.e., the normalized cyclic modulation spectrum, to demonstrate the effect of compressive sensing in terms of statistical detection. We compare the recovery and detection performance results of the sparse approximation algorithms based on the so-called iterative hard thresholding and compressive sampling matching pursuit. Results show that compression is achievable without affecting the detection performance negatively. The main challenges are the weak modulation, low signal-to-noise ratio, and nonstationarity of the ambient noise, all of which reduce the sparsity level, hence causing degraded recovery and detection performance.

RETRACTED ARTICLE: K-Partitioned Smallest Distance Mining Tree for Path Optimation in Wireless Sensor Network

In wireless sensor networks (WSN), energy efficiency is an important issue, since the sensor nodes act as both data originator and data router. The sink functionality typically includes mining and sensing data from sensors in the network via multihop relays performing data processing. The nodes which are closer to the sink have to take more substantial traffic load consequently reduce their energy quickly leading to energy hole around the sink. Then the energy hole may cause failure in the sensor network and create large coverage hole. Sink repositioning can be performed using multiple sink deployment and sink mobility. Relocating or repositioning the sink is very challenging during the regular network operation. It is challenging in a multi-hop network environment to find an optimal location. Most of the existing localization solutions had low accuracy in congested situations and did not consider the resource limitations of WSN. To rectify the issues of sink repositioning, this work presents a K-partitioned smallest distance tree (k-PSDT) utilizing the ideal scan for setting an ideal number of sinks in sensor networks. At first, the quantity of sinks is resolved utilizing the ideal sink algorithm fulfilling the h-jump requirement. k-PSDT is developed for situating numerous sink nodes and setting up the courses. In the wake of deciding the ideal number of sink positions and directing, best sink reposition is chosen by ideal pursuit technique. Sink development is finished by utilizing the canny development, and it confines the sinks developments while keeping up their bearing to the ideal positions. The performance of the new techniques is implemented using network simulator (NS2). Simulation results show that the proposed technology do the better performance as compared to the existing method regarding the metrics such as average packet delivery ratio, delay, and energy consumption.

A case of perihilar cholangiocarcinoma treated with multiple metallic stent deployment

A case of perihilar cholangiocarcinoma treated with multiple metallic stent deployment

ABOUT METRICS OF SELF-SUFFICIENCY AND INTELLIGENCE OF ROBOTIC COMPLEXES AND CYBERPHYSICAL SYSTEMS

The common feature of cyberphysical systems (CFSs) and robotic systems (RSs) is availability of control systems. CFS is a more general concept, which includes also robots. There are several levels of management of the CFS, starting from the reflex level. For advanced self-contained s RCs the appropriate decision-making systems (DMSs) are designed at the logical level, in which logical inference and automatic construction of algorithms from the «If-then» products, corresponding to the elementary actions of the RTCs are performed. In the field of artificial intelligence the appropriate expert systems were engaged in a logical conclusion, and such systems are currently called «problem-solving and inference systems» or «reasoners». To evaluate their intellectual level the appropriate metric in the form of «variables rules» is used, and such metric is based on estimating the maximum possible number of combinations equal to the factorial of the number of decision rules (products). In this case, the evaluation criterion is «the worst case of complexity» with a complete search of all possible rules describing the situation. The autonomy of the RCs is determined by the intelligence of their control systems, which is expressed in the RC ability to make decisions in various situations. As a metric for evaluation of the intelligence of CFS and RC, it is proposed to use the metric of «variables rules». It is demonstrated that due to the new data representation and the linear computational complexity of the logical inference (without a search!), mivar reasoners make it possible on the ordinary computers to resolve the task of situational control for CFS and self-contained multi-level RCs of multiple purposes and deployment types with a speed of more than one million production rules per microsecond.

Fairness aware multiple drone base station deployment

The recent advances in drone technology significantly improved the effectiveness of applications such as border surveillance, disaster management, seismic surveying, and precision agriculture. The use of drones as base stations to improve communication in the next generation wireless networks is another attractive application. However, the deployment of drone base stations (DBSs) is not an easy task and requires a carefully designed strategy. Fairness is one of the most important metrics of tactical communications or a disaster-affected network and must be considered for the efficient deployment of DBSs. In this study, a fairness-aware multiple DBS deployment algorithm is proposed. As the proposed algorithm uses particle swarm optimisation (PSO) that requires significant processing power, simpler algorithms with faster execution times are also proposed and the results are compared. The simulations are performed to evaluate the performance of the algorithms in two different network scenarios. The simulation results show that the proposed PSO-based method finds the three-dimensional locations of DBSs, achieving the best fairness performance with a minimum number of DBSs for deployment. However, it is shown that the proposed suboptimal algorithm performs very close to the PSO-based solution and requires significantly less processing time.