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制度:新 ; 報告番号:甲3356号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2011/3/15 ; 早大学位記番号:新567

Improving Geopolymer Characteristics with Addition of Poly-Vinyl Alcohol (PVA) Fibers

This paper presents the benefits of PVA fibers in improving the mechanical properties of class F fly-ash-based geopolymer concrete. The activator used in the geopolymer was 8M sodium hydroxide (NaOH) and sodium silicate (Na2SiO3), with a mass ratio of Na2SiO3 to NaOH varied from 1.5 to 2.5. Cylindrical specimens with a diameter of 100 mm and a height of 200 mm were prepared for mechanical strength tests. The PVA fibers in the geopolymers were fixed at 0.4%, 0.6%, and 0.8% by total volume. Some mechanical tests were carried out, including compression, splitting, direct tensile and elastic modulus tests. It was found that the mixture with a ratio of alkalis of 1.5 and the PVA fiber content of 0.4% had the best workability. The highest compressive strength was obtained in a mixture with alkali activator ratios of 1.5 and 2.0, and with 0.6% fiber addition. The ratio of the tensile (and splitting-tensile) strength to compressive strength was found to increase with the certain amount of PVA fibers and the ratio of the alkali activators. The workability issue and fiber direction in the concrete were the dominant factors influencing the properties of geopolymer concrete

PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV

Source at https://ctsoc.ieee.org/In this paper, we consider a wireless sensor network of nodes at the sea surface drifting due to wind and sea currents. In our scenario an Unmanned Aerial Vehicle (UAV) will be used to gather data from the sensor nodes. The goal is to find a flyable path which is optimal in terms of sensor node energy consumption, total channel throughput between the UAV and sensor nodes, flight time for the UAV and frequency of the node visits by the UAV. Finally, the path should also be optimal concerning node position estimation uncertainty. A Kalman Filter (KF) is used to estimate the nodes motions and Particle Swarm Optimization (PSO) is the method used to calculate the UAV path taking all of these objectives into account. The proposed node tracking aware path planning solution is compared to two other scenarios: One where the path planning is based on full knowledge of the node positions at all times, and one where path planning is based on the last known positions of the nodes

Evaluations of UAV-enabled FSO Communications in the Arctic

This paper investigates the feasibility of using free space optics (FSO) for communication between multiple hovering unmanned aerial vehicles (UAVs) and a detection unit (DU) in the Arctic. The uniqueness that sets apart UAV-based FSO systems from conventional FSO systems is the dynamics of the system since location and inclination of the UAV changes over time due to wind load and UAV oscillations. The envisioned scenario consists of UAVs equipped with laser diodes and a DU mounted on top of a ship. We propose an application scenario of search and rescue (SAR) operations in the High North. In the system design, the SAR team establishes communication with UAVs using radio frequency multiple-access links while DU demodulates the information from the incoming FSO fronthaul signals. Furthermore, statistical models for the FSO channel, random position and orientation fluctuations, snow, and fog have been derived. This work amplifies the need and possibility of enabling enhanced accessibility and connectivity in the Arctic utilizing UAVs and FSO

Path planning for UAV harvesting information from dynamical wireless sensor nodes at sea

A system of several wireless sensor nodes and one unmanned aerial vehicle (UAV) is considered in this research. The nodes are only floating and drifting with the sea stream. The UAV will be operating as a data mule to gather sensing information from wireless sensor nodes. Unlike prior studies, this paper addressed a realistic ocean model for the nodes movements which will be the references to the Kalman Filter (KF) in estimating for the nodes’ positions. Simulation results are evaluated for an optimal flight-able path for the UAV under several constraints by particle swarm optimization (PSO). Specifically, the deviation between the estimated positions and the referenced positions, total energy consumption by the sensors network, data rates between UAV and the nodes, flight time for the UAV, and frequency of visiting the nodes by the UAV will be considered for optimization. The systems performances will be evaluated based on these scenarios: a) an ideal and unrealistic scenario where the UAV follows the nodes continuously; b) a realistic case where the UAV only flies periodically. Discussions and solutions were also addressed for the situations when the deployed nodes are more significantly separated than the cases simulated in the paper.acceptedVersio

Enhancement of Vehicular Visible Light Communication Using Spherical Detector and Custom Lens Combinations

Vehicular Visible light communication (VLC) technology has recently attracted much interest from researchers and scientists. This technology enables connectivity between vehicles and infrastructures along the road by using vehicles’ headlights and taillights as wireless transmitters. The reliability of vehicle-to-vehicle (V2V) VLC systems is affected by several factors, such as car mobility, optics system design, and visibility conditions, where the first two have the most impact on the VLC system performance. This paper, therefore, focuses on the relative positions of the cars and the design of the optics, especially on the receiving end, which has been proposed with the use of a polar detector instead of the rectangular detectors commonly used in the literature. We investigate the achievable gain compared to the conventional detector for different vehicle locations, utilizing a professional optical system design and ray tracing approach. Then, to improve the performance, we introduce the utilization of an imaging receiver by integrating the polar detector with different optical commercial lens combinations, such as Fresnel and Aspherical lenses. To further improve the V2V system performance, we propose a novel optical lens combination design by integrating double-convex lens with half-Plano-concave lens, which allows the correction of more optical aberrations, such as chromatic and spherical aberration. Utilizing the non-sequential ray tracing tools, we designed these VLC systems and perform a realistic channel modeling study considering the typical 3D CAD models of vehicles and roads as well as the possibility of horizontal and vertical movement between the vehicles. Based on the channel impulse responses (CIRs) obtained from the ray tracing simulations, we analyzed the performance of V2V VLC systems with all lens combinations at different vehicle positions on the road. We further investigated the impact of different system parameters on the overall V2V system performance, such as receiver diameter and bandwidth. The obtained results demonstrated that with a carefully chosen system and lens parameters, the proposed system design of lens combination provides an enhancement of up to 7 dB in total received power compared to the case without a lens. Our results also revealed that the proposed system design outperforms the benchmark ones for all lateral displacements and longitudinal distances

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance

Improving Geopolymer Characteristics with Addition of Poly-Vinyl Alcohol (PVA) Fibers

This paper presents the benefits of PVA fibers in improving the mechanical properties of class F fly-ash-based geopolymer concrete. The activator used in the geopolymer was 8M sodium hydroxide (NaOH) and sodium silicate (Na2SiO3), with a mass ratio of Na2SiO3 to NaOH varied from 1.5 to 2.5. Cylindrical specimens with a diameter of 100 mm and a height of 200 mm were prepared for mechanical strength tests. The PVA fibers in the geopolymers were fixed at 0.4%, 0.6%, and 0.8% by total volume. Some mechanical tests were carried out, including compression, splitting, direct tensile and elastic modulus tests. It was found that the mixture with a ratio of alkalis of 1.5 and the PVA fiber content of 0.4% had the best workability. The highest compressive strength was obtained in a mixture with alkali activator ratios of 1.5 and 2.0, and with 0.6% fiber addition. The ratio of the tensile (and splitting-tensile) strength to compressive strength was found to increase with the certain amount of PVA fibers and the ratio of the alkali activators. The workability issue and fiber direction in the concrete were the dominant factors influencing the properties of geopolymer concrete

Throughput Optimization for NOMA Energy Harvesting Cognitive Radio with Multi-UAV-Assisted Relaying under Security Constraints

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints