グエン キエン

This paper proposes a load-independent high-frequency wireless power transfer (WPT) system. The proposed system achieves mode switching between constant current (CC) and constant voltage (CV) output modes, which can be applied to rechargeable batteries. In the proposed system, a single switch component is added on the receiver side to change the topology of the compensation circuit. As a result, the switching between CC and CV modes is accomplished on the receiver side, which means wireless communication about the load-information feedback from the receiver to the transmitter becomes unnecessary. By achieving zero-voltage switching (ZVS) in both CC and CV modes through the load-independent operation, the proposed system achieves high power-delivery efficiency even at high frequencies. Therefore, the proposed WPT system contributes to cost reduction, system simplification, and circuit downsizing. The experimental results agreed with the analytical predictions quantitatively, which verified the effectiveness of the proposed circuits.

This paper presents the design of the class-Φ3 power oscillator. The topology of the proposed power oscillator features the feedback network from the resonant capacitance, which allows for the application of wireless power transfer (WPT) systems. Furthermore, the required phase shift necessary for applying the proposed feedback network is achieved by employing the class-Φ3 inverter, which has a finite input inductance. Closed-form design equations for the proposed power oscillator are provided, which allows a quick and intuitive design. An experimental verification is conducted with the prototype power oscillator. The measured power-conversion efficiency was 92.2 % with 70 W output power at 1 MHz operation. The experimental results agreed quantitatively with the analysis, which shows the validity of the design.

This study explores the intersection of neuroscience and computer science, focusing on the use of spiking neural networks (SNNs) to simulate the behavior of biological neurons. A neural network model based on the Izhikevich neuron model is proposed to simulate the local auditory network of crickets. The parameters of the neuron model are optimized based on evaluation functions and identified by Particle Swarm Optimization (PSO), aligning its input-output relationships with the observed cricket neuron responses. The results showed that the network successfully simulated the behavior of individual neurons, promising applications in fields like neural prosthetics.

This paper proposes a high-frequency multiple-receiver wireless power transfer (WPT) system with a load-independent class-E/F inverter. Each receiver has a post-regulator, which changes the equivalent resistance seen from the inverter to obtain the necessary power for output voltage regulation. Because the load-independent class-E/F inverter generates constant AC current ((Formula presented.)), the transmitter supplies the minimum required power to the receivers by the change of the equivalent resistances. Besides, the load-independent inverter consistently achieves zero-voltage switching (ZVS) without any control. As a result, no information feedback by wireless communication is necessary for output regulation and ZVS achievement, simplifying the system configuration and improving the transient response of the control. This paper presents analytical expressions of the proposed system. Besides, the experiment was carried out with a two-receiver WPT system. The implemented system worked well by individual and independent output regulation of the post regulator at each receiver. The implemented WPT system achieved the power-delivery efficiency of 83.4% at the 6.78 MHz transmission frequency and the total output power of 40 W.

Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.

This paper proposes the load-independent (LI) class-E frequency multiplier along with a unified circuit analysis method with the LI class-E amplifier. A circuit-parameter determination strategy is presented to achieve LI operation and maximum power output capability at the rated condition. We designed the class-E amplifier and frequency doubler using the unified analytical expressions. Both the implemented circuits achieved the LI operation, namely constant output voltage amplitude and zero-voltage switching against load variations without any control. The experimental results showed quantitative agreements with the analysis results, namely waveforms and power conversion efficiency, which indicates the validity of the derived analytical expressions and design procedure.

An increasing number of devices are connecting to the Internet via Wi-Fi networks, ranging from mobile phones to Internet of Things (IoT) devices. Moreover, Wi-Fi technology has undergone gradual development, with various standards and implementations. In a Wi-Fi network, a Wi-Fi client typically uses the Transmission Control Protocol (TCP) for its applications. Hence, it is essential to understand and quantify the TCP performance in such an environment. This work presents an emulator-based approach for investigating the TCP performance in Wi-Fi networks in a time- and cost-efficient manner. We introduce a new platform, which leverages the Mininet-WiFi emulator to construct various Wi-Fi networks for investigation while considering actual TCP implementations. The platform uniquely includes tools and scripts to assess TCP performance in the Wi-Fi networks quickly. First, to confirm the accuracy of our platform, we compare the emulated results to the results in a real Wi-Fi network, where the bufferbloat problem may occur. The two results are not only similar but also usable for finding the bufferbloat condition under different methods of TCP congestion control. Second, we conduct a similar evaluation in scenarios with the Wi-Fi link as a bottleneck and those with varying signal strengths. Third, we use the platform to compare the fairness performance of TCP congestion control algorithms in a Wi-Fi network with multiple clients. The results show the efficiency and convenience of our platform in recognizing TCP behaviors.

Along with the process of urbanization and mechanization, environmental pollution is becoming more and more serious all over the world. To this end, numerous efforts are directed toward evolving effective monitoring operations, including both stationary and mobile systems. Compared to traditional fixed monitoring stations, mobile air quality monitoring systems offer a more flexible and cost-effective way to achieve a fine-grained air quality map. However, as mobile air quality monitoring systems typically rely on lightweight devices with low-capacity batteries, conserving the energy of these devices becomes a significant challenge. A trivial method to reduce devices’ energy is to set a low-frequency sampling rate and allow the device to go to sleep during the idle period. Nonetheless, this method may diminish the temporal granularity of the gathered data. To this end, in this paper, we propose a deep learning-based approach that adaptively regulates the activities of devices to simultaneously accomplish two goals: energy conservation and data quality assurance. The primary idea is to allow devices to go to sleep when the fluctuations of air quality indicators are minimal and to use a predictive model to forecast the air quality during the idle period. We evaluate our proposal on Fi-Mi, an actual bus-based air monitoring system in Hanoi, Vietnam. Experiment results indicate that our proposed method saves approximately 42% of the device’s energy consumption with an air monitoring error of only 3%.

Global air pollution is becoming increasingly severe. In this context, monitoring air quality at all times and locations is necessary. Traditionally, air quality is monitored using stationary monitoring stations. However, this approach has an inherent shortcoming: limited monitoring locations. Crowdsensing-based air monitoring has recently emerged as a promising alternative that expands monitoring coverage in both temporal and spatial dimensions through the collaboration of numerous participants. Typically, participants in crowdsensing systems are compensated for the data they provide. One of the critical challenges in handling a crowdsensing system is minimizing the cost while guaranteeing the quality of the data collected. For crowdsensing-based air monitoring systems, data quality refers to the temporal and spatial coverage corresponding to the locations and times the data was collected. In this study, we propose a solution based on deep reinforcement learning that simultaneously optimizes two goals: maximizing coverage range and minimizing costs. Our proposed solution is one of the first attempts to optimize both of these objectives for crowdsensing-based air monitoring systems. Compared to other algorithms, experimental results indicate that the proposed solution can increase coverage by more than 30% and reduce cost by more than 70%.

In recent years, there have been an increasing number of IoT applications, such as smart cities, which benefit human lives. The IoT applications face security and privacy protection challenges since they usually adopt a centralized structure (i.e., client-server architecture). The blockchain technology, with decentralized and trustworthy guarantee characteristics, has the potential to solve such IoT challenges. Hence, there are efforts to integrate IoT and blockchain into IoT-Blockchain systems, where the IoT devices are normally battery-powered and low-resource. Therefore, it is essential to understand the performance of IoT devices in cooperating with blockchain, which generally requires significant resources (e.g., computing for consensus). In this paper, we first build a private IoT. Blockchain system using the Ethereum framework and Raspberry Pi4 (RPi4) as IoT devices. We then evaluate the system with two consensus algorithms (i.e., Proof of Work (PoW) and Proof of Authority (PoA)), considering CPU, memory, disk, and power consumption of the IoT devices. The evaluation results show that the power consumption of PoW is double PoA's. Using the mining time data from running PoW on the RPi4, we calculated the expected mining time through curve fitting.

In recent years, the sharp growth of data volume has gradually driven communication networks to support more and more high-speed transfers. Accordingly, there are many efforts and interests in researching and developing such high-speed networks. Since Transport Control Protocol (TCP) plays a critical role in reliable communication, it is essential to investigate TCP performance in high-speed environments. This paper performs a measurement and performance analysis of TCP congestion control algorithms (CCAs) in an actual 10 Gbps Ethernet network setup. Unlike the other works, we have extensively investigated 14 CCAs available on Linux kernels in two evaluations: a small number (one and two) of flows and a large, varying number of flows. In the former, we verify CCAs’ responsiveness, fairness, and backward compatibility. In the latter, we measured fairness and throughput under the situation where the number of TCP flows varied by up to 10 flows. The results show that BBR is the best in responsiveness and fairness, while Illinois achieves the best backward compatibility in the two-flow experiments. On the other hand, CDG has the best performance in the evaluations with the large number of flows.

Software-defined networking (SDN) enables network automation and flexibility by offering programmability. However, SDN was originally designed for Ethernet, with its core being an SDN switch controlled by an SDN controller. Hence, integration of wireless interfaces to the SDN switch, which may bring appealing SDN features to wireless networks, is not straightforward. The root cause is the MAC address (of the Ethernet port) may be overwritten by the wireless interface’s one, leading to a MAC address mismatch problem for wireless communication. This paper presents a way to seamlessly integrate a wireless interface into an SDN switch by developing an Automatic ARP Header Modification Algorithm, which avoids the mismatch problem. We realize seamless integration with Open vSwitch (OVS), Pox controller, and a Wi-Fi card. Moreover, we show the usefulness of the integration in a wireless system with TCP and UDP communications. The results show that our algorithm successfully solves the mismatch problem under our wireless environment.

This paper presents a high-frequency constant current (CC) and constant voltage (CV) output load-independent wireless power transfer (WPT) system. A single switch component on the rectifier changes the CC and CV modes while maintaining zero-voltage switching (ZVS) operation. The proposed circuit eliminates the need for wireless communication from a receiver to a transmitter against load variations. Therefore, the proposed circuit contributes to reducing cost, simplifying systems, and downsizing circuits. The circuit experiments were conducted to verify the effectiveness of the proposed circuits. The obtained results validated the design approach.

This paper proposes a load-independent high-frequency wireless power transfer (WPT) system for multiple receivers with a single transmitter. The proposed system achieves zero-voltage switching (ZVS) at the transmitter and constant output voltages at all the receivers against load variations without any control. Moreover, the proposed system keeps the load-independent operation even when the number of receivers changes. The circuit analysis for the proposed WPT system is given. Experimental verifications for the proposed WPT systems with two receivers were conducted, which showed the effectiveness. The implemented WPT system achieved a peak efficiency of 83.4% at 6.78 MHz and total output power of 50 W.

In this research, we propose a reconfigurable load-independent class-E inverter. Adding only one extra mode switch, the proposed inverter can switch between constant voltage (CV) and constant current (CC) output against load variations. Moreover, for both modes, zero-current switching (ZCS) can be maintained despite load changes, which makes the proposed inverter a suitable candidate for wireless charger applications. For achieving load-independent conditions for both modes, the circuit-components derivation method is proposed in this research. Additionally, an experimental prototype was implemented for verifying the validity of the proposed inverter. The experimental prototype achieved CC and CV output with ZCS maintained despite load variation, which proved the effectiveness of the proposed inverter.

In the globalization era, a university's homepage on the Internet has become more critical since it can give an insight into the technologies valued by the university. In this research, we compared the university homepages/websites in Germany and Japan. We aimed to learn about the differences between these countries' university websites. We first compiled a list of 492 German and 716 Japanese university websites. We then conducted experiments where users from two countries accessed all the websites. We gathered and analyzed the performance metrics related to technical and loading speed-related data for comparison. The results show that Japanese websites perform better regarding returned HTTP error codes, fewer redirects, and smaller download sizes. Regarding the loading speed, we observed that the Japanese websites could be accessed faster on average, locally and from overseas, than their German counterparts.

This paper proposes a load-independent wireless power transfer (WPT) system for multiple receivers with fixed coupling coils. The proposed WPT system consists of the single load-independent class-E inverter with LCC filters and multiple receivers based on a current-driven class-D rectifier. In this system, the output power can be individually designed according to the circuit parameter of the LCC filter independent of turns ratio of each coupling coil. It's possible to use the fixed receiving coil in all receivers. The experimental results confirmed the maintenance of constant output dc-voltages and the ZVS operation even with load variations. The effectiveness of the proposed WPT system indicated by the demonstrations.

To address the communication congestion and high energy consumption issues in IoT networks, prior research has proposed a distributed information processing system called WiBIC. As an exploratory step towards implementing the basic functionalities of the WiBIC system, this study focuses on the wireless integration of SNNs. Considering the data transmission and learning characteristics of SNNs, we propose using the APCMA protocol to effectively stabilize the operation of the wireless SNN. By modeling Pavlovian conditioning on this network, the learning capabilities of the implemented system can be determined.

In recent IEEE 802.11 WLANs, an Access Point (AP) can use the Channel Bonding (CB) technique to increase the transmission bandwidth. Given limited bandwidth resources, it is not trivial to allocate channels for densely deployed APs to fairly meet their diverse traffic demands. In general, the non-overlapping channel allocation approach has been proven to be an ideal solution for maximizing the achievable throughput of WLANs using CB. However, we argue that the approach is no longer efficient because of two common situations: i) insufficient non-overlapping channels and ii) imbalanced traffic demands. Instead, the overlapping channel allocation is more suitable in such situations. In this study, we first apply a continuous-time Markov Chain (CTMC) model to analyze the effect of overlapping channel allocations for neighboring APs under unsaturated traffic conditions. Based on the analysis results, we propose a novel channel allocation algorithm based on the Bin-Packing Problem (BPP) that maximizes the fairness of the throughput-to-demand satisfaction ratio between APs. Through simulations, we demonstrated that the proposed algorithm can achieve near-optimal performance in various network settings. The algorithm also outperformed existing algorithms with the same overlapping channel approach.

This paper proposes a load-independent (LI) constant-current (CC)/zero-current-switching (ZCS) inverter with a series resonant (SR) filter. The conditions for realizing the LI operation are derived through the analysis of the proposed inverter. The circuit experiment was conducted to verify the effectiveness of the proposed inverter. The proposed inverter achieved high power output capability and low total harmonic distortion (THD) output compared with the LI class-EF inverter. The implemented prototype achieved 93.7% efficiency at 1 MHz frequency with 76.5 W output power in the rated condition.

This study proposes a self-tuned series resonant power oscillator capable of load-independent operation. The proposed power oscillator has a self-tuning feedback loop that provides a gate-driving voltage with a constant phase shift and amplitude. Therefore, it is robust against component tolerances in a series resonant filter. It is also robust against load variations owing to its load-independent design. Consequently, it maintains a high-efficiency and constant AC output against load variations and component tolerances. Experimental results demonstrated the effectiveness of the proposed power oscillator.

This paper presents a simulator-based design-optimization software for high-frequency power-electronics circuits, which consists of the heuristic algorithm, magnetic-component design tool, and commercial simulator, SPICE. Because we can use the SPICE component library, the software suggests high-accurate design results, and the experimental adjustments can be reduced. Besides, by using the heuristic algorithm, the optimization algorithm converges stably. The magnetic-component design tool provides a precise estimation of the power losses, which is important for high-frequency power-electronics circuit designs. As an example, we design the class-DE inverter by applying the developed software, which shows the usefulness of the software.

The evolving Internet of Things (IoT) promisingly improves the quality of life and transforms many industries. However, the IoT application challenges the wireless networks since the resource-constrained IoT devices typically need to send data to the cloud or edge server. Therefore, it is necessary to introduce an intermediate device between IoT devices and the servers, for example, to reduce the cost of direct communication between them. In another case, the device may move and collect the data from IoT devices before transmitting it to the server. The intermediate device should be designed to have resilient Internet connections and sufficient bandwidth in such a context. This work implements and evaluates a Multipath TCP (MPTCP) IoT router, which uses multiple radios to connect a server to address the demanding design. The router leverages MPTCP, an extension of TCP for simultaneous transmission over several paths on top of Wi-Fi interfaces. MPTCP has also supported several working modes for throughput and (or) resilience enhancements. First, we implement the MPTCP kernels, which can run on the popular IoT devices Raspberry Pi 3B+ and 4. Second, we extensively evaluate the performance of IoT routers in a static and mobility scenario. The static scenario’s evaluation results show that the MPTCP-based router can achieve seamless handover and bandwidth aggregation. In the mobility scenario, the MPTCP router with one backup path performs better than the single-path TCP. Besides, the MPTCP routers are more energy-efficient than TCP on the same hardware.

In the era of 5G and beyond, mobile devices usually can access several heterogeneous wireless networks (e.g., Wi-Fi and 5G). To simultaneously and efficiently utilize the accessible network resources, muli path transport protocols, such as MPTCP and MPQUIC, have shown much potential. In these protocols, scheduling is one of the critical processes to ensure the performance of the multipath transmission. Although there have been many proposed multipath schedulers in the literature, they have not performed well in heterogeneous networks, especially when the network conditions vary (i.e., dynamicity). In this paper, we propose a novel Q-learning-based Multipath scheduler for data transmission optimization (Q-SAT), aiming to bypass the existing limitation. By leveraging the self-learning ability of reinforcement learning, Q-SAT can instantly observe environmental changes and deploy appropriate path selection to optimize data transmission time. As a result, Q-SAT efficiently schedules multipath communication in heterogeneous wireless networks with different dynamicity levels. We have implemented Q-SAT with MPQUIC and extensively evaluated Q-SAT in an emulated environment and a real network. The evaluation results show that Q-SAT improves the data transmission time by at least 10% in the emulation and 26% in the actual deployment compared to the state-of-the-art schedulers.

Multipath QUIC (MPQUIC), an emerging multipath transport protocol (MTP) that inherits the advantages of the canonical multipath TCP (MPTCP) and the widespread QUIC, potentially plays a vital role in 5G and beyond. MPQUIC can exploit multiple networks (e.g., Wi-Fi, LTE, 5G) on a mobile device to boost the quality of services while efficiently utilizing network resources. In MPQUIC, the scheduler, which is in charge of concurrently scheduling data transmission in several paths, largely impacts the protocols' performance, especially in dynamic environments. In fact, in the literature, a considerable number of MTP schedulers for MPQUIC have been proposed. Unfortunately, their performances have been primarily evaluated in static networks without (or simply) considering mobile ones. Hence, this work attempts to investigate the performance of MPQUIC schedulers in the mobile context, aiming to fill the literature gap. Specifically, we implement and assess the performance of five MPQUIC schedulers in various mobility patterns using the Mininet-WiFi emulator. More importantly, we introduce q-ReLeS, an extension of an MPTCP scheduler called ReLeS for MPQUIC. The experimental results show that q-ReLeS reduces the download time from to compared to the others. Besides, the empirical investigation demonstrates that mobility and velocity patterns substantially impact the performance of MPQUIC schedulers.

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC's subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.

Abstract Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model’s hyper-parameters. This work proposes a novel deep learning-based Q–H prediction model that overcomes all the shortcomings encountered by existing approaches. Specifically, to address data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction model’s optimal hyper-parameters. We conducted extensive experiments on two datasets collected from Vietnam’s Red and Dakbla rivers. The results show that our proposed solution outperforms current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifically, by exploiting the ensemble learning technique, we can improve the NSE by at least $$2\%$$. Moreover, with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more than $$5\%$$. Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least $$6\%$$ and up to $$40\%$$ in the best case.

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks' lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and the charging time to avoid node failure in WRSNs. We formulate the optimization and propose a solution for that. The proposal includes an algorithm to find a list of potential charging locations and their associated optimal charging time. Moreover, the Q-learning technique is adopted to determine the optimal next charging location among the candidates. We implement the algorithm and conduct experiments to compare it to the related works. The results show that the proposed algorithm outperforms the others in terms of network lifetime. Specifically, the highest performance gap of our proposal to the best algorithm among the others is 8.29 times.

With the development and commercialization of new mobile network generations such as 5G and beyond, future communications are shifting from the traditional single-path paradigm to multipath transport protocols such as MPTCP and MPQUIC. One of the most critical issues in dealing with the multipath transmission is appropriately scheduling the pathways in order to guarantee QoS. Despite the fact that tremendous effort has been put into developing multipath scheduling algorithms, existing approaches suffer from several limitations when dealing with the network's dynamicity, including congestion and packet loss. In this paper, we propose a novel Reinforcement learning-based multipath transport protocol named SATO, which efficiently schedules multipath communication in heterogeneous wireless networks. By leveraging the self-learning ability of reinforcement learning, a node equipped with SATO can capture the environmental changes and select transmission paths based on an appropriate policy to optimize QoS. Our evaluation results show that SATO improves the QoS by 10%-15% in simulation and 12% in a real deployment compared to the state-of-the-art algorithm.

In this research, a novel load-independent Class-E/F inverter with constant voltage (CV) and constant current (CC) output modes is proposed. By adding one auxiliary switch to the class-E/F inverter, the proposed inverter can change into the class-E topology. A components design method is proposed to achieve load-independent conditions for both topologies by merging the design conditions of the load-independent class-E and class-E/F inverters together. Therefore, the proposed inverter can switch between CV and CC output modes against load changes at the same switching frequency. Additionally, zero-voltage switching (ZVS) can be maintained for both modes despite load variation. The proposed inverter has a simplified topology, making it suitable for minimized wireless power transfer applications as the transmitter part. A 1 MHz experimental prototype circuit was designed and implemented. A circuit experiment was conducted, which confirmed the usefulness of the proposed inverter.

We proposed the concept of implementing Spiking Neural Network (SNN) dynamics in information networks, such as Internet of Things (IoT) networks, by incorporating information processing by neurons in IoT devices. In this manuscript, we investigate the relationship between the TDMA (Time Division Multiple Access) subslot width and the learning ability of a wireless SNN (WSNN) in the WSNN system with TDMA and IEEE802.15.4e IoT communication. we investigate the relationship between the TDMA subslot width and the learning ability of a WSNN, which combines TDMA (Time Division Multiple Access) with IEEE802.15.4e-compliant wireless communications for the IoT. It becomes clear that the lower bound of the subslot width is closely related to the number of network firings. Besides, it is shown that there is a threshold that separates successful learning from unsuccessful learning.

This paper proposes the maximum efficiency tracking for the multi-receiver wireless power transmission (WPT) system. The proposed system is designed to satisfy zero-voltage switching (ZVS) for load variations and coil misalignment, regulating the output voltage. The control strategy for the multiple-receiver WPT system is suggested in this paper. The experiment with the two-receiver WPT system is conducted to show the effectiveness of the proposed system.

This paper presents a design procedure for wireless power transfer (WPT) systems with the load-independent class-E inverter family. The design procedure guarantees that the WPT system will achieve constant output and soft switching in response to load variations without particular control, which can apply not only single-hop single-output but multi-hop multi-output WPT system designs. It is analytically explained that a WPT-system output type is determined by properly selecting inverter, rectifier, and coupling resonant structure. As a concrete design example, we designed the three-hop three-output WPT system for installation into a robot arm. Experimental results exemplified the validity and usefulness of the established procedure.