As an essential aerial platform in Internet of Things (IoT) applications, UAV swarms require high-precision attitude estimation in GPS-limited and dynamic environments, which supports higher-level IoT functions such as smart logistics, disaster response, and environmental monitoring. However, most odometry-based pose estimation methods in dynamic scenarios without GPS encounter issues with cumulative errors over time. In this paper, we propose a method to reduce these cumulative errors by constraining the absolute positioning of Visual-Inertial Odometry (VIO) using relative poses obtained from multi-view landmark and Resonant-Beam (RBeam) sensors between UAVs. For synchronous moments, we estimate the 6 Degree-of-Freedom (DoF) relative pose using the Angle of Arrival and Time of Flight data from the RBeam, combined with nonlinear optimization. For asynchronous moments, a two-stage visual estimation method is introduced, combining multi-camera epipolar geometry for rotation recovery and depth reconstruction optimization for translation recovery, enabling the estimation of asynchronous relative poses. Finally, we design a global objective function based on a sliding window and factor graph, integrating RBeam, multi-view landmark, and VIO for absolute 6-DoF pose optimization of the UAV swarm. Simulation results demonstrate that optimizing with the addition of RBeam synchronous relative poses improves overall positioning accuracy by 32.94% compared to pure VIO. Incorporating both RBeam synchronous and visual asynchronous relative poses further enhances overall positioning accuracy by 37.65%. Additionally, the UAV’s attitude benefits from the rotational constraints provided by the RBeam, achieving over 30% improvement in the pitch and yaw directions.

With the rapid development of the internet of things (IoT), location-based services are becoming increasingly prominent in various aspects of social life, and accurate location information is crucial. However, RF-based indoor positioning solutions are severely limited in positioning accuracy due to signal transmission losses and directional difficulties, and optical indoor positioning methods require high propagation conditions. To achieve higher accuracy in indoor positioning, we utilize the principle of resonance to design a triangulation-based resonant beam positioning system (TRBPS) in the RF band. The proposed system employs phase-conjugation antenna arrays and resonance mechanism to achieve energy concentration and beam self-alignment, without requiring active signals from the target for positioning and complex beam control algorithms. Numerical evaluations indicate that TRBPS can achieve millimeter-level accuracy within a range of 3.6m without the need for additional embedded systems.

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications.

Simultaneous wireless information and power transfer (SWIPT) is considered a promising technology for the future of the internet of things (IoT), with significant potential to enable advancements in both smart living and smart production. As IoT devices often operate wirelessly using various types of radiation, such as radio frequency (RF), microwave, laser, etc, the topic of human-safe irradiance is getting increasingly important. The resonant beam system (RBS) exploits the self-aligning characteristics of spatially distributed laser resonators to simultaneously achieve high-power and high-capacity SWIPT. The self-protection RBS, equipped with an adaptive phase adjuster, further mitigates safety concerns by reducing the irradiance on the invasion object. However, the power optimization under safety restrictions of this system remains underexplored, leaving significant potential for further enhancement of its performance. In this paper, we establish a theoretical model of RBS with an adaptive phase adjuster, including the irradiance on the invading object. Furthermore, based on the proposed model, we construct an optimization framework using alternating optimization to refine the system parameters, which are equivalent reflectance of the output reflector, radius of the beam on the gain medium, and split ratio of the protective beam. Finally, the numerical results demonstrate that the system’s optical power output is enhanced by 19.91% while adhering to safety limits for the human skin of 1 W/cm2 in the standard “safety of laser products IEC 60825-1.”. As a SWIPT system, it can deliver an average of 5 W of electric power and achieve a spectral efficiency of 14.7 bps/Hz with 300 W input power over a distance of 9 m.

In the era of the Internet of everything (IoE) and the metaverse, there is a growing demand for high-accuracy indoor positioning for applications such as autonomous robots, virtual reality, and smartphones. This paper proposed a resonant beam phase-based passive localization (RBPPL) system optimized for high-precision indoor positioning in multi-access scenarios. By leveraging the self-alignment characteristic and integrating the analysis of resonant beam phase, angle of arrival (AoA) matching and binocular disparity method for 3D point coordinate acquisition, the RBPPL system achieves binocular passive multi-access 3D positioning with an error within 4 cm at a distance of 8 m. We present a novel multi-access AoA estimation method that overcomes the challenges of spot overlap in traditional CMOS-based angle analysis. We propose a telescope system to correct the phase and focus the propagation direction of optical resonant beam systems. Simulations demonstrate the system’s robustness and high accuracy. The proposed RBPPL system, optimized for multi-access scenarios, offers a promising solution for high-accuracy indoor positioning, supporting various IoE and metaverse applications. Future work will focus on real-world deployment and its potential in complex multi-access scenarios.

The relative localization (RL) of unmanned aerial vehicle (UAV) swarms has gained considerable traction within the realm of UAV formation control. However, most existing RL methods face the challenge of balancing accuracy and computational power in unknown environments. The Resonant Beam (RB) system, which features energy-concentrated and selfaligned transmission can be used for positioning with centimeterlevel accuracy. In this study, we improved the RB system through a double complementary metal-oxide-semiconductors (CMOS) design and proposed a corresponding calculation method to simultaneously obtain the relative position and relative attitude angle, making it adapt to RL problems of swarms. Building upon these advancements, we design a UAV system to solve the three-dimensional RL problems. For large-scale swarms and indirect RL situations, we propose an RB-featured direct-indirect link optimization algorithm considering dynamic correction to improve positioning accuracy and meet real-time requirements. The simulation results show that our root mean square error (RMSE) is within 4cm when the UAV reaches a maximum distance of 30m and is not adjacent to the baseline UAV. This represents a notable 24.2% improvement over the shortest path algorithm. Compared with using LiDAR only for RL, our method offers higher accuracy and achieves millisecond-level speed without requiring GPU computing power.

Simultaneous lightwave information and power transfer (SLIPT) is increasingly vital in the burgeoning field of mobile Internet of Things (IoT) technologies. It offers a dual advantage: high-power wireless charging and high-rate data communication, essential for IoT devices. Resonant beam system, utilizing spatially separated laser resonators (SSLR), presents a promising solution. These systems enable the creation of resonant beams without necessitating beam steering devices or alignment/tracking processes, even when the receiver’s location changes. However, a critical challenge in deploying these systems within IoT scenario lies in their limited field of view (FoV) and coverage angle. In this paper, we present a design strategy for integrating concave mirrors with lenses in a cat’s-eye retroreflector configuration. This adjustment significantly enhances the FoV of the resonant beam SLIPT system, resulting in more efficient and broader system coverage. Through an analysis grounded in optical field propagation, we evaluate the FoV performance of the resonant beam SLIPT system. The numerical results show that with a retro-reflector dimension of 2.5 mm, our proposed system demonstrates the capability of conducting watt-level electrical power transfer, coupled with a communication capacity of approximately 10-bit/s/Hz under a 6.87° FoV. This substantial improvement in FoV not only addresses the current limitations but also provides new avenues for resonant beam SLIPT systems in a wide range of IoT applications.

In industrial environments where pedestrians and vehicles closely interact, existing point positioning systems often yield inaccurate results by disregarding crucial vehicle information, such as shape and angles, thereby compromising effective early warning mechanisms. Moreover, existing positioning systems relying on visual or radar methodologies also have limitations due to environmental factors. To address these challenges, this article introduces a novel pedestrian and vehicle area positioning and warning system base on multiple ultrawideband (UWB) signals, offering detailed information about vehicle positioning, orientation, and shape. Employing multiple UWB tags alongside the two-way ranging (TWR) algorithm, this system models the vehicle’s positioning area and triggers alerts when the pedestrian-to-vehicle distance falls below a specified threshold derived from the shape and orientation data of the positioning area. Additionally, this work proposes a priori data for coordinate calibration, integrating extended Kalman filtering (EKF) and a dynamic threshold algorithm to seamlessly recalibrate the vehicle’s positioning in both stationary and mobile scenarios. Experimental results demonstrate that, compared with using original area data for positioning, the modified algorithm can reduce the positioning coordinate STD by 18.43%, and the vehicle body shake variance by 50.56%, while also achieving a 4.30% increase in success warning rate and a notable 33.35% decline in false warning rate.

In the rapidly evolving field of the Internet of Things (IoT), simplifying infrastructure and deployment is highly competitive. This study introduces a novel approach using an individual-source resonant beam (RB) for passive 3-D positioning in IoT scenarios, excelling in efficient system design and signal utilization. The proposed solution simultaneously exploits the frequency structure and spatial distribution of RB to estimate position parameters, facilitating a compact design for both the base station (BS) and the mobile target (MT). The BS incorporates an individual-source RB transmitter, while the MT is equipped with a passive (electricity-free) reflector. Specifically, for extracting the distance information from RB, we develop theoretical models to illustrate the frequency attributes and associated structural variations of RB due to MT’s movement. Regarding angle estimation, we deduce the relationship between the RB field distribution and incident angles. We also conduct simulations to validate the entire positioning process. The results indicate the positioning accuracy can reach approximately 2 cm over a distance from 1.7 to 2.5 m. This technique sets a new benchmark for precise and efficient 3-D positioning, making it suitable for various IoT scenarios, such as smart homes, industrial automation, and asset tracking.

The rapid advancement of the next generation of communications and internet of things (IoT) technologies has made the provision of location-based services for diverse devices an increasingly pressing necessity. Localizing devices with/without intelligent computing abilities, including both active and passive devices is essential, especially in indoor scenarios. For traditional RF positioning systems, aligning transmission signals and dealing with signal interference in complex environments are inevitable challenges. Therefore, this paper proposed a new passive positioning system, the RF-band resonant beam positioning system (RF-RBPS), which achieves energy concentration and beam alignment by amplifying echoes between the base station (BS) and the passive target (PT), without the need for complex channel estimation and time-consuming beamforming and provides high-precision direction of arrival (DoA) estimation for battery-free targets using the resonant mechanism. The direction information of the PT is estimated using the multiple signal classification (MUSIC) algorithm at the end of BS. The feasibility of the proposed system is validated through theoretical analysis and simulations. Results indicate that the proposed RF-RBPS surpasses RF-band active positioning system (RF-APS) in precision, achieving millimeter-level precision at 2m within an elevation angle of 35°, and an error of less than 3cm at 2.5m within an elevation angle of 35°.

Simultaneous lightwave information and power transfer (SLIPT) is a potential way to meet the demands of sustainable power supply and high-rate data transfer in next-generation networks. Although resonant beam-based SLIPT (RB-SLIPT) can realize high-power energy transfer, high-rate data transfer, human safety, and self-alignment simultaneously, mobile transmission channel (MTC) analysis under non-line-of-sight (NLOS) propagation has not been investigated. In this paper, we propose analytical models and simulation tools for reflector-assisted NLOS transmission of RB-SLIPT, where transmission loss and accurate beam field profile of NLOS MTC can be obtained with a receiver at arbitrary positions and attitude angles. We establish analytical models relying on full diffraction theory for beam propagation between tilted or off-axis planes. Then, we provide three numerical methods (i.e., NUFFT-based, cubic interpolation-based, and linear interpolation-based methods) in simulations. Moreover, to deal with the contradiction between limited computing memory and high sampling requirements for long-range transmission analysis, we propose a multi-hop sliding window approach, which can reduce the sampling number by a factor of thousands. Finally, numerical results demonstrate that RB-SLIPT can achieve 3W charging power and 10bit/s/Hz data rate over a 2m distance in NLOS scenarios.

Coupled with identification, 3D positioning can significantly enrich location-based services. Resonant beam (RB) is emerging as a promising solution to indoor positioning due to its self-aligning and energy-focused transmission. We propose a system for simultaneous localization and identification using RB as the individual medium. The base station (BS) employs a single-source RB, and each mobile target (MT) is equipped with a signal reflection module. For 3D localization, the BS estimates direction by analyzing RB’s spatial distribution and determines the distance from its frequency components. For identification, the passive MT captures the RB for power and reflects its identity (ID) to BS as spot flicker signals. To demonstrate the working principles, we have developed models for location estimation and ID recognition, as well as the power flow within the RB channel. In implementation, we incorporate a threshold regulation scheme for accurate image signal retrieval, along with an input power distribution model tailored for multi-access scenarios. Through simulating the entire process, we verify the system’s feasibility, including confirming the viability of ID recognition. We also evaluate localization performance, averaging ~ 1 cm at heights of 1.5 m ~ 2.5 m, showing promise for a wide range of potential applications.

Both high-accuracy localization and power supply for mobile and Internet of Things (IoT) devices are demanding in the next-generation wireless networks. Recently, research on complete localization has been proposed, i.e., capturing the target’s location and identification (ID). However, the existing schemes face challenges, including: 1) improving positioning accuracy without the active emission of signals from the targets and 2) providing sufficient power for the targets to enable features such as ID. In this article, we present a simultaneous localization and power transfer (SLAPT) system relying on the resonant beam system (RBS), which can realize complete, passive, and high-accuracy positioning along with the capability of sufficient power supply. We first establish analytical models to illustrate the principles of power transfer to the target with an open-cavity laser, positioning the target, including Time-of-Flight (ToF) ranging and Angle-of-Arrival (AoA) estimation with the self-reproducing mode theory, wireless charging power conversion, and identifying the target using shutter modulation. Numerical results demonstrate that the proposed SLAPT system can achieve less than 1-cm positioning accuracy over 2-m distance in 16° Fields of View (FoVs) with 3-W wireless power supply. The proposed system allows passive and complete localization for the target without battery or power line in various IoT applications.

Locating mobile devices precisely in indoor scenarios is a challenging task because of the signal diffraction and reflection in complicated environments. One vital cause deteriorating the localization performance is the inevitable power dissipation along the propagation path of localization signals. In this paper, we propose a high-accuracy localization scheme based on the resonant beam system (RBS) and the binocular vision, i.e., binocular based resonant beam localization (BRBL). The BRBL system utilizes the energy-concentrated and self-aligned transmission of RBS to realize high-efficiency signal propagation and self-positioning for the mobile target (MT). The binocular method is combined with RBS to obtain the three-dimensional (3-D) coordinates of the MT for the first time. To exhibit the localization mechanism, we first elaborate on the binocular localization model, including the resonant beam transmission analysis and the geometric derivation of the binocular method with RBS. Then, we establish the power model of RBS, and the signal and noise models of beam spot imaging, respectively, to analyse the performance of the BRBL system. Finally, the results of numerical analysis and comparison show that BRBL balances system complexity and localization precision effectively, i.e., providing cm-level localization accuracy with its simplicity and ease of implementation.

Long-range, high-power wireless power transfer (WPT) and high-capacity communication can be achieved simultaneously in the resonant beam system (RBS), characterized with spatially separated transmitter and receiver. However, human safety in space transmission cannot be guaranteed if the transmitted power is above a certain level (e.g., several Watts). Thus, in this article, we propose a self-protection RBS, in which the protective beams embrace the energy-transfer resonant beam and are formed by the refracting part of the resonant beam emitted from the output reflector. If an external object comes across the protective beam whose power is very low, the protective beam transmission is cut off. Meanwhile, the resonant beam is interrupted due to the limit of excitation threshold. Then, we reveal the self-protection mechanism based on electromagnetic field propagation, self-mixing interference effect, and output power model. Afterward, we demonstrate that safe energy transfer can be realized by pumping gain medium with a pumping power that is greater than the threshold of the self-protection RBS and less than that of the unprotected RBS. Finally, the numerical results show that about 4.6-W electric power and 12.8 bps/Hz spectral efficiency can be transmitted at 2-m transmission distance safely in the self-protection RBS. Hence, the self-protection RBS provides a new way for safe simultaneous wireless information and power transfer (SWIPT) without mechanical control.

The demands for integrated communication and positioning (ICP) have been put forward in Internet of Things. However, the existing ICP systems either face challenges in trade-off between data rates and positioning accuracy or have difficulties in guaranteeing human safety while maintaining excellent performance. In this paper, we propose a monocular resonant beam-based ICP (RB-ICP) design for simultaneously realizing high-rate data transfer and high-accuracy localization while keeping the features as intrinsic safety. Utilizing the high-efficiency transmission channel of resonant beam system and the frequency-doubled beam design, we introduce the communication model without the echo interference issue. Then, we propose a distance estimation model using the phase-shift method with frequency-quadrupled beam design. Next, the angle of arrival estimation can be conducted relying on the energy-concentrated and self-alignment features of resonant beam. The simulation methods including centroid algorithm and signal conversion simulation in a photosensor along with the noise analysis are presented. In numerical analysis, we demonstrate that the positioning error can be less than 1cm and the achievable spectral efficiency can reach 16bit/s/Hz over 2m distance in 15° field of view (FoV). This proposed system enables simultaneous high-rate data transfer and high-accuracy receiver positioning for the applications such as augmented reality/virtual reality (AR/VR).

SPOT6/7 images of bared soil and mid-season soybean were used to propose a new methodology for site-specific management zone (SSMZ) delineation in the Black soil farmland of Northeast China and soil organic matter and normalized difference vegetation index (NDVI) were used to evaluate the results. The results show: 1) SSMZ based on the June 9thimage and the NDVI on the August 8thhad the consistent trend; 2) SSMZ delineation based on the remote sensing image and object-oriented segmentation method is time intensive, low cost and high precision. The research results could provide the basis for the field variable fertilization and accelerates precision agriculture application.

Complex event processing (CEP) is a technique that handles massive data in the Internet of Things (IoT). A lot of researches have been done in RFID. However, most of the existing CEP engines are centralized which result in the resources of sensors are underutilized and wireless network overload. In this paper, wireless sensor network (WSN) will be organized into a logical tree structure. Tree-structured WSN is combined with automatic machine based CEP to form a novel hierarchical CEP. By query planning, the expression will be decomposed to sub-expressions and sub-expressions are assigned to each sensor and gateway. By processing sub-expressions, sensors and gateways would only need to transmit useful data meanwhile their computing and storage capacity are fully utilized. Experiments show that, in this way, the network bandwidth will be saved significantly and the pressure on the server will be reduced.

MOTIVATION: ChIP-seq data are enriched in binding sites for the protein immunoprecipitated. Some sequences may also contain binding sites for a coregulator. Biologists are interested in knowing which coregulatory factor motifs may be present in the sequences bound by the protein ChIP'ed. RESULTS: We present a finite mixture framework with an expectation-maximization algorithm that considers two motifs jointly and simultaneously determines which sequences contain both motifs, either one or neither of them. Tested on 10 simulated ChIP-seq datasets, our method performed better than repeated application of MEME in predicting sequences containing both motifs. When applied to a mouse liver Foxa2 ChIP-seq dataset involving ~ 12 000 400-bp sequences, coMOTIF identified co-occurrence of Foxa2 with Hnf4a, Cebpa, E-box, Ap1/Maf or Sp1 motifs in ~6-33% of these sequences. These motifs are either known as liver-specific transcription factors or have an important role in liver function. AVAILABILITY: Freely available at http://www.niehs.nih.gov/research/resources/software/comotif/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.