The rapid increase in wireless devices has raised significant security and privacy concerns, positioning Specific Emitter Identification (SEI) as a crucial physical-layer security technology. Although Deep Learning (DL) methods have been widely applied to SEI due to their powerful end-to-end nonlinear mapping capabilities, they generally require large amounts of high-quality signal examples, which are difficult to obtain in adversarial environments. Moreover, wireless channel perturbations induce a distribution shift between the training and testing signal examples from the same emitter. This shift prevents DL-enabled SEI models from learning emitter-specific, channel-agnostic features, leading to a severe degradation in identification performance. In this work, we propose a cross-channel SEI method based on Meta-Feature Augmentation-Enhanced Few-Shot Learning (MFA-FSL) to efficiently address the aforementioned challenges. To overcome the data scarcity, we use signal examples from base emitters with physical-layer characteristics similar to target emitters for pre-training. To overcome the channel perturbations, we employ meta-learning as the pre-training technique to learn a channel-agnostic feature embedding function. Considering that the function does not perform well in scenarios where signal examples of target emitters are extremely scarce, we approximate the target emitter’s feature distribution and sample augmented features from it. These augmented features, together with the raw features extracted from a few signal examples of target emitters, provide sufficient supervision to train a simple classifier. Experiments conducted on a Wi-Fi dataset comprising 16 emitter categories—10 as base emitters and 6 as target emitters—demonstrate that our proposed method achieves more than 85.63% identification accuracy with only 5 signal examples per target emitter, maintaining more than 83.35% accuracy even under varying wireless channel conditions. The code can be downloaded from https://github.com/lovelymimola/MFA-FSLIoTJ-Version.

With the widespread deployment of wireless sensor networks (WSNs), energy replenishment and data collection of wireless sensor nodes (SNs) have emerged as vital research challenges. In existing studies, on one hand, autonomous aerial vehicles (AAVs) are employed to facilitate wireless charging for SNs within clusters, yet the fairness of energy replenishment among these SNs has been overlooked. On the other hand, as a novel and flexible data collection method, AAVs equipped with active reconfigurable intelligent surfaces (RISs) can reduce hardware requirements and signal processing complexity on the AAV side; however, the additional energy consumption introduced by active RIS operation is generally not considered. Therefore, in this work, we focus on utilizing AAV-mounted active RIS in conjunction with a non-orthogonal multiple access (NOMA) scheme to enable green (renewable) energy far-field wireless charging and data relaying for WSNs. We define the AAV’s service as powering SNs to reach their target energy thresholds and collecting the SNs’ data via the active RIS. We develop models for active RIS-aided data relaying and for determining the optimal reflection coefficients. We then formulate an optimization problem to maximize the number of SNs that can be served by the AAV. Given the NP-hard nature of the problem, we further propose a two-step solution with corresponding heuristic algorithms to efficiently solve it. Finally, extensive simulation results demonstrate the superior performance of the proposed solution.

Sixth-generation (6G) communication emphasizes the deep integration of sensing, communication, and computing to support intelligent and rapid-response networks. Autonomous aerial vehicles (AAVs), known for their superior flexibility, terrain adaptability, and low deployment costs, are promising candidates for data collection in large-scale wireless sensor networks (WSNs). However, many existing AAVs-assisted data collection studies assume that AAVs operate at relatively low speeds and incorporate hovering time during data collection. In such scenarios, the AAVs inevitably require longer flying durations and consume much energy. Additionally, they often neglect the impact of the Doppler effect during the data collection. In most general and realistic scenarios, AAVs typically fly at high speeds without the need to hover, and the Doppler effect highly impacts the communication between sensor nodes (SNs) and AAVs. To address this, we propose a data collection framework that leverages orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access (NOMA) to mitigate the Doppler-induced interference in the up-link. We formulate an AAV-assisted data collection efficiency maximization problem by jointly considering AAV energy consumption, and the SNs’ uploading rates and bit error rates (BERs). Given the NP-hard nature of this problem, we design a three-step solution: the first two steps employ heuristic algorithms and the third step integrates a bi-directional long short-term memory (BiLSTM) for intelligent AAV symbol detection. Simulation results validate the superiority of our proposed solution.

The booming development of Internet of Things (IoT) is enriching people’s daily lives in intelligent and convenient ways. Due to the limited battery capacities of IoT Devices (IoTDs), maintaining IoTDs long-term operation remains a critical challenge. The advent of wireless charging technology provides a promising solution to this energy supply issue by utilizing Mobile Charging Vehicles (MCVs) to recharge IoTDs. However, the application scenarios considered by most existing research are overly idealized, i.e., neglecting impacts such as energy constraints of MCVs and the presence of obstacles in practical wireless charging network scenarios. This renders the solutions proposed by those research efforts are difficult to be implemented in reality. Therefore, in this paper, we propose to leverage multiple MCVs to provide directional wireless charging to IoTDs while considering the obstacles in the charging environment. In order to avoid obstacles in the MCVs’ traveling paths while enabling IoTDs to receive more energy, we formulate the problem on maximizing the charging efficiency of MCVs. Since this problem has been proven to be NP-hard, we further propose the Efficiency-driven Obstacle-avoidance Charging (EOC) algorithm to effectively solve it. Finally, we validate the performance of the proposed algorithm through extensive simulations.

As Internet of Things (IoT) networks continue to expand rapidly, remote IoT devices (IoTDs) face significant challenges related to energy supply and data collection. On the one hand, limited battery capacities hinder the long-term, intervention-free operation of IoTDs. On the other hand, the Age of Information (AoI) is a crucial metric for evaluating data freshness, and delays in data collection reduce its value. A promising solution to these challenges is the use of autonomous aerial vehicles (AAVs) to facilitate green energy far-field wireless charging and data collection. Although extensive research has been conducted on scenarios where AAVs operate at fixed altitudes, in many real-world applications, most AAVs operate in 3-D space. In this work, we focus on a 3-D scenario where AAVs first wirelessly charge IoTDs and then collect data. We investigate the 3-D trajectories of multiple AAVs, considering varying altitudes and velocities, and introduce models for AAV-based wireless charging and data collection. We then formulate a multi-AAV wireless charging efficiency maximization problem, taking into account the IoTDs’ average AoI. Given the NP-hard nature of this problem, we propose the joint charging and data collection (JCDC) algorithm, which aims to ensure data timeliness while replenishing as many IoTDs as possible. Finally, extensive simulations are conducted to validate the performance of the proposed JCDC algorithm.

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime.

Sixth generation (6G) communications empower billions of Internet of Things devices (IoTDs), bringing significant convenience to modern life. However, the challenge of powering a burgeoning number of IoTDs has become a critical concern. Radio frequency (RF) wireless charging technology is a promising solution to this issue; however, its adoption has been limited due to its relatively low charging efficiency. Beamforming facilitated by antenna arrays can enhance the efficiency of RF wireless charging. In addition, renewable (green) energy can act as the energy source for RF wireless chargers. In this work, we integrate beamforming technique with green energy empowered RF wireless charging to develop a three-dimensional charging model for a green charger (GC). Furthermore, when investigating the multi-GC to multi-IoTD wireless charging scenario, we derive an accurate model to quantify the accumulation of wireless energy in the charging area. Based on this, we formulate the multi-GC to multi-IoTD charging efficiency maximization problem to enhance the energy received by IoTDs within a given charging period. Since this optimization problem is proved to be NP-hard, we propose the Efficient multi-beAm coopeRative chargiNg (EARN) algorithm to solve it efficiently. EARN coordinates multiple charging beams by intelligently assigning proper compensating phases to each GC, effectively utilizing the energy transmitted by GCs in all directions to strengthen the wireless charging effect. Ultimately, extensive simulations validate the performance of the EARN algorithm in conspicuously improving the wireless charging efficiency.

With the rapid development of Internet of Things (IoT), wireless rechargeable sensor networks (WRSNs) have found widespread applications in modern society. However, due to the dynamic energy consumption of sensor nodes (SNs) and the challenges associated with battery replacement, ensuring a timely energy supply to SNs is a pressing concern. Currently, employing unmanned aerial vehicles (UAVs) for far-field wireless charging emerges as a promising solution to charge SNs. However, with the expansion of WRSNs, existing solutions face challenges in meeting the energy demands of large-scale WRSNs. Therefore, we propose to leverage multiple green energy powered UAVs to facilitate far-field wireless charging for large-scale WRSNs. We first segment the SNs into clusters and propose the dynamic energy consumption models for UAVs and SNs. Then, we formulate the UAVs' charging service efficiency maximization problem, enabling more SNs to receive sufficient energy replenishment. As this problem is proved to be an NP-hard problem, we further propose the dyNamic wIreless chaRging (NIR) algorithm to efficiently determine the charging sequences of the SNs and reduce the UAVs' energy consumption. Extensive simulations have validated that NIR notably improves the charging service efficiency of the UAVs, thereby prolonging the WRSN's lifespan.

With the significant progress in recent decades, wireless rechargeable sensor networks (WRSNs) have been widely deployed in smart cities, lakes, forests and other intelligent scenarios to perceive physical environment. To remotely power these sensor nodes (SNs), unmanned aerial vehicle (UAV) can provide far-field wireless charging by electromagnetic waves. Designing a trajectory for the UAV to “visit” all sensors with the shortest time is an important issue to be resolved in UAV-facilitated WRSNs. Existing researches have proposed massive novel algorithms to solve this issue but cannot realize a desirable solution for charging large-scale WRSNs within a tolerable time. In this work, we leverage UAV to wirelessly power the sensors and relay their sensed data timely. We first propose the UAV moving direction model, UAV-to-SN charging model, data backhauling model and UAV flying speed model to calculate the time consumed by the UAV for charging SNs and relaying data. In order to reduce the work time (equivalent to energy consumption) of the UAV, a flying and hovering time minimization problem is formulated. As this problem is proved as an NP-hard problem, we further propose the Fly Forward (FF) algorithm to efficiently solve it. Through extensive simulations, we have validated that the FF algorithm can effectively reduce the UAV's flying and hovering time, thus realizing an efficient and economical flying trajectory.

Sixth-generation (6G) wireless communications greatly emphasizes the integration of sensing, communicating, and computing. Unmanned aerial vehicles (UAVs), by leveraging their feasibility and mobility, can naturally facilitate flexible far-field wireless charging and data backhauling for widely implemented wireless rechargeable sensor networks (WRSNs) across diverse domains, such as intelligent agriculture, smart cities, and modern factories. However, the energy constraints inherent to UAVs, coupled with the absence of joint optimization in clustering and trajectory design, present formidable challenges in efficiently leveraging UAVs for large-scale WRSN wireless charging and data backhauling. Therefore, in this work, we empower the green energy-powered base station (GBS) to power a UAV by laser charger to prolong the UAV’s uptime. This enables the UAV to effectively perform wireless charging and data backhauling for a WRSN. By considering the GBS’s green energy budget, we formulate an optimization problem focused on determining the optimal 3-D hovering points for UAV to maximize the number of sensor nodes (SNs) capable of receiving sufficient energy and uploading data. Given the NP-hard nature of this problem, we propose a two-step solution featuring corresponding heuristic algorithms designed to efficiently address it. Extensive simulations have been conducted to validate the efficacy of our proposed algorithms.

The high precision measurement of binocular camera depends on the calibration accuracy. Mainstream methods focus on the calibration of intrinsic parameters and the pose relationship of two cameras in sequence. However, the transmission of error will decrease the calibration quality. It is still challenging especially in the case of an unknown imaging model due to the intervention of baffle and multimedia refraction. In this article, a general calibration method for a binocular vision system with an unknown imaging model is proposed. Based on the one-to-one mapping between 4-D image coordinates and 3-D Cartesian coordinates of spatial points, the intersection line of two calibration planes is extracted. All calibration planes are organized in the form of a three-plane group, and in each group, the poses are solved by the intersection lines. These poses are further optimized to acquire accurate 3-D coordinates of points in calibration planes. Combined with the correspondences of image pixels and points in a calibration plane, the line-plane intersection point between the projection line and this plane is calculated. Finally, projection lines are fitted based on corresponding line-plane intersection points. By modeling the relationship between image pixels and object-end projection lines, the uncertainty of the imaging model is solved. The proposed method is universal and stable for different imaging models including refraction, and the results of simulation and experiment show its effectiveness.

High-precision dynamic 3-D reconstruction technology is highly valuable in industrial automation applications, including defect detection, visual measurement, and grasp planning. Presently, predominant 3-D reconstruction for industrial applications requires projecting a pattern sequence, introducing complexities to positioning and measurement in dynamic conditions. We proposed a single-shot dense 3-D reconstruction framework for industrial tasks in response. This work is valuable in the following two aspects. 1) We proposed a design scheme of structured light patterns and developed a multifrequency phase decoding network to extract the wrapped phase from the multifrequency fringe pattern. These unique phase combinations can be used as a robust feature for disparity optimization and stereo matching. 2) We created a dataset with 3000 scenes and their high-precision wrapped phase ground truth. Experiments demonstrate that our proposed method significantly outperforms existing single-shot structured light reconstruction methods with high precision and adaptability. Thus, this framework has significant potential application in industrial automation.

The worldwide rapid development of the Internet of Things (IoT) has led to an exaggerated scale and explosive increase of IoT devices (IoTDs). However, owing to embedded capacity limited batteries, most IoTDs have relatively short lifespans, which is a crucial factor of enervating their IoT applications. Far-field wireless charging is a means to extend IoTDs' lifespans. However, many far-field wireless charging scheduling strategies overlook the work load imbalances among different charging base stations, thus leading to low charging throughput (CTP) and prolonged waiting times for IoTDs. This adversely affects the IoTDs wireless charging network performance. Based on the M/G/1 queuing model, in this work, we first analyze the wireless charging workload of a charging base station and then aim to maximize CTP in the network by jointly considering the charging base stations' wireless charging workloads and the IoTDs' non-preemptive charging priorities. As the CTP maximization problem is NP-hard, we further propose the woRkload bAlancing and non-Preemptive TempOral pRiority (RAPTOR) algorithm to efficiently solve this problem. Finally, extensive simulations have validated the performance of RAPTOR in improving the CTP of the whole network.

With the rapid development of the fifth generation (5G) communication and Internet of Things (IoT) technology, the applications of Wireless Rechargeable Sensor Networks (WRSNs) have been widely adopted in modern society. Wireless Power Transfer (WPT) technology enables WRSNs to play important roles in target detection, environment monitoring and other IoT application scenarios. In certain areas, especially when Sensor Nodes (SNs) are deployed on a large scale, Mobile Charging Vehicles (MCVs) with large-capacity batteries are usually adopted to replenish wireless energy to the SNs. During the charging process, the closer an MCV is to an SN, the higher the wireless charging efficiency achieves. However, if an MCV travels a lengthy distance before reaching the SNs, its energy will direly waste during the movement. Thus, the total charging efficiency in the network is diminished. In order to improve the MCV's charging efficiency in WRSN, this work proposes to enable the MCV to wirelessly power the SNs cluster by cluster. Then, the MCV's charging efficiency maximization problem is formulated as an optimization problem. Since this formulated problem is NP-hard, we further propose the Efficiency-driven Path Selection (EPS) algorithm to effectively reduce the MCV's charging time and traveling distance, thus conserving more energy to power the SNs. Extensive simulations validate that our proposed algorithm preeminently improves the MCV's charging efficiency.

Powering a huge number of Internet of Things Devices (IoTDs), necessitated in many Internet of Things (IoT) applications, is a dreadful problem in many circumstances, in terms of the cost of labor, time, and so on. Far-field green energy wireless charging is a promising technique to remotely power IoTDs in a large-scale network. In order to improve the end-to-end energy efficiency, for the scenario with multiple green base stations (GBSs) wirelessly charging multiple IoTDs, our previous research work proposed a wireless charging scheme to aggregate the wireless power received by a static IoTD from multiple GBSs. In reality, in various emerging IoT applications, a large proportion of IoTDs are mobile. Hence, in this work, we focus on intelligently assigning multiple GBSs to wirelessly charge mobile IoTDs such that the IoTDs can be efficiently powered. We first propose the moving model and the charging model of IoTDs. Based on the nonlinear wireless charging model and nonlinear wireless energy conversion model, we then propose the Greedy dynamic joint charging (GAIN) algorithm to efficiently power the mobile IoTDs. Through extensive simulations, we validate the performance of the proposed algorithm.

Dynamic object perception is an important yet challenging direction in the field of robot navigation. Without any prior knowledge about motion and objects, a novel dynamic rigid bodies mining and motion estimation method based on monocular camera is proposed in this article. Different from the existing works based on sampling that associate feature points to motion hypotheses according to the reprojection errors, our work endeavors to find the intrinsic relevance among motion hypotheses. To represent this relevance, the concept of the probabilistic field on the Lie group Sim(3) manifold is introduced, which is established using random sampling. It provides a computable way for the regions on the manifold where rigid bodies possibly appear. The probability of a motion hypothesis falling on a region is expressed by its confidence. The regions with large confidences in the probabilistic field are selected as potential rigid bodies, whose corresponding feature points are further sampled for pose calculation. As a result, the randomness of sampling is reduced and the inliers for possible rigid bodies are enhanced, which guarantees the accuracy of motion estimation. On this basis, the tracking of rigid bodies is achieved. The proposed method distinguishes the feature points of dynamic objects with 3-D motion from those in the static background, thus enabling simultaneous localization and mapping (SLAM) to be initialized in dynamic environments. The experimental results on the KITTI, Hopkins 155, and MTPV62 datasets demonstrate the effectiveness. Comparison experiments indicate that our method outperforms the other methods in sensitivity of dynamic objects perception.

With the development of Internet of Things (IoT), wireless rechargeable sensor networks (WRSNs) have been widely applied in modern society. There has a greater demand for green and efficient remote wireless charging to power the wireless sensor nodes (SNs) in WRSNs. However, few has tackled the wireless charging efficiency problem that can achieve the low dead SNs percentage (DSP) in far-field wireless charging. Existing far-field wireless charging schemes cannot meet the energy supply requirement of large-scale SNs. Therefore, this work proposes the multiple mobile chargers (MCs)-assisted efficient green energy wireless charging for WRSNs. Firstly, according to the existing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the area not covered by the wireless charging base stations (BSs) is divided into multiple irregular-shape sub-regions. Then, we leverage multiple MCs to wirelessly charge the SNs located in these sub-regions. Finally, we propose the Minimal chArging grouPings (MAP) algorithm to minimize the number of the MCs’ anchor points (APs) and efficiently charge the SNs. The simulation results validate that our proposed algorithm can effectively improve the wireless charging energy efficiency and reduce the DSP.

Nowadays, Far-field Wireless Power Transfer (FWPT) has attracted many research efforts to conveniently power the Internet of Things (IoT) devices. Electromagnetic Radiation (EMR) safety in FWPT has brought much attention from the public. Existing works on FWPT mainly focus on improving the remote charging efficiency but overlooking the effects of EMR. A few works consider EMR safety but do not present accurate EMR quantization analysis because there lacks an accurate EMR computing model in IoT wireless charging scenario. In this paper, in order to evaluate and avoid the EMR's harmful impact, we first propose an accurate theoretical calculation equation for EMR and the concept of Charging Restricted Area (CRA). In the wireless charging area on a 2-dimensional plane, according to the EMR computing model, we further maximize the overall charging power by adjusting the power of chargers and ensure that the EMR in this area is lower than the EMR safety threshold. The wireless charging EMR safety problem is formulated as a linear programming problem with infinite constraints. To re-express the wireless charging EMR safety problem as a typical linear programming problem with finite constraints, the Sampling Safety Charging (SSC) algorithm is proposed. We have conducted extensive experiments to validate our proposed algorithm; the simulation results show that the performance achieved by our algorithm outperforms that achieved by the distributed RObustlySafE (ROSE) algorithm.

A wireless sensor network's (WSN's) uptime is highly depended on the lifespans of its wireless sensor nodes (SNs). However, the battery of an SN sometimes may not be easy to be replaced owing to circumstantial constraints. Nowadays, the far-field wireless power transmission (WPT) has attracted more attention from industry and academy. Many researchers have proposed to utilize the WPT technology to remotely charge the SNs. As unmanned aerial vehicle (UAV) can fly close to the SNs located in some hard-to-reach areas, in this work, a UAV is adopted to assist the green base station (GBS) to efficiently power the SNs which are far away from the GBS. Considering the limited energy carried by the UAV and the UAV's energy consumption, we propose a novel UAV comprehensive charging strategy to efficiently power the WSN and to enable all the SNs to be charged with considerable energy replenishment. The simulation results validate our proposed strategy can effectively extend the WSN's uptime.

Owing to the development of the Fifth Generation (5G) Communication and Internet of Things (IoT), Wireless Sensor Network (WSN) has received unprecedented attention from modern society. Wireless Power Transfer (WPT) is a promising technique to alleviate the energy sustainment issue of WSN. WPT enables WSN playing significant roles in smart farmland, intelligent factory and smart city. In these application scenarios, in order to save the on-grid power and protect natural environment, green charging base station (GBS) is proposed to first harvest green energy and then wirelessly power the wireless sensors with WPT. However, when a GBS omnidirectionally power its surrounding wireless sensors, the sensors closer to the GBS may be overcharged, while the sensors far from the GBS may not receive enough energy supply. Therefore, we propose a scheme to leverage the sensors with abundant energy replenishment to relay energy for the sensors with less residual energy. Then, we further propose an heuristic algorithm to efficiently solve the relay assignment problem. By this algorithm, the sensors far from the GBS can also receive considerable energy transmitted by the relays. Extensive simulations have validated that, compared to the conventional wireless charging scheme without energy relay, our proposed scheme can dramatically improve wireless charging performance in reducing the Dead Sensors Percentage (DSP) in the local WSN by around 50%.

With the worldwide ubiquitous implementation of Internet of Things (IoT), IoT devices (IoTDs) and their emerging applications are commendably enriching people’s daily life with intelligence and convenience. However, a tremendous number of IoTDs all over the world, incurring a huge amount of energy consumption, are exacerbating the global electric grid load and natural environment changes while the electronic equipment traditional charging approaches are unable to efficiently power the IoTDs. Leveraging green energy to remotely charge the IoTDs (i.e., green energy far-filed wireless charging) is the essential solution to revolutionarily resolve these problems. In this work, we propose a two-step green energy wireless charging (TREE) algorithm to efficiently power the IoTDs for the multiple-green base stations (GBSs)-to-multiple IoTDs charging scenarios. First, we propose the recharging threshold model for IoTDs and schedule the IoTDs to be efficiently charged by their associated GBSs within the green wireless charging time period. Second, for those IoTDs that will not be fully charged, we propose the multi-GBS joint accumulative charging scheme to fulfill most of the IoTDs’ charging requirements within the charging period. Finally, we validate the performance of the proposed algorithm through extensive simulations.

Category-level 6D object pose estimation has gained popularity and it is still challenging due to the diversity of different instances within the same category. In this paper, a novel category-level 6D object pose estimation framework with structure encoder and reasoning attention is proposed. A structure autoencoder is introduced to mine the shared structure features in the color images within the same category, via a distinct learning strategy that recovers the image of another instance but with the most similar pose to the input. On this basis, a reasoning attention decoder and full connected layers are stacked to form a rotation prediction network, where the structure features and 3D shape features are integrated and projected to a semantic space. The semantic space includes observed patterns and learnable patterns, which are better learned by adding a shortcut connection branch parallel to reasoning attention decoder with gradient decouple. Further reasoning based on these patterns endows the decoder with powerful feature representation. Without 3D object models, the proposed method models the attributes of category implicitly in the semantic space and better performance of 6D object pose estimation is guaranteed by reasoning on this space. The effectiveness of the proposed method is verified by the results on public datasets and actual experiments.

Using green energy to charge Internet of Things (IoT) devices is becoming a technically viable option. Green far-field Wireless Power Transmission can help powering IoT terminal devices. Keeping the maximum number of IoT devices (IoTDs) in their normal working modes is to furthest prolong the lifetime of the IoT network. In this work, for a given charging area, we first determine association between IoTDs and the green energy base station (GEBS), and then efficiently power IoTDs wirelessly. In green far-field wireless charging, we propose a dual-threshold model for IoTDs to facilitate wireless charging. In the dual-threshold model, we define two thresholds, i.e., Alarm Threshold and Working Threshold. Based on the dual-threshold model, we then propose the Dual-Threshold Orderly Charging (DTOC) algorithm to efficiently charge their surrounding IoTDs in a specific order to improve the charging efficiency. Finally, we validate the performance of the proposed algorithm through extensive simulations.

Owing to the development of Internet of Things (IoT) and Artificial Intelligence (AI) technology, powering IoT devices has become a dire problem that mobile IoT devices need a more portable way to be charged. Based on our previous research on green IoT, the far-field Wireless Power Transfer (WPT) powered by green energy can alleviate this problem. Although many existing works on Multi-Base Station Joint Charging Schemes have gained remarkable results, the aggregation of multiple power waves cannot be explicitly described by the traditional 1-dimensional model suggested by Friis Formula. The 2-dimensional model called vector model can solve this problem by clearly indicating how the multiple power waves aggregate at an IoT device in the form of a 2-dimensional vector. In this work, an Adjusting Phase (AP) method based on the vector model is designed to enhance the value of aggregated power waves. In addition, we propose the Greedy chArging Grouping Algorithm (GAGA) to ensure that the charging mission will be completed on time and the risk of running out of power can be reduced. Finally, we validate the performance of the proposed algorithm in comparison with the state-of-the-art solutions through extensive simulations.

In this correspondence paper, an image dynamics-based visual servoing for quadrotors is proposed to realize stable hovering and tracking. Four perspective image moments are adopted as visual features to control all the independent degrees of freedom of a quadrotor. The complicated interaction matrix is simplified by projecting original image to virtual image plane. On this basis, the dynamics of the system is determined by considering the dynamics of image features and the quadrotor simultaneously. Backstepping controllers are then designed to stabilize the visual servoing system of the quadrotor. In reality, it is unrealistic to have exact prior knowledge about the trajectory parameters of an unpredictable moving target. To solve this problem, a trajectory observer based on nonlinear tracking-differentiator to estimate trajectory parameters of the target is firstly integrated into the quadrotor with image dynamics, which guarantees a satisfactory performance. The effectiveness of the proposed approach is verified by simulations.

As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.

In this paper, a novel method is proposed for curve detection in images using a feature-related searching control model. It is composed of three parts: 1) prediction; 2) searching; and 3) updating. First, curve related features are modeled to a three order array. Then, equations of the prediction, searching, and curve parameter updating are deduced. Third, an optimal model for curve parameter estimation during iterations is given. Based on the proposed model, a curve detection algorithm is designed. Experiments on thousands of images demonstrate the effectiveness and advantages of the proposed method. Comparison experiments with state-of-the-art methods show that the proposed method outperforms the existing methods on most indexes. Our method can describe the contents of original images more completely with fewer curves. The contour evaluation framework and the Berkeley segmentation dataset are used to evaluate the performances of different curve detection methods. The proposed method can also detect curves in the order relates to their importance, which has been validated in experiments.

Internet of Things (IoT) promotes a heightened level of awareness about our world and makes our life more intelligent and convenient. In IoT, machine-to-machine (M2M) communications enables direct connectivities among machines and devices to automatically exchange information and perform actions. Low Power Wide Area (LPWA) plays a crucial role in provisioning wide area coverage and low energy consumption network for M2M communications in IoT. Moreover, green energy harvesting is essential for mobile machine type-devices (MTDs) to achieve their self-sustainability and independence. Therefore, we propose dual-battery architecture to empower MTDs with concurrent green energy harvesting and IoT functionalities. Rather than routing through an LPWA base station (BS), direct and dual-hop transmissions are proposed for proximal M2M communications. According to the residual green energy in the MTDs' batteries, we provision a relay incentive policy and relay selection schemes to facilitate direct and dual-hop M2M communications. For dual-hop M2M communications, some heuristics are proposed to maximize the overall data rate with low computational complexity. Finally, we validate the performances of the proposed architecture and schemes through extensive simulations.

This paper proposes a suite of methods to realize high precision pose measurement in 3-D Cartesian space based on a multicamera microscopic vision system. Since it is inefficient to develop a specific image algorithm for each kind of object and the imaging condition might be unsatisfactory, we propose a method of contour primitives of interest extraction, which allows flexible reconfiguration for novel object image and owns robustness under different imaging conditions. The object is detected in grayscale image based on a template of contour primitives. Edges are extracted according to derivatives along the normal vectors of these contour primitives. The positions and directional derivatives of these edges are used for feature extraction and autofocus, respectively. The point features and line features extracted from multiview images are utilized to measure 3-D vectors and orientations, respectively, based on image Jacobian matrices. Cameras' linear motions are considered in the imaging model, so that the measurement range is expanded beyond the limitation of microscopes' shallow depths of field. The affine epipolar constraint and focused planes intersection constraint between cameras are applied to improve the real time performances of image feature extraction and multicamera autofocus, respectively. A series of experiments are conducted to verify the effectiveness of the proposed methods. The root mean square errors of pose measurement are evaluated as 3 μm in position and 0.05° in orientation, while the measurement range is about 5000 μm in position and 20° in orientation.

Owing to the impact of greenhouse gases on the environment and climate change, the on-grid energy consumption of Information and Communications Technology (ICT) has received much attention in recent years. Cellular networks are among the major energy guzzlers in ICT, and their contributions to the global energy consumption increase rapidly. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Utilizing green energy to power BSs is essential to save the on-grid energy and reduce the electricity expenditure of the network providers. However, in order to furthest save on-gird energy, most existing works only focus on maximizing the green energy utilization, while compromising the services received by the mobile users. In fact, dissatisfaction of services eventually leads to loss of market shares and profits of the network providers. Therefore, we propose a novel profit driven user association scheme for green heterogeneous cellular networks by jointly considering the green energy utilization and traffic delivery latency to maximize the profit of the network providers. Since this profit driven user association problem is NP-hard, we further propose some heuristics to maximize the profit with low computational complexity. Finally, we validate the performance of the proposed algorithm through extensive simulations.

This paper describes a high-frame-rate (HFR) vision system that can detect multiple objects in an image of 512 × 512 pixels at 12,000 frames per seconds (fps). An optimized algorithm is proposed based on conventional Histograms of Oriented Gradient (HOG) descriptor and Support Vector Machine (SVM) classifier algorithms for hardware implementation. By implementing the proposed algorithm on a field-programmable gate array (FPGA) of a high-speed vision platform, multi-object in an image can be detected at 12,000 fps under complex background. In hardware implementation, 64 pixels were processed in parallel with 80 MHz camera clock. Source image and detection results can be transferred to personal computer (PC) in real-time for recording or post-processing. Our developed HFR multi-object detection system was verified by performing several evaluations.

The Internet of Things (IoT) heralds a vision of future Internet where all physical things/devices are connected via a network to promote a heightened level of awareness about our world and dramatically improve our daily lives. Nonetheless, most wireless technologies in unlicensed band cannot provision ubiquitous and quality IoT services. In contrast, cellular networks support large-scale, quality of service guaranteed, and secured communications. However, tremendous proximal communications via local base stations (BSs) will lead to severe traffic congestion and huge energy consumption in conventional cellular networks. Device-to-device (D2D) communications can potentially offload traffic from and reduce energy consumption of BSs. In order to realize the vision of a truly global IoT, we propose a novel architecture, i.e., overlay-based green relay assisted D2D communications with dual batteries in heterogeneous cellular networks. By optimally allocating the network resource, our proposed resource allocation method provisions the IoT services and minimizes the overall energy consumption of the pico relay BSs. By balancing the residual green energy among the pico relay BSs, the green energy utilization has been maximized; this furthest saves the on-grid energy. Finally, we validate the performance of the proposed architecture through extensive simulations.

As the era of Internet of Things (IoT) approaches, we are facing a new level of awareness about our world. It has been predicted that almost 50 billion devices will be connected by 2020 to realize the Internet of Things. A large number of devices will communicate with each other to gather, share and forward information to connect people in a more intelligent, convenient and efficient way. Therefore, Device-to-Device (D2D) communications is expected to be the intrinsic part of IoT. However, most existing researches in D2D communications are based on the D2D and cellular communications coexisted architecture. Provisioning D2D and cellular communications in the same cellular network quickly exhaust the limited resources, thus leading to performance degradation. We envision a novel architecture of green relay assisted D2D communications with dual battery for IoT. We adopt low power small base stations (BSs) as the relay BSs in the network. By optimally allocating the network resource, our proposed architecture enables the source- destination device pairs to reach their required transmission data rates to satisfy their different application services. The relay BSs are powered by both green energy and on-grid energy, and equipped with dual battery. By balancing the residual green energy among the relay BSs, we maximize the utilization of green energy in the network and achieve the goal of furthest saving the on-grid energy. Finally, we validate the performance of the proposed architecture through extensive simulations.

Green communications has received much attention in recent years. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Reducing energy consumption of BSs is essential to realizing green cellular networks. Utilizing green energy to power BSs is a promising way to reduce the on-grid energy consumption. Maximizing the utilization of green energy has thus been proposed to furthest save the on-grid energy. In this paper, we propose a green energy driven user-BS association with dual battery system to maximize the utilization of green energy at BSs. The BSs of cellular networks are powered by both on-grid energy and green energy. The optimal usage of green energy is achieved by balancing the mobile users among BSs according to the amount of residual green energy in their batteries. This green energy driven user association optimization problem is NP-hard. Hence, we propose some heuristics to maximize the green energy utilization and approximate the optimal user association with low computational complexity. Finally, we validate the performance of the proposed algorithm through extensive simulations.

Green communications has received much attention in recent years. In cellular networks, base stations (BSs) account for more than 50 percent of the energy consumption. Reducing energy consumption of BSs is essential to realize green cellular networks. Utilizing green energy to power BSs is a promising way to reduce the on-grid energy consumption. Owing to the dynamics of both mobile traffic loads and green energy, the mismatch between the energy demands and green energy generation in a BS results in inefficient green energy utilization. Managing the battery in BSs can control the green energy usage in individual time slots, thus alleviating the inefficiency caused by the mismatch. In this paper, we propose an intelligent battery management mechanism to optimize the green energy utilization in BSs based on the Markov Decision Process (MDP). A large number of states in the Markov chain are required to model the dynamics of solar radiation and BS workload demands. Thus, the original MDP optimal policy iteration method incurs a high computational complexity. Therefore, we propose some heuristics to approximate the optimal energy dispatching strategy with low computational complexity, and validate the performance of the proposed algorithm through extensive simulations.

This correspondence paper proposes a fast orientation estimation approach of natural images without the help of semantic information. Different from traditional low-level features, our low-level features are extracted inspired by the biological simple cells of the visual cortex. Two approximated receptive fields to mimic the biological cells are presented, and a local rotation operator is introduced to determine the optimal output and local orientation corresponding to an image position, which serve as the low-level feature employed in this paper. To generate the low-level features, a bisection method is applied to the first derivative of the model of receptive fields. Moreover, the feature screener is introduced to eliminate too much useless low-level features, which will speed up the processing time. After all the valuable low-level features are combined, the overall image orientation is estimated. The proposed approach possesses several features suitable for real-time applications. First, it avoids the tedious training procedure of some conventional methods. Second, no specific reference such as the horizon is assumed and no a priori knowledge of image is required. The proposed approach achieves a real-time orientation estimation of natural images using only low-level features with a satisfactory resolution. The effectiveness of our proposed approach is verified on real images with complex scenes and strong noises.

This paper proposes a novel general framework for line segment perception, which is motivated by a biological visual cortex, and requires no parameter tuning. In this framework, we design a model to approximate receptive fields of simple cells. More importantly, the structure of biological orientation columns is imitated by organizing artificial complex and hypercomplex cells with the same orientation into independent arrays. Besides, an interaction mechanism is implemented by a set of self-organization rules. Enlightened by the visual topological theory, the outputs of these artificial cells are integrated to generate line segments that can describe nonlocal structural information of images. Each line segment is evaluated quantitatively by its significance. The computation complexity is also analyzed. The proposed method is tested and compared to state-of-the-art algorithms on real images with complex scenes and strong noises. The experiments demonstrate that our method outperforms the existing methods in the balance between conciseness and completeness.