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Pulsewidth modulation (PWM) strategies, including continuous PWM (CPWM) and discontinuous PWM (DPWM), have been efficiently applied in the three-phase voltage source converters (VSCs). In the three-phase VSCs, dead-time distortion is inevitable. The characteristics of the dead-time distortion of three-phase VSCs with CPWM strategies have been studied extensively, whereas those of DPWM strategies have not been studied in detail. In this article, a comparative analysis of the dead-time distortion of CPWM strategies and DPWM strategies is conducted. Furthermore, the improvement of the PWM strategies to mitigate dead-time distortion is presented. The improved PWM strategies inject the compensation signals to the modulation signals to reconstruct the dead-time voltage error, which can significantly reduce the dead-time distortion of various PWM strategies. Experimental tests are conducted and the experimental results are provided to verify the analysis results of the dead-time distortion of DPWM strategies and the improved PWM strategies. [ABSTRACT FROM AUTHOR]

With the explosive growth of devices and tasks deployed in the industrial Internet of Things (IIoT), the lack of interconnection and collaboration between devices leads to poor timeliness and security in IIoT resource scheduling. This article focuses on the issue of adaptive scheduling of resources in large-scale IIoT. First, a collaborative terminal-edge IIoT architecture is designed, which introduces blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. Then, a smart contract-based multidimensional resource transaction model is developed to improve the efficiency and security of resource scheduling by establishing a credit-based consensus mechanism. Distributed transaction learning resource scheduling algorithm is further proposed to implement resource-adaptive scheduling between devices in IIoT. Extensive simulation experiments are conducted to evaluate the proposed method with respect to several performance aspects covering the scheduling decision delay, transaction generation ratio, and security. The obtained results demonstrate that the comprehensive scheduling performance of the proposed method outperforms other existing algorithms. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) technology has been deployed in various scenarios in recent years. In some practical RFID applications, the items attached with tags can be divided into multiple groups. Thus, the efficient and accurate missing tag detection of each group is critical. Accordingly, this article concentrates on the problem of missing tag detection in the multiple-group RFID systems, after which three distinctive protocols are proposed. First, we propose an aptitudinal multiple-group missing tag detection protocol, which makes full use of the expected singleton slots. Then, an enhanced multiple-group missing tag detection protocol is proposed, which can achieve significant broadcast and response savings. Finally, an accurate and expeditious multiple-group missing tag detection protocol is designed, the detection reliability of which can approximate 100%. The theoretical analysis and extensive simulations are conducted and the results verify that the proposed protocols in this article outperform the other ones. [ABSTRACT FROM AUTHOR]

In this letter, we consider a non-orthogonal multiple access (NOMA) network with a half-duplex (HD) decode-and-forward (DF) relay to improve physical layer security (PLS) of two users. Two legitimate users of U1 and U2 are far from the multi-antenna base station (BS). Thus, a relay with multi-antenna is used to enhance communication and obtain diversity gain. In the first stage, the BS transmits the superimposed signal to the relay via NOMA. Meanwhile, U1 generates the jamming signal to the eavesdropper (Eve). In the second stage, the relay forwards the signal to two users while the BS transmits the jamming signal to disrupt the eavesdropping. It is assumed that the Eve has strong eavesdropping ability. Thus, we need to introduce jamming to guarantee security. Furthermore, we use U1 instead of a full-duplex (FD) relay to send the jamming signal in the first stage to avoid self-interference. To measure the secrecy performance, a closed-form expression of secrecy outage probability (SOP) is derived for the two users. Simulation results are provided to verify the correctness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Recently, intelligent data processing and interpretation based on deep learning (DL) have received considerable attention. Training data are vital for DL-based approaches. In geosciences, researchers have been facing a significant obstacle, i.e., the absence of authoritative and representative open data for training and testing artificial neural networks (ANNs). Although open-source works in geosciences are increasing, the quantity is currently limited. With the aid of the Society of Exploration Geophysicists (SEG) 2020 Machine Learning (ML) Blind-Test Challenge Data, we open source a package for DL-based seismic facies classification (SFC). We regard SFC as translating the seismic data into the facies by training a flexible end-to-end encoding–decoding-style ANN “BridgeNet” revised from U-Net. Inspired by the residual network (ResNet), we further enhance the BridgeNet by inserting the identity shortcut connections, which can theoretically ease the notorious problem of vanishing/exploding gradients. The evaluated framework, however, is not restricted to SFC. We hope that it can provide some insights that help researchers to construct and train ANNs that yield reliable and robust results in their own tasks. We carry out benchmarking experiments to investigate some crucial factors impacting the ANN’s performance to elucidate how we obtained our current optimal results on the SEG 2020 ML Challenge Data. The accuracy and continuity of predicted facies along the training and testing sections indicate that the results are consistent with geologic sedimentation, verifying the generalization capability of the enhanced flexible end-to-end encoding–decoding-style BridgeNet. For SFC, we recommend a memory-saving sparse categorical cross-entropy (CC) loss function to improve the efficiency. The codes are available at https://doi.org/10.5281/zenodo.5787673. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) systems have achieved wide applications in various scenarios, such as warehouse management, logistic tracking, smart transportation, etc. Despite the enormous benefits from the RFID systems, the security issues are still of great concern, such as the cloning attacks. In this article, we focus on the detection of probabilistic cloning attacks for the anonymous RFID systems, in which each cloned genuine tag suffers attacks from its clone tags with a certain probability. We propose an efficient detection protocol against the probabilistic cloning attacks in anonymous RFID systems named DAP, which can detect the probabilistic cloning attacks with the required detection reliability $\alpha$ if at least one tag is attacked with the probability no less than the threshold $P_T$. The proposed DAP protocol fully utilizes the inconsistency and unreconcilable collision caused by the probabilistic cloning attacks to effectively detect the probabilistic cloning attacks.The parameters are theoretically analyzed to maximize the detection efficiency. The extensive simulations are conducted and the results demonstrate the effectiveness of the proposed DAP protocol. [ABSTRACT FROM AUTHOR]

Localization services play an important role in Internet of Connected Vehicles (IoCV) and vehicle predictive localization information can greatly improve traffic efficiency and reduce accidents. However, a huge amount of computing and communication overhead is required to obtain such information by traditional methods. In this work, we propose a Behavior-based Clustering Method (BCM) to analyze the behavioral correlation between vehicles and classify them into different clusters. Based on BCM results coupled with a deep learning model, we further propose a Clustering-learning-based Long-term Predictive Localization (CLPL) algorithm to predict vehicles’ future location distribution. In the proposed CLPL algorithm, all the traffic roads are divided into consecutive small segments in order to pinpoint vehicles’ precise current locations and to obtain long-term predictions. Extensive simulations, notably involving real dataset, have been carried out to evaluate BCM and CLPL in terms of several performance criteria including matching rates. The analysis of the results validated how the designed methods can predict vehicle location much more accurately than existing algorithms. [ABSTRACT FROM AUTHOR]

Physical and/or economic constraints cause acquired seismic data to be incomplete; however, complete data are required for many subsequent seismic processing procedures. Data reconstruction is a crucial and long-standing topic in the exploration seismology field. We extended our previous works on deep learning (DL)-based irregularly and regularly missing 2-D data reconstruction to 3-D data. A key motivation is that the 3-D convolutional neural network (CNN) can take full advantage of the 3-D nature of the data, and the additional dimension allows more information to contribute to the data reconstruction. DL also avoids many assumptions (e.g., linearity, sparsity, and low-rank) limiting conventional nonintelligent reconstruction methods. We built an artificial neural network (ANN) based on an end-to-end U-Net encoder–decoder-style 3-D CNN. The ANN was trained on large quantities of various synthetic and field 3-D seismic data using a mean-squared-error (MSE) loss function and an Adam optimizer. We demonstrated that the developed 3-D CNN reconstruction method appears to outperform the 2-D CNN for 3-D restoration. We benchmarked the ANN’s generalization capacity for recovery of irregularly and regularly sampled 3-D data on several typical seismic data sets, particularly those with high missing percentages or large gaps. An ANN trained with irregularly sampled data can be partly applied to regularly sampled cases. We investigated how a key parameter, i.e., the learning rate, can be experimentally determined. In the context of the presented examples, our methodology provided a substantial improvement over an open-source state-of-the-art rank-reduction-based approach in terms of data fidelity and efficiency. [ABSTRACT FROM AUTHOR]

Radio-Frequency Identification (RFID) is an emerging technology which has been widely applied in various scenarios, such as tracking, object monitoring, and social networks, etc. Cloning attacks can severely disturb the RFID systems, such as missed detection for the missing tags. Although there are some techniques with physical architecture design or complicated encryption and cryptography proposed to prevent the tags from being cloned, it is difficult to definitely avoid the cloning attack. Therefore, cloning attack detection and identification are critical for the RFID systems. Prior works rely on that each clone tag will reply to the reader when its corresponding genuine tag is queried. In this article, we consider a more general attack model, in which each clone tag replies to the reader’s query with a predefined probability, i.e., attack probability. We concentrate on identifying the tags being attacked with the probability no less than a threshold $P_{t}$ with the required identification reliability $\alpha $. We first propose a basic protocol to Identify the Probabilistic Cloning Attacks with required identification reliability for the large-scale RFID systems called IPCA. Then we propose two enhanced protocols called MS-IPCA and S-IPCA respectively to improve the identification efficiency. We theoretically analyze the parameters of the proposed IPCA, MS-IPCA and S-IPCA protocols to maximize the identification efficiency. Finally we conduct extensive simulations to validate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

With the increasing number of vehicles, the traffic congestion is becoming more and more serious. In order to alleviate such a problem, this article considers transmission and inference delay of cloud centralized computing in the software defined Internet of Vehicles (SDIoV), and builds a new SDIoV architecture based on edge intelligence, for supporting real-time vehicle routing decision through distributed multi-agent reinforcement learning model. Then, a software defined device collaboration optimization method is designed to improve the efficiency of distributed training. Combined with multi-agent reinforcement learning, a distributed-learning-based vehicle routing decision algorithm (DLRD) is proposed to adaptively adjust vehicle routing online. The performed simulations show that the DLRD can successfully realize real-time routing decision for vehicles and alleviate traffic congestion with the dynamic changes of the road environment. [ABSTRACT FROM AUTHOR]

Log-structured-merge (LSM) trees are a highly write-optimized data structure for lightweight, high-performance key-value (KV) stores. Furthermore, solid-state drives (SSDs) are a crucial component for I/O acceleration. Conventional LSM-over-SSD designs involve multiple software layers, including the LSM tree, host file system, and flash translation layer (FTL), which introduce cascading write amplifications. To manage the write amplifications from different layers, we propose KVSSDs, which are a close integration of LSM trees and the FTL. KVSSDs exploit the FTL mapping mechanism to implement copy-free compaction of LSM trees, and they enables direct data allocation in flash memory for efficient garbage collection. Our design also uses a fine-grained, dynamic striping policy to fully exploit the rich internal parallelism of multichip SSDs. The experimental results indicated that our LSM-SSD integrated design reduced the write amplification by 86% and improved the throughput by 383% compared with a conventional LSM-over-SSD design. [ABSTRACT FROM AUTHOR]

The proliferation of the greedy heterogeneous wireless network demands the ability to handle the sharply increasing resource requirements of power and spectrum. Thus, the study of novel and effective methods to allocate power and spectrum resources in order to reduce energy consumption for heterogeneous devices has become an urgent issue. In this context the article proposes a DJRA in which data fusion, combined with NOMA, SIC and beamforming techniques, are utilized to allocate power and spectrum resources to heterogeneous devices with a reduced energy consumption, where different radio access technologies are integrated into a cellular network for communication between devices. The data correlations between the devices are computed to group the devices so as to reduce data transmission. In addition, NOMA, SIC and beamforming are used in combination to reduce network interference between BSs and the heterogeneous devices. The main contribution of this article is the design of a large-scale heterogeneous wireless network paradigm to improve the percentage of resource utilization also reducing the energy consumption. [ABSTRACT FROM AUTHOR]

Rapidly growing wireless networks are facing spectrum shortages, so how to improve spectrum utilization becomes critical. The rise of artificial intelligence (AI) technologies can provide a more intelligent and effective strategy for realizing cognitive wireless communication to improve spectrum utilization. Therefore, this article uses AI technology for data analytics, and combines cognitive technology to perform dynamic spectrum allocation. In terms of data analytics, AI technology is utilized in both feature extraction and data dimensionality, and the data correlation calculation between users. Then the data analytics results are applied to the spectrum allocation. Combined with deep learning, an AI-driven data-analytics-based spectrum allocation (ADASA) algorithm is proposed. ADASA enables the adaptive adjustment of the allocation parameters according to the network environmental status when allocating spectrum to users. Finally, the simulation results prove that the proposed ADASA algorithm can effectively improve the spectrum utilization in heterogeneous wireless networks. [ABSTRACT FROM AUTHOR]

Haptic feedback is crucial to immersive experience in virtual and augmented reality applications. The existing promising maglev haptic devices have advantages of no mechanical friction and low inertia. However, their performance is limited by the navigation approach, which mainly results from the challenge that it is difficult to obtain high precision, high frequency, and good stability with lightweight design at the same time. In this study, we reformulate visual-inertial navigation as a regression problem, and adopt deep learning to perform fusion navigation for maglev haptic interaction. A cascade-LSTM-based q-increment learning method is first proposed to progressively learn the increments of target variables. Two cascade LSTM networks are then constructed to estimate the increments of position and orientation, which are pipelined to accomplish visual-inertial fusion navigation. Additionally, we set up a maglev haptic platform as the system testbed. Experimental results show that our cascade-LSTMbased visual-inertial fusion navigation approach can reach 200 Hz while maintaining high-precision navigation (the mean absolute error of the position and orientation is less than 1 mm and 0.02°, respectively) for a maglev haptic interactive deformation application. [ABSTRACT FROM AUTHOR]

This paper develops a symmetric atmospheric plasma source integrated with electrospray ionization (ESI) tip as a dual-ion source for simultaneously detecting nonpolar and polar compounds using the ambient mass spectrometry. A cross-shaped tube with symmetric discharge electrodes is used to generate dielectric barrier discharge to produce ionized gas charging nonpolar molecules, while a coaxial capillary is placed at the center of the cross-shaped tube for generating electrospray discharge. Since there is no floating voltage at the outlet of the cross-shaped tube, the potential interference for the ESI can be excluded. Results show that dual-ion source can stably produce high-intensity ions of 109 ion $\cdot $ cm−3. Moreover, the relationship between the measured ion intensity and the gas flow rate, gas temperature, and applied voltage for ESI are systematically investigated. Various samples including volatile organic compounds, pesticides, Chinese herbal medicines, and vitamins are used for evaluating the sensing performance of the dual-ion source in plasma mode, electrospray mode, and dual-ion source mode, respectively. Results show that the developed dual source is efficient to simultaneously detect both the polar and nonpolar compounds. [ABSTRACT FROM AUTHOR]

With the rapid development of earth observation technology, the number of available remote sensing data has soared dramatically. It becomes a significant problem that how to use remote sensing images and how to improve the accuracy of change detection effectively. In this paper, a novel approach for change detection using weight-clustering sparse autoencoders (WCSAE) combined object-oriented classification with difference images (DIs) is proposed. First, bi-phase images are segmented as patches through the density-based spatial clustering of applications with noise algorithm. Afterward, the average and variance of superpixels are stacked as the input of WCSAE. To reduce the redundant information of extracted features, similar weights in the hidden layer of WCSAE are clustered layer-wise under termination conditions by using the hierarchical agglomerative clustering algorithm. For the improvement of the classification accuracy, L1/2 regularization is introduced in the objective function to extract more sparse features and avoid over-fitting. Next, the post-classification change detection map is obtained by means of comparing with classification results of two phase images. Then, by using the change vector analysis technology, the difference map is yielded and also classified under WCSAE to acquire the DI classification map. Finally, the joint probability judgment is implemented on the joint scopes to determine changed and unchanged areas. The effectiveness and superiority of the proposed method are verified in accordance with the experimental results on standard datasets and actual remote sensing images. [ABSTRACT FROM AUTHOR]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission. [ABSTRACT FROM AUTHOR]

A reconfigurable hybrid beamforming (HBF) system is presented to dynamically exploit the channel diversities and antenna array directivity in millimeter wave (mmWave) channels. To develop an effective method for HBF architecture adaptation in wideband mmWave channels, the performance of an architecture is evaluated based on its average frequency-domain channel capacity. According to our analysis and simulations, the spatial covariance of the frequency-domain channels of using HBF can still be approximately modeled in a Kronecker form. Furthermore, beamforming directivity and channel spatial diversity have different advantages on capacity improvement in different transceiving conditions. Based on the results of channel modeling, system analysis, and simulations, a reconfigurable HBF architecture is thus proposed to enable flexible adjustments of the active subarrays, the antennas and polarizations of each subarray, and the active analog front-end and radio frequency chains linked to the active subarrays. A training procedure is meanwhile developed for the HBF system to learn proper architectures according to the system constraints, the channel statistics, the baseband precoder structure, and the channel capacity between the transmitter and the receiver. Compared with a fixed HBF system, such a reconfigurable system provides 3–15-dB power gains in channel capacity under various simulated channel conditions. [ABSTRACT FROM PUBLISHER]

The space-terrestrial integrated network (STIN), one of the hottest trends in technology, is transforming our future through interconnecting heterogeneous devices. It has a revolutionary impact on reshaping the industry and changing the world. The extending of new generation network spectrum resources creates tremendous opportunities for obtaining high-level terrestrial network and brings a good prospect for the development of STIN, but STIN still faces a big change of resource shortage caused by the ever increasing heterogeneous devices. In this article, a new STIN architecture supporting virtualization technology is first designed to manage spectrum resource in STIN to satisfy various quality of service (QoS) requirements from heterogeneous devices. Then a dynamic virtualized QoS-driven spectrum allocation algorithm (VQSA) is proposed to improve the QoS from different devices in STIN. VQSA classifies heterogeneous devices into virtual cells according to their QoS correlation and determines the corresponding base stations and satellites for providing services. Spectrum resources in multiple base stations and satellites are rationally assigned in a virtual cell to serve heterogeneous devices with different QoS requirements. Finally, simulation results are provided to show the efficiency of the architecture and the VQSA algorithm in terms of transmission delay and transmission rate. [ABSTRACT FROM AUTHOR]

Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods. [ABSTRACT FROM PUBLISHER]

Video transmission is an indispensable component of most applications related to the mobile cloud networks (MCNs). However, because of the complexity of the communication environment and the limitation of resources, attempts to develop an effective solution for video transmission in the MCN face certain difficulties. In this paper, we propose a novel green video transmission (GVT) algorithm that uses video clustering and channel assignment to assist in video transmission. A video clustering model is designed based on game theory to classify the different video parts stored in mobile devices. Using the results of video clustering, the GVT algorithm provides the function of channel assignment, and its assignment process depends on the content of the video to improve channel utilization in the MCN. Extensive simulations are carried out to evaluate the GVT with several performance criteria. Our analysis and simulations show that the proposed GTV demonstrates a superior video transmission performance compared with the existing methods. [ABSTRACT FROM PUBLISHER]

Using the analogue of the electric inductance, we reveal the properties of the thermal inductance in GaN-based light-emitting diode devices by testing their transient thermal behaviors. We find that the devices exhibit a transient thermal response under step-down or step-up currents and observe notable inductive phenomena of the temperature response as time evolves from start up to some hundred microseconds. We define thermal inductance as the rapid change in device temperature that is opposite to the temperature change expected from the power input. These findings can promote new temperature measurements, and novel thermal analyses of high-frequency semiconductor devices that combining the thermal resistances, thermal capacitances, and thermal inductances. [ABSTRACT FROM AUTHOR]

For intrusion detection in 3D wireless sensor networks, the monitoring quality and the energy efficiency are both of great significance. In this paper, we develop a novel globoid model to ensure the all-directional detection quality while saving the network energy effectively, which divides the sensing area into outermost shell and interior region. We first propose an outermost shell coverage algorithm to guarantee the recognition quality of intruding events. Then, a Markov prediction model is designed to predict the motion probability in the adjacent area based on the historical trajectories of intruders. According to the predicted results, different working frequencies will be allocated to the covered nodes by using software defined radio technology. Moreover, a trajectory correction strategy is proposed to relocate the missing intruders during the operation. The performance evaluations show the efficiency of our scheme in terms of the network lifetime, trajectory prediction accuracy, and success rate of correction strategy. [ABSTRACT FROM PUBLISHER]

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian’s locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist. [ABSTRACT FROM PUBLISHER]

This paper presents a tri-mode digital buck converter in 0.18\-\upmu \text m CMOS technology for photovoltaic energy harvesting. The on-chip gate-boosted digital pulsewidth modulation (DPWM) improves the conversion efficiency at heavy load conditions. Pulse–frequency modulation (PFM) along with digital self-tracking zero current detection is proposed to avoid reverse current at light load. The asynchronous mode (AM) operation further reduces the controller loss and improves the conversion efficiency at ultra-light load conditions. By applying DPWM, PFM, and AM at different load conditions, the proposed converter provides a maximum conversion efficiency of 92% with output power ranging from 50 nW to 10 mW. In addition, the proposed buck converter achieves more than 70% efficiency from 400 nW to 10 mW output power. [ABSTRACT FROM PUBLISHER]

The emerging 5G wireless networks are envisioned to provide dramatically increased network capacity by using significantly expanded frequency band. With already crowded radio spectrum, utilization efficiency of frequency resources needs to be substantially improved in 5G to accommodate more users. On the other hand, the extremely wide and dynamic spectrum resources required by 5G create a significant opportunity to develop advanced resource management techniques. In this article, we propose a novel architecture to support 5G spectrum management, which uses various requirements for QoE as the design objective. With the proposed architecture, an intelligent and dynamic QoE-driven spectrum assignment scheme for 5G is introduced. The proposed scheme accomplishes dynamic spectrum assignment for each macrocell by utilizing global information that reflects the dynamic QoE requirement. In addition, we effectively allocate the spectrum bands for various devices relying on the reconfigurable RF front-end of SDR-based devices. The presented architecture is expected to support dynamic and efficient spectrum management for 5G wireless networks. Meanwhile, the intelligent and dynamic QoE-driven spectrum assignment scheme is expected to achieve an improved spectrum utilization rate, which could directly accelerate the development of 5G wireless networks. [ABSTRACT FROM PUBLISHER]

Biogenic nanoporous shells derived from marine diatoms Coscinodiscus wailesii were used for nanoparticle translocation experiments, and the observed ionic current was compared with results of analytical calculation and finite-element simulation. The lateral size ( 250~\mu \textm ) of the shells enabled positioning and immobilization on micromachined silicon substrates. Since these shells have a hierarchical structure with the smallest aperture diameter of 40 nm and length of 50 nm as obtained from scanning electron microscopy, the ionic current reduction of 28.69 ± 4.12 pA could be measured during the translocation of 27-nm diameter polystyrene spheres. Note, this average current of 10 events, measured for 120 s, was comparable with the results of: 1) finite-element simulation as a function of the position of the polystyrene sphere using a simplified geometry of the smallest aperture and 2) analytical calculation from the Coulter Counter theory. The current reduction obtained from the simulation and theory was 28.36 and 29.95 pA, respectively. In addition, a mobility of 1.11 \times 10^-8~\textm^2\texts^-1\textV^-1 for the 27-nm polystyrene spheres was used to convert the simulated current from spatial dependence to time dependence in order to match the average experimental translocation time of 155~\mu \texts . [ABSTRACT FROM PUBLISHER]

The efficacy of channel modeling and training for multiple-input multiple-output (MIMO) time-varying flat faded Rayleigh channels is studied herein from the information-theoretical perspective. To characterize the channel dynamics in wide-sense stationary uncorrelated scattering wireless environments, proper autoregressive (AR) channel models for different fading speeds are discussed from the viewpoints of the mean squared error (MSE) and Bayesian Cramér-Rao lower bound (BCRB) of channel estimation. Furthermore, the training efficacy is examined with the achievable capacity when the MSE of channel estimation attains the BCRB. Our numerical simulations show that neither is the first-order AR model enough, nor is a large-order AR model needed for modeling time-varying fading channels. The analysis on BCRB also shows that the influence of the multiple access interference among transmit antennas is not negligible for channel estimation in time-varying fading channels even if using orthogonal sequences for training. As channel tracking can utilize the current and all past training symbols, the optimal training lengths for each data packet may be less than the number of transmit antennas. These results help re-examine the efficacy of model complexity and training overhead, and characterize the achievable rate for MIMO systems that use more practical methods in estimating Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

Owing to its attractive features such as fast identification and relatively long interrogating range over the classical barcode systems, radio-frequency identification (RFID) technology possesses a promising prospect in many practical applications such as inventory control and supply chain management. However, unknown tags appear in RFID systems when the tagged objects are misplaced or unregistered tagged objects are moved in, which often causes huge economic losses. This paper addresses an important and challenging problem of unknown tag identification in large-scale RFID systems. The existing protocols leverage the Aloha-like schemes to distinguish the unknown tags from known tags at the slot level, which are of low time-efficiency, and thus can hardly satisfy the delay-sensitive applications. To fill in this gap, two filtering-based protocols (at the bit level) are proposed in this paper to address the problem of unknown tag identification efficiently. Theoretical analysis of the protocol parameters is performed to minimize the execution time of the proposed protocols. Extensive simulation experiments are conducted to evaluate the performance of the protocols. The results demonstrate that the proposed protocols significantly outperform the currently most promising protocols. [ABSTRACT FROM AUTHOR]