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A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm's flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman's coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization. [ABSTRACT FROM AUTHOR]

Symbiotic Radio Networks (SRNs) have emerged as an important communication protocol to solve the increasing energy demand and spectrum resource shortage. However, the low bit rates of the devices working in SRNs during backscatter communication, where the surrounding radio frequency resources are used by subsystems different from the main system, make SRNs very vulnerable to external attacks such as eavesdropping and jamming. To solve this problem, the Physical Layer Security (PLS) for SRNs with Signal Emitter (SE), user, jammer, receiver and eavesdropper (ED) is analyzed. While the SE conveys its information to the receiver, the user assists the SE in part of the time period and transmits its information to the receiver in the other part. While ED is overhearing SE and user's information over the wiretap channel, the jammer is trying to prevent ED with the signal it emits. This model, in which the secrecy rate is maximized over time parameters, is the first approach in which PLS analysis is carried out in the presence of a cooperative jammer when the perfect/imperfect Successive Interference Cancellation (SIC) technique is used at the receiver. Numerical results show that the existence of a symbiotic relationship between the user and the SE increases the secrecy rate of the system compared to the non-symbiotic situation. Moreover, adopting the perfect SIC technique at the receiver without energy constraint at the user resulted in a significant increase in PLS performance compared to the imperfect SIC under energy constraint. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic quadrotor unmanned aircraft vehicle driven by bidirectional electronic speed controllers is proposed to enhance maneuverability and stability. Bidirectional electronic speed controllers are applied to achieve rapid deceleration of motors during flight. To match with bidirectional electronic speed controllers, fractional order Proportional-Integral-Derivative (PID) controllers are considered to attain better rapidity compared to PID controllers, and an innovative control allocation matrix with direction symbols is developed. The model, controllers, and allocation methods have been proven an effective scheme in simulations of attitude and position tracking. [ABSTRACT FROM AUTHOR]

The spectrum sensing function plays a significant role in the performance of cognitive radio (CR). Spectrum sensing specifies if free channels exist and identifies free channels for secondary users, actively helping in the improvement of spectrum usage and recognizing available channels in CR systems. Cyclostationary feature detection (CFD) is a spectrum sensing method that detects signals depending on different characteristics such as carrier frequency, modulation types, cyclic frequency, and symbol rates with an extremely low signal-to-noise ratio. At low SNR, CFD achieves a detection process with a high computation complexity. This paper designs Enhanced Cyclostationary Detector complexity with improved detection speed performances. For the sake of minimizing system complexity, utilizing the advantages of the Haar wavelet transform and signed correlator method for estimating the cyclic spectra of a detected signal. The proposed method performance was evaluated over Rayleigh flat fading and AWGN channel that had low SNR values. The acquired simulation results indicated the efficiency of the proposed method in terms of reduction 70% in complexity, 60% in time, and 7% in memory storage, with improved detection performance that is about 8% compared to conventional method at low SNR values reach to -30dB. [ABSTRACT FROM AUTHOR]

Purpose: In Sri Lanka, the Radio Frequency Spectrum (RF spectrum) has traditionally been overseen managed and regulated by the government regulatory body(s), mainly accommodating the administrative RF spectrum management regime. This approach entails allocating and assigning RF spectrum to licensed operators solely through administrative means, with the regulatory authority having sole control and rights over it. However, in line with global trends, in recent years, many countries have begun to transition towards market-based approaches to RF spectrum management from legacy administrative regime, aiming to improve efficiency, promote innovation, and foster competition in the telecommunications sector. Currently, the regulatory landscape in Sri Lanka is also exploring the transition towards a market-oriented spectrum management regime. In this context, this legal research article critically analyses the viability and appropriateness of the transition from administrative RF spectrum management to market-based RF spectrum management in Sri Lanka in a regulatory context, highlighting various dimensions to be considered while migrating. Design/methodology/approach: In this research paper, the authors have considered the regulatory aspects of the transition from an administrative to a marked-based approach for the RF spectrum in Sri Lanka. Hence, the authors shall be carrying out a legal research using the doctrinal research methodology, which is commonly used in legal research. This research article considers John A. Posner’s law and economic theory and John Lock’s property rights theory as the theoretical foundation. Further, this research paper uses qualitative research methodology. By using the said methodological approach, the authors analyse the current administrative RF spectrum management regime in Sri Lanka comparing it with global trends towards market-based approach and thereby evaluate the primary objectives behind the transition, the current RF spectrum framework, and the anticipated challenges that they would encounter with the transition. Findings: The analysis revels that RF spectrum management in Sri Lanka under the administrative system is restrictive and hinders innovation and competition, compared to a more liberalized market-based approach. A market-based approach, characterized by auctions, in primary markets and spectrum sharing, trading, and leasing in secondary markets identified as a potential solution to these limitations. However, the study also highlights several challenges, including the need for regulatory reform, capacity building, and the development of a robust legal framework to support the transition. Originality: This paper contributes to the existing body of knowledge by providing a detailed analysis of the potential shift in RF spectrum management practices within Sri Lanka, in the regulatory context, a subject area that has not been extensively explored in the local context. It offers a comparative perspective with international practices and provides actionable recommendations tailored to Sri Lankan conditions. Implications: The findings suggest that transitioning to a market-based RF spectrum management regime could significantly improve the efficiency and dynamism of Sri Lanka's telecommunications sector. However, careful planning and implementation are crucial to address potential challenges and ensure that the transition supports long-term goals of innovation and competition. The recommendations aim to guide policymakers in developing a framework that balances market efficiency with regulatory oversight. [ABSTRACT FROM AUTHOR]

Picking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method. [ABSTRACT FROM AUTHOR]

This paper proposes a core and spectrum allocation algorithm for elastic optical networks based on multi-core fibers. In this context, the fragmentation and crosstalk mitigation algorithm (FraCA) is proposed. FraCA implements mechanisms to reduce spectral fragmentation and inter-core crosstalk in the network, proving efficient when compared with six other algorithms reported in the literature. The numerical results show that when compared with the most competitive of the six algorithms, FraCA achieves a gain of request blocking probability of at least 16.87%, a gain of bandwidth blocking probability of at least 43.95%, and a mean increase in spectral utilization of at least 4.36%. [ABSTRACT FROM AUTHOR]

In the existing velocity filtering based track‐before‐detect (VF‐TBD), target existence is generally assumed to be constant, and multiple successive frames in a sliding window are jointly processed. However, when the target existence changes abruptly in a processing batch, the incorporation of noise‐only frames may corrupt target energy integration. This results in degraded detection and estimation performances with serious decision delays. A pseudo‐spectrum based VF‐TBD is proposed to effectively detect and track targets with existence uncertainty. An adaptive integration strategy is developed to realise effective energy accumulation with only informative frames by matching target appearance and disappearance times in addition to the velocity. The change of target existence can be discovered in time, and the decision delay is reduced. An estimation strategy based on the maximum output SNR is presented to supply accurate estimations of not only target motion parameters (including the position and velocity) but also time parameters (including appearance and disappearance times). The target output envelope, SNR gain and receiver operating characteristic curve of targets with uncertain existence are analysed theoretically. Numerical simulations exhibit the validity of the proposed method. The cover image is based on the Original Article Adaptive pseudo‐spectrum based track‐before‐detect for targets with uncertain existence by Peiyuan Li et al., https://doi.org/10.1049/rsn2.12502. [ABSTRACT FROM AUTHOR]

Introduction/purpose: This article analyzes the publicly available literature on drone classification in the radio frequency domain, focusing on detection and identification. Drones are increasingly used for illegal purposes, making classification techniques crucial. This review paper covers passive radio frequency sensors, classification techniques, and datasets that highlight the challenges. Methods: Researchers are developing antidrone solutions because drones have become valuable tools and targets for illegal activities. Due to the scope of the subject matter, the review included only the classification of drones via passive radio frequency sensors with a description of the classification techniques (set of algorithms, methods, and procedures) and the datasets used for performance testing. This study introduces a new categorization and offers deeper insights into publicly available drone classification techniques. Results: Based on the results of this study, it is apparent that deep learning algorithms are presently the most effective approach to addressing the challenge of drone classification within the radio frequency domain. One of the primary obstacles is the absence of a comprehensive standard for classifying drones in the radio frequency domain, which should be based on end-user requirements. Additionally, the results of two ablative experiments highlight the preprocessing of raw I/Q radio signals as an essential step in drone classification. Conclusion: In summary, the proposed categorization provides a valuable tool for literature review. Deep learning is the most effective technique for drone classification, but publicly available datasets with drone radio signals are limited. The key strength of this study is that it represents the first review of publicly available datasets with drone radio signals. [ABSTRACT FROM AUTHOR]

In this study, the unsteady Reynolds-averaged Navier–Stokes (URANS) equations are employed in conjunction with the Menter Shear Stress Transport (SST)-Scale-Adaptive Simulation (SAS) turbulence model in compressible flow, with an unstructured mesh and complex geometry. While other scale-resolving approaches in space and time, such as direct numerical simulation (DNS) and large-eddy simulation (LES), supply more comprehensive information about the turbulent energy spectrum of the fluctuating component of the flow, they imply computationally intensive situations, usually performed over structured meshes and relatively simple geometries. In contrast, the SAS approach is designed according to "physically" prescribed length scales of the flow. More precisely, it operates by locally comparing the length scale of the modeled turbulence to the von Karman length scale (which depends on the local first- and second fluid velocity derivatives). This length-scale ratio allows the flow to dynamically adjust the local eddy viscosity in order to better capture the large-scale motions (LSMs) in unsteady regions of URANS simulations. While SAS may be constrained to model only low flow frequencies or wavenumbers (i.e., LSM), its versatility and low computational cost make it attractive for obtaining a quick first insight of the flow physics, particularly in those situations dominated by strong flow unsteadiness. The selected numerical application is the well-known M219 three-dimensional rectangular acoustic cavity from the literature at two different free-stream Mach numbers,  M ∞  (0.85 and 1.35) and a length-to-depth ratio of 5:1. Thus, we consider the "deep configuration" in experiments by Henshaw. The SST-SAS model demonstrates a satisfactory compromise between simplicity, accuracy, and flow physics description. [ABSTRACT FROM AUTHOR]

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals. [ABSTRACT FROM AUTHOR]

Relay-assisted hybrid beamforming plays an inevitable role in enhancing network coverage, transmission range, and spectral efficiency while simultaneously reducing hardware cost, power consumption, and hardware implementation complexity. This study investigates a cognitive radio network (CRN)-based hybrid wideband transceiver for millimeter-wave (mm-wave) decode-and-forward (DF) relay-assisted multiuser (MU) multiple-input multiple-output (MIMO) systems. It is worth mentioning that the underlying problem has not been addressed so far, which is a real motivation behind the proposed algorithm. The joint optimization of hybrid processing components and the constant amplitude constraints imposed by the analog beamforming solution make this problem non-convex and NP-hard. Furthermore, the analog beamformer common to all sub-carriers is another challenging aspect of the underlying problem. To derive the frequency-flat analog processing component in the radio frequency (RF) domain and frequency-dependent baseband processing matrices in the baseband domain, the original complicated problem is reformulated as two single-hop sum-rate maximization sub-problems. Taking advantage of this decomposition, the sum spectral efficiency is maximized through RF precoding and combining. On the other hand, the impact of interference among transmitted data streams and inter-user interference (IUI) is minimized via baseband processing matrices. Finally, computer simulations are conducted by changing system parameters, considering both perfect and imperfect channel state information (CSI). Simulation results demonstrate that the proposed algorithm achieves performance close to full-complexity precoding and outperforms other well-known hybrid beamforming techniques. Specifically, more than 95% efficiency is achieved with perfect CSI, and more than 90% efficiency is attained under the assumption of 30% error in the estimated channels. [ABSTRACT FROM AUTHOR]

Free space optical communication (FSO) is thriving with greater pace nowadays due to its ability to support high speed, greater capacity, improved security, ease of installation, not having of Electromagnetic interference (EMI) and unlicensed spectrum. Atmospheric turbulence like Rain, Fog, and Haze is a prominent issue in FSO system that adversely affects the performance. This paper presents the demonstration of a multibeam FSO system operating at 1.25 Gbps in several haze types, including hazardous, very unhealthy, sensitive, good, moderate, and unhealthy. Additionally, BER and Q factor comparisons are made across three distinct pulse formats: carrier suppressed RZ (CSRZ), return to zero (RZ), and non-return to zero (NRZ). Utmost goal for the deployment of multibeam FSO is to get high received power and to compensate attenuation arising from haze effects. Results revealed that CSRZ performed exceptionally well as compared to NRZ and RZ. [ABSTRACT FROM AUTHOR]

Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user's protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms. [ABSTRACT FROM AUTHOR]

Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly affects the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the multi-information combination convolutional neural network (MCCN) velocity auto-picking method. Building on the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we use a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach. [ABSTRACT FROM AUTHOR]

According to the current challenges of spectrum scarcity, poor wireless communication quality, insufficient energy supply and physical layer security, this paper proposes a secure transmission scheme for multi-hop cognitive relay radio networks based on the underlay mode. When a passive eavesdropper E exists in the transmission environment, secondary source S needs to transmit secrecy information to the legitimate destination D. For the underlay spectrum sharing strategy, S and cognitive relays access the licensed spectrum concurrently with primary transmission by controlling the interference to the primary networks under the interference threshold. In order to achieve better communication between S and D, this paper adds some secondary relays to assist cognitive communication, and these secondary relays are equipped with energy harvesting (EH) devices that will collect energy from the surrounding radio frequency (RF) environment based on the time switching strategy. Then, this energy will supply consumption of decoding and forwarding (DF) the received signal. In the process of forwarding the signal through the secondary relays to D, artificial noise is added to the last relay so that D can successfully receive the message while preventing E from eavesdropping on the secret message. This paper analyzes the influence of target data rates, energy harvesting times, interference thresholds, and other factors on secrecy outage probability (SOP), secrecy capacity (SC), and energy efficiency (EE). [ABSTRACT FROM AUTHOR]

This study investigates the heat transfer enhancement by electrohydrodynamic (EHD) in a rectangular channel with various geometric and operational parameters in a wide NEHD range of 0.4–5. Here, NEHD is a dimensionless number, describing the ratio of electrostatic, inertial, and viscous forces. Two working regimes in EHD are identified: inertial and electrostatic regimes. The results show that the barrier effect exists only in the inertial regime; however, the interference effect occurs in the electrostatic regime. The barrier effect relies heavily on NEHD rather than the dimensionless distance between electrodes l* and the dimensionless channel width H* because the thermal boundary layer cannot be disturbed sufficiently by a small electrostatic force; however, the interference effect depends on l* and H* rather than NEHD due to the stagnant area initiated only by a strong interaction between adjacent emitting electrodes. A new effect, the "blocking effect," is found in the electrostatic regime. The mechanism of the blocking effect is different from that of the barrier and interference effects. The blocking effect is initiated by a giant vortex, which "blocks" the airflow flowing toward the downstream channel. The average Nusselt number of channels can be reduced by at least 8%, 13%, and 5% for the barrier, interference, and blocking effects, respectively. A working spectrum of EHD-induced heat transfer enhancement in a rectangular channel is provided under NEHD and channel area coupling conditions. We believe the spectrum can help in designing EHD-induced heat transfer enhancement because it provides theoretical guidance for avoiding the three effects. [ABSTRACT FROM AUTHOR]

This paper illustrates that campus radio continues to be relevant within the Canadian media ecosystem. Because of the unique commitments of campus-community radio, this sector constitutes an important part of the Canadian broadcasting ecosystem. In July 2019, the Canadian Radio-television and Telecommunications Commission licensed Caper Radio as a campus broadcaster located in Nova Scotia's Cape Breton University. This marked a milestone in the evolution of a broadcaster that started as a cafeteria stereo system in the 90s and which enjoys cultural significance within the local creative arts milieu. Within the context of Canadian broadcasting policy and regulation, this article draws on archival data and semi-structured interviews to account for Caper Radio's evolution. It argues that Caper Radio has historically existed on the margins not just of the broadcast spectrum but also within its host university. However, as its recent licensing and shift towards more engagement by institutional stakeholders show, Caper Radio continues to be relevant as a campus broadcaster. The history of Caper Radio contributes to the literature on campus broadcasting in Canada. [ABSTRACT FROM AUTHOR]

The dual hyperbolic frequency modulated (Dual-HFM) speed spectrum estimation method provides high-resolution continuous Doppler estimation results under ideal conditions. However, the sidelobe suppression caused by the multipath and dual-target environment weakens the ability of the speed spectrum method to suppress noise and affects the target parameter estimation. In this paper, the application of this method in various complex underwater acoustic environments such as multipath and dual-target is discussed in depth, and the position of the speed spectrum sidelobe caused by multipath and dual-target is derived. The speed spectrum sidelobe suppression method based on a multi-frame signal is proposed. It uses the difference between the target echo information and the multipath sidelobe clutter in the signal space of another dimension to suppress the speed spectrum sidelobe in the complex underwater environment, and retains the advantages of low speed spectrum calculation. The applicability of the proposed method is verified by numerical simulation, which provides a theoretical basis for Doppler estimation in low signal-to-noise ratio, multipath and dual-target environments. [ABSTRACT FROM AUTHOR]

Detecting acoustic signals in the ocean is crucial for port and coastal security, but existing methods often require informative priors. This paper introduces a new approach that transforms acoustic signal detection into network characterization using a MCN construction method. The method constructs a network representation of the acoustic signal by measuring pairwise correlations at different time scales. It proposes a network spectrum distance method that combines information geometry and graph signal processing theory to characterize these complex networks. By comparing the spectra of two networks, the method quantifies their similarity or dissimilarity, enabling comparisons of multi-scale correlation networks constructed from different time series data and tracking changes in nonlinear dynamics over time. The effectiveness of these methods is substantiated through comprehensive simulations and real-world data collected from the South China Sea. The results illustrate that the proposed approach attains a significant detection probability of over 90% when the signal-to-noise ratio exceeds −18 dB, whereas existing methods require a signal-to-noise ratio of at least −15 dB to achieve a comparable detection probability. This innovative approach holds promising applications in bolstering port security, facilitating coastal operations, and optimizing offshore activities by enabling more efficient detection of weak acoustic signals. [ABSTRACT FROM AUTHOR]

This paper proposes a low-divergence method to generate inhomogeneous and anisotropic turbulence. Based on the idea of correlation reconstruction, the proposed method uses the Cholesky decomposition matrix to re-establish turbulence correlation functions. This is in contrast with the time-consuming procedure used in conventional methods to solve eigenvalues and eigenvectors in each location required by coordinate transformation, thereby reducing the computational complexity and improving the efficiency of synthetic turbulence generation. The proposed method is based on the classical spectrum-based method that is widely used to generate homogeneous and isotropic turbulence. By adjusting the generation strategy of specific random vectors, inhomogeneous and anisotropic turbulence with a relatively low divergence level can be obtained in practice, with almost no additional computational burden. Two versions of the proposed method are presented: the inverter and shifter versions. Both the versions are highly efficient, easy to implement, and compatible with high-performance computing. This method is also suitable for providing high-quality initial or boundary conditions for scale-resolving turbulence simulations with large grid numbers (such as direct numerical simulations or large eddy simulations); it can be rapidly implemented using various open-source computational fluid dynamics codes or common commercial software. [ABSTRACT FROM AUTHOR]

A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence and low search accuracy. To enhance the diversity of the golden eagle, the algorithm is initialized with the Arnold chaotic map. Furthermore, nonlinear convex weight reduction is incorporated into the position update formula of the golden eagle, improving the algorithm's ability to perform both local and global searches. Additionally, a final global optimization strategy is introduced, allowing the golden eagle to position itself in the best possible location. The effectiveness of the enhanced algorithm is evaluated through simulations using 12 benchmark test functions, demonstrating improved optimization performance. The algorithm is also tested using the CEC2021 test set to assess its performance against other algorithms. Several statistical tests are conducted to compare the efficacy of each method, with the enhanced algorithm consistently outperforming the others. To further validate the algorithm, it is applied to the cognitive radio spectrum allocation problem after discretization, and the results are compared to those obtained using traditional methods. The results indicate the successful operation of the updated algorithm. The effectiveness of the algorithm is further evaluated through five engineering design tasks, which provide additional evidence of its efficacy. [ABSTRACT FROM AUTHOR]

In this paper, for the wireless network, wearable device, and Internet of Things (IoT) markets, a delay-locked loop (DLL) is used to implement accurate multiplication for a reference clock and the frequency of various applications through an edge combiner (EC). A simpler structure is more sensitive to process, voltage, and temperature (PVT), so DLL complements itself quickly in the feedback system and improves the stability of the final output. The proposed DLL-based multiplier can prevent harmonic lock generation using a first phase canceller (FPC), thus compensating for faster lock time. The circuit is built with a 55 nm CMOS process and has a chip area of 0.0225 mm2. The proposed design achieves a total power consumption of 0.48 mW at the 30.72 MHz operating clock frequency, and the clock duty can also operate stably from 15 to 75%. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, we focus on a periodic resource allocation problem applied on a dynamical system which comes from a biological system. More precisely, we consider a system with N$$ N $$ resources and N$$ N $$ activities, each activity use the allocated resource to evolve up to a given time T>0$$ T>0 $$ where a control (represented by a given permutation) will be applied on the system to reallocate the resources. The goal is to find the optimal control strategies which optimize the cost or the benefit of the system. This problem can be illustrated by an industrial biological application, namely, the optimization of a mixing strategy to enhance the growth rate in a microalgal raceway system. A mixing device, such as a paddle wheel, is considered to control the rearrangement of the depth of the algae cultures, hence the light perceived at each lap. We prove that if the dynamics of the system is periodic, then the period corresponds to one reallocation whatever the order of the involved permutation matrix is. A nonlinear optimization problem for one reallocation process is then introduced. Since N!$$ N! $$ permutations need to be tested in the general case, it can be numerically solved only for a limited number of N$$ N $$. To overcome this difficulty, we introduce a second optimization problem which provides a suboptimal solution of the initial problem, but whose solution can be determined explicitly. A sufficient condition to characterize cases where the two problems have the same solution is given. Some numerical experiments are performed to assess the benefit of optimal strategies in various settings. [ABSTRACT FROM AUTHOR]

Identifying the modulation type of radio signals is challenging in both military and civilian applications such as radio monitoring and spectrum allocation. This has become more difficult as the number of signal types increases and the channel environment becomes more complex. Deep learning-based automatic modulation classification (AMC) methods have recently achieved state-of-the-art performance with massive amounts of data. However, existing models struggle to achieve the required level of accuracy, guarantee real-time performance at edge devices, and achieve higher classification performance on high-performance computing platforms when deployed on various platforms. In this paper, we present a family of AMC models based on communication domain knowledge for various computing platforms. The higher-order statistical properties of signals, customized data augmentation methods, and narrowband convolution kernels are the domain knowledge that is specifically employed to the AMC task and neural network backbone. We used separable convolution and depth-wise convolution with very few residual connections to create our lightweight model, which has only 4.61k parameters while maintaining accuracy. On the four different platforms, the classification accuracy and inference time outperformed those of the existing lightweight models. Meanwhile, we use the squeeze-and-excitation attention mechanism, channel shuffle module, and expert feature parallel branch to improve the classification accuracy. On the three most frequently used benchmark datasets, the high-accuracy models achieved state-of-the-art average accuracies of 64.63%, 67.22%, and 65.03%, respectively. Furthermore, we propose a generic framework for evaluating the complexity of deep learning models and use it to comprehensively assess the complexity strengths of the proposed models. [ABSTRACT FROM AUTHOR]

We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium–aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximum of 26% missing frequencies. In summary, our modulated fingerprint method is an efficient tool to transform raw spectroscopy data and train neural network models with high accuracy and resistance to spectra distortion. [ABSTRACT FROM AUTHOR]

Summary: An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied. [ABSTRACT FROM AUTHOR]

Space division multiplexing elastic optical networks (SDM-EONs) based on multi-core fiber (MCF) technologies have attracted widespread attention as a potential means of enhancing large capacity and high flexibility. However, inter-core crosstalk (XT) degrades the quality of transmission. The algorithm for minimizing XT leads to an increase in spectrum fragmentation in the lightpath, which influences the spectrum utilization. Therefore, the question of how to comprehensively consider the two factors and improve the network performance is an issue worthy of study. This paper focuses on maximizing spectrum resource utilization while satisfying the XT constraints. Firstly, we optimize a three-dimensional metric model to evaluate XT and fragmentation more exactly in SDM-EONs. Furthermore, a crosstalk classification (CC) algorithm, which can adjust the XT constraints according to the actual situation of the network, is proposed. Moreover, to match the CC algorithm, we describe the crosstalk and fragmentation in the network and propose a synthetically consider crosstalk and fragmentation (SCCF) algorithm. Finally, simulation results show that the proposed CC-SCCF routing, modulation, core, and spectrum allocation algorithm reduces the XT on existing lightpaths, and also provides a lower probability of blocking and greater spectrum utilization. [ABSTRACT FROM AUTHOR]

The channel length required for the development of the flow, from the channel entrance to full establishment, is often a prerequisite when designing hydraulic structures or planning research experiments in open channels. However, the information on the flow development length ( L D ) is scarce, and even its definition remains vague. In hydraulic experiments, this lack of knowledge introduces great uncertainty, often making comparisons of findings from different studies questionable. This paper offers a physics-based definition for L D , and reports results of systematic laboratory studies to provide guidance on its quantitative assessment. Our data for uniform flows suggest that up to 100 flow depths ( H) are required for mean velocity field (including sidewall secondary currents), turbulent stresses (except streamwise variance), velocity skewness and kurtosis, and depth-scale large-scale-motions to become essentially independent of the streamwise coordinate. However, very large-scale-motions, streamwise velocity variance, and roughness-induced secondary currents are found to require longer L D of around 150 H. [ABSTRACT FROM AUTHOR]

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Cognitive relay network is a spectrum dynamic paradigm that exploits the unused portions of the licensed spectrum. This is based on merging both cooperative relaying techniques and cognitive radio network to achieve spectrum efficiency and enhance the overall system performance. In this paper, the presence of mobile users at the destination node is considered. Here, the end users can navigate at relatively fast vehicular velocities causing dynamic multipath fading and high Doppler shift. which can be fairly modelled using Nakagami‐m$m\;$fading channel (i.e. m<1$\;m < 1$). In a spectrum scarcity environment, a secondary user must deploy an optimal power allocation policy to get higher transmission rates while the overall interference affecting the primary user (PU) is kept below a certain threshold value. In particular, the outage probability (OP) performance is studied over the mixed Rayleigh and Nakagami‐m fading channels for different scenarios and a tight closed‐form expressions are derived for the system OP of underlay dual‐hop cognitive relay networks with a single amplifiy‐and‐forward (AF) relay with and without the use of the direct link transmission and selection diversity at the destination with interference power constraints for the primary network over independent and non‐identical (i.n.i.d) Rayleigh and Nakagami‐m fading channels when m<1$m < 1$ based on the statistical characteristics of signal‐to‐noise ratio. Numerical results are presented to evaluate the impact of the fading parameter, m, the maximum aggregated intrusion constraint, and the locations of the primary users (PUs) on different channel scenarios at high vehicular speeds. Monte Carlo simulations are presented to verify and validate analytical results. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH. [ABSTRACT FROM AUTHOR]

The depiction model presents a major advance in our theoretical conceptualization of how humans experience and understand social robots. But the scope of the model is, I suggest, more limited: It pertains to one possible phase in a more comprehensive cognitive-practical dynamics of sense-making ("sociomorphing") as conceptualized in the OASIS framework. According to the OASIS framework, some basic social actions can be realized by robots, while others may be depicted in the way described by the model. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad‐hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR‐based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on‐demand distance vector routing protocol is proposed for CR‐based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed. [ABSTRACT FROM AUTHOR]

To address the control allocation problem caused by the redundant arrangement of control surfaces with nonlinear effectiveness for tailless aircraft, a novel multiobjective incremental control allocation (MICA) strategy is proposed. Firstly, the incremental nonlinear control allocation (INCA) method together with the active set quadratic programming algorithm is adopted to precisely allocate the virtual control commands. Secondly, a series of normalized objective functions in the form of increment are designed. Combining these functions by means of linear weighted sum, an incremental multiobjective function is constructed. Then, an improved nondominated sorting genetic algorithm (INSGA) is introduced to offline determine a set of weights that best meets the requirements of each flight phase. In this way, the dependence on subjective experience is minimized based on the theory of Pareto optimal. Meanwhile, the huge computational burden that the intelligent optimization algorithm brings can also be avoided. Finally, combined with the nonlinear dynamic inversion (NDI) control method, a closed-loop validation for the effectiveness of this control allocation strategy is carried out on the tailless aircraft model. [ABSTRACT FROM AUTHOR]

The availability of the propulsion system is of primary importance to ensure safe and stable operations of marine crafts, both during transit and station keeping. Diminished propulsion efficiency could impair the ability of a vessel to maintain speed and course and possibly lead to a drifting craft. The waterjet's propulsion efficiency is affected by several factors such as cavitation, erosion, vibration and noise emission. This paper addresses the design of a fault-tolerant thrust allocation algorithm able to maintain the seaworthiness of a twin-waterjet marine craft in the presence of a severe power loss in one of the waterjets. The proposed solution combines a load torque estimator with an optimization routine that accounts for the power limits when a waterjet is subject to a power loss. This prevents faults from quickly escalating into a complete failure of the waterjet due to excessive power demands. Two simulated case studies including zig-zag path following and sideways movements are presented to demonstrate the effectiveness of the fault tolerant control thrust allocation strategy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) provides opportunities to enable high-efficiency wireless communication to dynamically adapt to the local environments and user demands. In this article, a continual learning digital predistortion (DPD) algorithm is proposed to linearize radio frequency (RF) power amplifier (PA) in 6G AI-empowered wireless communication. It is achieved by employing the ability of knowledge retention and knowledge transfer to merge the new PA operating state into the previous model, which makes the model more intelligent when dealing with multiple states. In addition, an augmented self-sensing model is proposed to accurately capture the common characteristics and key dynamic characteristics among different PA operating states to improve the knowledge transfer ability during the continual learning process. The experiments were carried out on a Doherty PA with dynamic configurations associated with power, bandwidth, and signal type. The experimental results have shown that the proposed technique can continuously learn when faced with unpredicted new PA operating states and so as to linearize all known states. The proposed DPD method performs with very low complexity in the long term, which is very suitable for the emerging AI-empowered dynamic scenario. [ABSTRACT FROM AUTHOR]

The method of using millimeter-wave radar sensors to detect human vital signs, namely respiration and heart rate, has received widespread attention in non-contact monitoring. These sensors are compact, lightweight, and able to sense and detect various scenarios. However, it still faces serious problems of noisy interference in hardware, which leads to a low signal-to-noise ratio (SNR). We used a frequency-modulated continuous wave (FMCW) radar sensor operating at 77 GHz in an office environment to extract the respiration and heart rate of a person accustomed to sitting in a chair. Indeed, the proposed signal processing includes novel impulse denoising operations and the spectral estimation decision method, which are unique in terms of noise reduction and accuracy improvement. In addition, the proposed method provides high-quality, repeatable respiration and heart rates with relative errors of 1.33% and 1.96% on average compared with the reference values measured by a reliable smart bracelet. [ABSTRACT FROM AUTHOR]

We consider random-access networks with nodes representing transmitter-receiver pairs whose signals interfere with each other depending on their vicinity. Data packets arrive at the nodes over time and form queues. The nodes can be either active or inactive: a node deactivates at unit rate, while it activates at a rate that depends on its queue length, provided none of its neighbors is active. In order to model the effects of user mobility in wireless networks, we analyze dynamic interference graphs where the edges are allowed to appear and disappear over time. We focus on bipartite graphs and study the transition time between the two states where one part of the network is active and the other part is inactive, in the limit as the queue lengths become large. Depending on the speed of the dynamics, we are able to obtain a rough classification of the effects of the dynamics on the transition time. [ABSTRACT FROM AUTHOR]

In this paper, a novel full-duplex overlay cognitive wireless powered communication network (FD-OCWPCN) is proposed where a full-duplex (FD) hybrid-access point (H-AP) supports the full access of all battery-free secondary users (SUs). The H-AP broadcasts wireless power to empower the nearby SUs in the downlink (DL) phase while decoding the information transmitted uplink (UL) phase by the SUs, simultaneously. To overcome the self-interference (SI) phenomenon in FD-OCWPCN, the problem of maximizing the system sum-throughput with optimal UL-DL transmission/reception time and H-AP's transmit power allocation is considered. This problem is non-convex under perfect/imperfect SI cancelation (SIC), so we employ the active interference temperature control and the gradient projection techniques to effectively reduce it into a convex problem. Closed-form expressions for the perfect/imperfect SIC cases are also derived. To assess the performance of the FD-OCWPCN, a comparison with a half-duplex OCWPCN (HD-OCWPCN) is provided. The achievable average sum-throughput for different FD/HD-OCWPCN is compared in the context of the average and peak transmit power at the H-AP, the number of SUs, path loss exponent and fairness metric. The simulation results depict the superiority of the FD-OCWPCN over the HD-OCWPCN for the perfect SIC and the effective imperfect SIC. [ABSTRACT FROM AUTHOR]

The article analyses the problem of measuring and examining the magnitudes of vibration amplitudes at the machining of materials by milling technology. By measuring the vibration parameter on the spindle head of a universal milling machine, the level of the vibration amplitudes magnitude during the milling process was acquired using a piezoelectric vibration sensor. The magnitudes of the vibration amplitudes were recorded during machining when the spindle speed varied between 1200 rpm and 1600 rpm. The measured values were evaluated in the form of FFT spectra, and the information required to design measures to avoid excess vibration amplitudes in such a case was obtained. [ABSTRACT FROM AUTHOR]