We propose a physics-informed learning framework, called Koopman-PINN, to estimate the parameters of the Heston stochastic volatility model with high-frequency price data in financial markets. The method integrates a nonparametric volatility estimation (known as ART-filter in the literature), moment-based parameter initialization, and a neural Koopman operator constrained by the infinitesimal generator of the underlying stochastic differential equation. By incorporating a generator-based loss, the model bridges Koopman theory and neural modeling to handle partially observed coupled stochastic dynamics in a manner consistent with continuous-time evolution. Across diverse parameter combinations reflecting varying market conditions, Koopman-PINN consistently achieves accurate and robust five-parameter recovery, outperforming existing estimators under a minimal set of initialization assumptions.

Radiation source (RS) localization is crucial in electromagnetic environmental monitoring. Existing methods require prior knowledge of the environment and have overlooked the impact of multipath effects. Their positioning accuracy is penalized in practical applications. This paper presents an environment cognition-based variational Bayesian positioning method, which can achieve precise multi-source localization in uncooperative multipath environments using only the measurements of received signal strength (RSS). We begin by leveraging the signal propagation properties to design a model-data integrated unsupervised learning network, which partitions the positioning area according to the transmission states of different multipath signals. To estimate the number of RSs and mitigate the multipath effect, we identify the data samples of multipath RSS and divide them into groups, each corresponding to an RS. Then, a new variational Bayesian positioning algorithm is developed to operate without prior channel information and locate multiple RSs accurately across varying signal propagation models. Simulations show that our positioning method can effectively improve the accuracy of multi-RS localization by 10% in uncooperative multipath environments, compared to the state-of-the-art RSS-based localization techniques.

Drones are expected to be promising aerial platforms in air-to-ground (A2G) integrated communication networks, where the A2G propagation channel is fundamental for reliable communication links. This paper proposes a hybrid parameter generation framework combining the deterministic and statistical methods for a cluster-based A2G channel model. In this framework, the map-based deterministic method is used to generate delay and angle parameters, which can achieve great scenario consistency. However, it is difficult for users to provide precise material information of scatterers, which would cause deviation of power parameters. To tackle this issue, a measurement-driven power generation method is proposed. Firstly, a bandwidth-dependent clustering method is developed to group the rays into clusters. Then, the cluster power is generated by measurement-driven statistical models with a power-decomposition idea. It decomposes the power parameter into several parts that are less dependent on the scenario. Moreover, it can avoid massive measurement campaigns and is more robust when applied in unmeasured scenarios. Finally, a new channel measurement campaign in a street canyon scenario is performed for validations. The proposed method is also compared with a ray-tracing (RT) method and the 3rd Generation Partnership Project (3GPP) channel model. It is shown that the proposed framework and power generation method are great alternatives for accurate and robust modeling requirements under specific A2G communication scenarios.

Distributed multiple-input multiple-output (MIMO) radar networks operating at millimeter-wave frequencies enable high-resolution microwave imaging but face fundamental limitations in antenna array synthesis. Sparse virtual apertures reduce hardware complexity yet introduce severe grating lobes and sidelobe artifacts, which degrade image fidelity via aliasing in the electromagnetic (EM) spatial frequency domain. To address these antenna array synthesis challenges, we propose a noisy tensor completion framework exploiting joint low-rank and sparsity constraints inherent in radar scattering. Our method reorganizes radar echoes into high-dimensional tensors with dispersed missing elements, explicitly modeling the sparse array sampling process. A key innovation is an adaptive singular-value reweighting scheme that preserves dominant EM scattering components while suppressing noise-corrupted interference. The resulting optimization is solved via an alternating direction method of multipliers (ADMM) algorithm. Extensive EM simulations and experimental validation using a prototype W-band (77 GHz) distributed MIMO radar system demonstrate superior artifact suppression and target reconstruction over state-of-the-art methods. This establishes a robust imaging solution for sparse-aperture systems by directly addressing antenna array pattern limitations through tensor-based aperture synthesis.

Spectrum mapping (SM) visualizes spectrum information across a geographical area, constructing radio environment maps (REMs), which serve as a foundation for spectrum monitoring, management, and security. Most existing SM schemes rely on spatially distributed sensors or vehicle-mounted equipment, and assume prior environmental knowledge, limiting their applicability in dynamic or unknown 3D environments. In this paper, we propose a Bayesian learning-based three-dimensional (3D) SM framework that enables accurate REM construction through adaptive UAV sampling in complex and unknown environments. First, a mutual-information-driven UAV path planner is designed by integrating an enhanced sampling-based optimization scheme, enabling efficient data collection according to the maximum mutual information criterion and recent sensing data. Second, a semi-deterministic channel dictionary, refined with sampled field data, is established to model the correlation between observed spectrum values and environmental features. Based on this dictionary, a Bayesian learning-based recovery algorithm reconstructs the spectrum distribution at unsampled positions, producing the corresponding 3D REM. Experimental results on open simulated and measured datasets demonstrate that the proposed framework reduces the mean absolute error by over 60% compared with CS-based methods and by 35% with data-driven interpolation. It also improves sampling efficiency by up to 70% for a given recovery accuracy, highlighting the effectiveness in unknown 3D environments.

The remote identification (Remote ID) broadcast capability allows unmanned aerial vehicles (UAVs) to exchange messages, which is a pivotal technology for inter-UAV communications. Although this capability enhances the operational visibility, low delay in Remote ID-based communications is critical for ensuring the efficiency and timeliness of multi-UAV operations in dynamic environments. To address this challenge, we first establish delay models for Remote ID communications by considering packet reception and collisions across both BLE 4 and Wi-Fi protocols. Building upon these models, we formulate an optimization problem to minimize the long-term communication delay through adaptive protocol selection. Since the delay performance varies with the UAV density, we propose an adaptive BLE/Wi-Fi switching algorithm based on the multi-agent deep Q-network approach. Experimental results demonstrate that in dynamic-density scenarios, our strategy achieves 32.1% and 37.7% lower latency compared to static BLE 4 and Wi-Fi modes respectively.

Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments.

Specific emitter identification (SEI), known as radio frequency fingerprint (RFF) identification, is one of the key techniques to provide effective protection for the low-altitude security. However, most existing SEI methods cannot achieve satisfactory identification performance in low signal-to-noise ratio (SNR) environments. By fusing background information of the environment, this paper presents a new deep learning-based SEI method that can accurately identify emitters in the environments with severe noises. We first construct a dual convolutional neural network (DCNN) and a U-shaped Convolutional Network (UNet) to extract the RFFs and background information features, respectively. Then, a background-fingerprint attention fusion network (BFAFN) is designed to fuse the background information with RFF features. Using this network, we can obtain detailed emitters information through the fused signals, improving the identification accuracy. Experimental results show that our proposed SEI method outperforms other methods in performance on both open-source and collected unmanned aerial vehicles (UAVs) datasets, with an improvement in identification accuracy of 2% to 5%.

This paper proposes an early detection method of the pole blockage for millimeter-wave (mmWave) railway communications. In this method, the (line-of-sight) LoS cluster power gain is used for early detection instead of the total received power to reduce the effect of the other scatterers in dynamic railway scenarios. Moreover, the power difference calculation and triggering value searching steps are introduced to handle issues such as time-varying mean power gain, accidental power losses, and so on. Simulation and measurement cases are performed in railway communication scenarios at 60 GHz for validation. The results show that the proposed method can early detect the blockage area before a significant power attenuation occurs. The performance of the proposed detection method is also compared with that of the existing method.

Traditional satellite-to-vessel (S2V) models struggle to handle wave movement and random obstacles in complex maritime environments. These models are designed for urban scenarios and fail to account for the unique dynamics of sea surface fluctuations, obstacle distribution, and satellite posture, limiting their applicability in maritime communications. We propose a novel empirical model that integrates multiple maritime environmental factors to overcome these challenges. A realistic virtual sea surface scenario is constructed by extracting oceanic geometric features and statistically modelling waves and obstacles. A large volume of line-of-sight (LoS) data is obtained through ray tracing (RT) simulations. A multi-input graph convolutional network (GCN) is trained for parameter estimation, and a new complexity parameter is introduced to characterize environmental impact. Simulation results demonstrate that the proposed method achieves higher prediction accuracy in maritime communication scenarios and significantly outperforms baseline models. Even with simplified inputs, the model performs well in urban scenarios, demonstrating strong generalization and adaptability.

Constructing three-dimensional (3D) radio environment maps (REMs) has emerged as a promising solution to visualize the spectrum information over the geographical map. In this paper, we propose a novel online path planning method for unmanned aerial vehicle (UAV)-based 3D spectrum mapping in unknown environments. The UAV can effectively collect spectrum data along dynamically planned paths while adhering to budget constraints. We formulate the path planning problem by a surrogate objective that maximizes the information gain along the sampling path. Specifically, a Gaussian process (GP) is adopted to estimate the spatial distribution of received signal strength (RSS) based on observations. A goal location decision algorithm based on the negative integrated posterior variance (NIPV) criterion is developed, which identifies high-value locations by maximizing the reduction in uncertainty. Besides, an uncertainty-aware local path planner is introduced to optimize sampling paths during flight. Simulation results demonstrate that it achieves at least a 66.49% improvement in REM construction accuracy and 42.74% reduction in mapping uncertainty compared to traditional methods.

Due to the increasing demand for the accuracy of spectrum maps, fusing spectrum maps has gained attention as an effective method to improve the exactitude of spectrum map construction. However, most of the existing spectrum map fusion methods overlook the over-fitting problem and the correlation of spectral data in the fusion process, so the performance can hardly meet expectations. In this paper, a spectrum map fusion method based on differential ridge regression is proposed, which can construct accurate spectrum maps in the electromagnetic environment with strong-correlation data with high accuracy. First, we construct a spectrum map fusion model by exploiting the propagation characteristics of the spectrum signal. According to the path loss model, the differential ridge regression regularization term is designed to handle the correlation of spectral data and suppress anomalies from spectrum receivers. Finally, we construct a convex optimization problem for spectrum map fusion and obtain the lower bound of the problem by developing Lagrange duality. This method can ensure the convergence of the spectrum map fusion problem and accelerate the convergence speed under low complexity. Simulation results show that the proposed fusion method can effectively improve the accuracy of spectrum map construction compared with the state-of-the-art.

Radio environment maps (REMs) have been used to visualize the information of invisible electromagnetic spectrum. Although in the past there have been many research activities dealing with the reconstruction of static REMs, they did not consider the time variation of the dynamic spectrum operational environment. In this article, we present a novel time-variant REM reconstruction methodology based on sparsely distributed sensors which jointly considers sensor layout optimization, propagation model improvement, and missing spectrum data recovery. First, a low complexity and computationally efficient method is proposed to improve the sampling efficiency. The proposed method jointly employs the gradient descent method and an upgraded greedy matching algorithm to optimize the sensor positions even when large-scale scenarios are considered. Then, by using the sampled spectrum data obtained from these sensors, the accuracy of commonly employed propagation models is improved and subsequently used to construct a channel dictionary for such time-varying environments. By exploring the heterogeneity of dynamic spectrum operational environments, an improved optimal reconstruction method is designed to recover the spectrum data using their spatial-temporal correlation. By considering a typical university campus environment as a case study, simulation and measurement data are obtained to reconstruct the time-variant REM. Through the simulation data, the reconstruction performance results are compared with those obtained from other state-of-the-art methods showing that the proposed methodology outperforms the others with respect to the sampling scheme and missing rate. Additionally, field measurement results have demonstrated that the proposed approach can effectively reconstruct time-variant REMs under dynamic scenarios.

Unmanned aerial vehicle (UAV) air-to-air (A2A) communications are emerging as a vital component of future low-attitude wireless networks. This paper introduces a novel A2A channel model and evaluates its statistical properties through analysis and experimental validation. The proposed model adopts a quasi-deterministic approach that incorporates rooftop specular reflection (RSR), distinguishing it from traditional ground reflection. Airframe occlusion (AO) is represented using a diffraction-based segmented function to accurately characterize its impact on the line-of-sight path. Key statistical indicators are derived based on the proposed model, and numerical results show that the presence of RSR reduces both the level crossing rate and average fade duration. Enhanced Rician factor improves spatial-temporal correlation, while higher transmission power and lower UAV mobility reduce outage probability. Compared to standardized models, the proposed model demonstrates improved capability in capturing the effects of RSR and AO while maintaining compatibility. Additionally, an A2A channel measurement platform leveraging the high autocorrelation properties of Zadoff-Chu sequences is developed to extract channel impulse response. Experimental measurements of channel capacity, outage probability, and root mean square delay spread closely align with simulated results, validating the model’s accuracy and reliability.

Large-scale multiple-input-multiple-output (MIMO) holds great promise for the fifth-generation (5G) and future communication systems. For near-field scenarios, the spherical wavefront model is commonly utilized to depict the propagation characteristics of large-scale MIMO communication channels. However, employing this modeling method necessitates the computation of angle and distance parameters for each antenna element, resulting in challenges regarding computational complexity. To solve this problem, we introduce a subarray decomposition scheme with the purpose of dividing the whole large-scale antenna array into several smaller subarrays. This scheme is implemented in the near-field channel modeling for large-scale MIMO communications between the base station (BS) and mobile receiver (MR). Essential channel propagation statistics, such as spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (CFs), and channel capacities, are derived and discussed. A comprehensive analysis is conducted to investigate the influences of the height of the BS, motion characteristics of the MR, and antenna configurations on the channel statistics. The proposed channel model criterions, such as the modeling precision and computational complexity, are also theoretically compared. Numerical results demonstrate the effectiveness of the presented communication model in obtaining a good tradeoff between modeling precision and computational complexity.

For autonomous aerial vehicles (AAV)-assisted integrated sensing and communication (ISAC) systems, a semantic-based channel state information (CSI) feedback scheme is proposed in this article. Unlike traditional full CSI feedback, the proposed scheme minimizes feedback burden by utilizing predefined semantic databases at both the transmitter and receiver. First, a deep-learning-based clustering method is developed to construct the semantic database from measured CSI samples. Then, an incremental clustering-based identification method is proposed, enabling dynamic updates and adjustments to semantic databases as new CSI is continuously acquired. Finally, the proposed CSI feedback scheme is validated through scenario identification, and extensive channel measurements are conducted in three typical campus scenarios: 1) playground; 2) lake; and 3) buildings. The results show that the accuracy of the semantic feedback-based scenario identification reaches 97.5%, which is 0.6% higher than the accuracy of the full-CSI feedback-based scenario identification. Specifically, the CSI is fed back through semantic database labels, requiring only a few bytes. This significantly reduces feedback burden while maintaining high accuracy of ISAC tasks. Furthermore, the proposed feedback scheme can also be extended to other AAV-assisted applications, such as the Internet of Things and emergency response.

Due to the high flexibility and versatility, uncrewed aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue. However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39%, 12.74%, and 16.6% than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.

In the increasingly critical field of aerial communication, unmanned aerial vehicles (UAVs) have gained significant attention as prominent representatives, and accurate air-to-air (A2A) channel modeling plays a pivotal role in the design and evaluation of reliable communication systems. This paper presents a A2A channel model for UAV communications. It introduces a quasi-deterministic approach to address limitations in existing modeling frameworks. The proposed model uses a truncated ellipsoid to capture the distribution of scatterers in A2A scenarios and, for the first time, incorporates rooftop specular reflection (RSR). Power correction factors, based on the UAV’s airframe structure, position, and posture, are introduced to provide a comprehensive and realistic depiction of the A2A communication channel. The performance of proposed model is assessed by simulating key statistical channel characteristics and comparing with other alternatives. The simulations illustrate how channel behavior is influenced by factors such as flight level, flight trajectory, and UAV posture. The results show that RSR leads to the channel hardening effect, while airframe occlusion causes the received signal power to vary gradually with changes in UAV’s position and posture. The validity of the model is confirmed through comparison with measurement data and ray-tracing results, proving its accuracy and practical application.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a decisive role in the design, optimization, and evaluation of communication systems between UAV and ground terminal. This paper proposes a three-dimensional (3D) model for U2G communication channels, enhanced with ultra-wideband (UWB) features and frequency non-stationarity. This model integrates large-scale and small-scale fading components, introducing bandwidth-dependent path numbers and the UAV posture matrix for realistic scenario representation. It encompasses specific UWB U2G channel phenomena such as the channel hardening, UAV 3D movements, and posture variation effect. The channel parameters, including spatial large-scale parameters (LSPs), bandwidth-correlated path numbers, delay-posture-correlated path power, and frequency-correlated path phase, are generated to capture channel non-stationary characteristics across time and frequency domains. Employing ray-tracing (RT) for the path number and optimization methods for the path delay, the proposed model ensures reliable parameter evolution. The proposed model is assessed through key statistical properties, including space-time-frequency correlation functions, power delay profile, root-mean-square delay spread, Doppler power spectrum density, and the energy variance. It is demonstrated that both posture and bandwidth variations have crucial effects on channel characteristics. The validity and practicability of this research is demonstrated by comparing the simulated outcomes with the measurement data.

The optimization of line-of-sight (LoS) probability is essential for enhancing 6G unmanned aerial vehicle (UAV)-to-ship (U2S) communication in maritime scenarios, where the communication channel is affected by dynamic sea waves (SW) and six-dimensional (6D) transceiver positioning. Traditional models have been found inadequate in addressing these variables. In this paper, a novel LoS probability model is introduced, integrating dynamic SW and 6D positioning of ships and UAVs. The effects of SW height, number, and period on LoS blockage are quantified, providing a tailored approach for maritime environments. A dual-channel convolutional neural network (DCCN) coupled with spider monkey optimization (SMO) is applied to refine the complex relationship between environmental factors and LoS probability. The model, trained with ray tracing (RT) data, is shown to significantly outperform standard models like 3GPP and 5GCM, offering new insights into U2S communication in maritime scenarios.

Metaverse, which enables the combination of the virtual and the physical worlds, requires mobile networks with high-capacity and reliable connectivity. Radio maps (RMs) can offer the knowledge of the wireless environments to improve the connectivity, by charactering the spatial distribution of received signal strength (RSS) throughout physical spaces. This paper investigates a collaborative RM reconstruction scheme, where client unmanned aerial vehicles (UAVs) collect RSS samples measured by mobile users for local training, while a server UAV performs model aggregation to optimize the global model. Unfortunately, RSS samples measured in practice can be sparse, non-uniformly distributed and non-independent and identically distributed (non-iid), such that reconstructing a complete RM is intractable. Therefore, we propose a novel RM reconstruction scheme based on federated learning (FL) with generative adversarial network (GAN), where GAN is exploited to generate a RM with sparsely and non-uniformly distributed RSS data. In order to tackle with non-iid RSS data, the FL is integrated with an adaptive client UAV selection strategy with model similarity evaluation, as well as a model weight assignment method with earth mover’s distance evaluation for model aggregation. Simulation results reveal that benefiting from the aforementioned design, the proposed scheme can significantly enhance the reconstruction accuracy and convergence speed compared to the conventional algorithms.

Unmanned aerial vehicle (UAV) communication is a promising part of next generation mobile communication network. Path loss (PL) is vital for the communication quality, where vegetation penetration loss (VPL) is a critical factor under UAV-to-ground (U2G) scenarios. In this paper, we propose a VPL model considering the foliage density and moisture content, and a measurement system is developed and implemented to acquire a large amount of channel data for fitting model parameters. Measurement results show that the proposed VPL model is more accurate and universal compared with typical models such as COST-235 and FITU-R. The proposed model and measurement results can provide a valuable reference for U2G communication in vegetation scenarios.

Accurately measuring the radiation directional pattern of base stations is crucial for performance evaluation of antenna radiation in fifth generation (5G) wireless communications. However, existing methods construct antenna directional pattern typically rely on the E-plane and H-plane data, limiting their applicability. This paper presents a new model-driven three-dimensional (3D) radiation directional pattern construction method based on data correction for 5G base stations. This method utilizes an unmanned aerial vehicle (UAV) to collect radiation characteristic data of the base station. We first propose a position correction method based on the 3D space transformation, and refine the radiation characteristic data accuracy according to a flat earth two-ray (FE2R) model. A improved close-in (CI) model driven inverse distance weighting (IDW) algorithm is then designed to accurately sense and depict the directional pattern of the base station. Experimental results demonstrate that the proposed radiation pattern construction method can achieve highly accurate radiation pattern of base stations.

Unmanned aerial vehicle (UAVs) are a key platform in the sixth generation (6G) communication networks, where integrated sensing and communication (ISAC) is also a promising technology that requires real-time channel estimation. This paper proposes a robust and efficient angle-of-arrival (AOA) estimation method based on a field-trained neural network (NN) for low-latency UAV ISAC applications. In this method, the NN is pre-trained quickly in the field with each receiving antenna element's channel state information (CSI) and the transceivers' locations. We extract the channel multi-paths from the CSI and calculate the path phases as the training data set in real time. Then the pre-trained NN is used for high-efficient AoA estimation in real time. A real-time UAV channel sounder is utilized to verify the proposed method. The measurement results show that the proposed field-trained estimation method is faster and more robust compared with the traditional method and fixed-trained NN. The proposed angle estimation method is valuable for UAV channel estimation and low-latency UAV ISAC applications.

This paper presents a quantitative analysis of the nonlinearities of the positive predictive value (PPV) and its effect in evaluating two-class pattern classification models with imbalanced datasets. The analysis is made through an expression of the PPV as a function of two other classification ratios that are invariant to the data imbalance —the true positive rate (TPR) and false positive rate (FPR), and [Formula: see text] — the imbalance ratio (IR) of the dataset such that PPV [Formula: see text]TPR/([Formula: see text]TPR[Formula: see text]FPR). The curvatures of PPV in the three-dimensional TPR–FPR–[Formula: see text] space are studied using the Hessian matrix, from which a saddle-shaped 3D surface in the space is revealed. This paper explores the nonlinear behaviors of PPV around the critical points, identified at FPR [Formula: see text]TPR on the saddle surface, along with its scaling and sensitivity issues as performance measurements in model evaluation. The effect of the nonlinearities of PPV for the F1 and MCC metrics on imbalanced datasets is also studied. It is warned through the results of this study that the evaluations of classification models could be misleading if without an awareness and understanding of the nonlinearities associated with the PPV and its relevant metrics on imbalanced datasets.

As an aerial service platform for Internet of Things (IoT), unmanned aerial vehicle (UAV) can provide integrated sensing, computing and communication (ISCAC) services for the IoT nodes. In this paper, a UAV-enabled ISCAC system is proposed for IoT to meet the evolving requirements of emerging services in 6G networks. This system has three functions: sensing user equipments (UEs) for acquiring radar sensing information, executing computing tasks, and offloading incomplete tasks to the access point (AP) for further processing. Through jointly optimizing UAV CPU frequency, UAV radar sensing power, transmit power of UEs, and UAV trajectory, the weighted total energy consumption of both the UAV and the UEs can be minimized. We present a three-layer iterative optimization algorithm to tackle the original non-convex optimization problem. Finally, the effectiveness of the algorithm and its superiority in energy consumption compared to other benchmark schemes are verified through simulation results.

A realistic channel model is vital for designing, optimizing, and evaluating unmanned aerial vehicle (UAV)-to-ground (U2G) communication systems. This paper presents a comprehensive U2G ultra-wideband (UWB) channel model by considering the large-scale fading (LSF), including path loss (PL), shadow fading (SF), and airframe shadowing (AS). The effects of carrier frequency, bandwidth, building distribution, and UAV airframe structure on the LSF are fully studied. Different from the traditional bandwidth-independent PL calculation method, an extended closed-form expression for calculating PL is proposed to capture the new characteristics of ultra-wide bandwidth. The SF part is statistically described by a parametric lognormal model, and the built-up scenario-dependent parameters are predicted by a hybrid method with the ray-tracing and virtual scenario technologies. In addition, the AS part with respect to the airframe structure and posture variation is derived. It consists of the direct and reflected path components which are obtained by the deterministic and statistical approaches, respectively. The numerical simulations show that bandwidth, building distribution, and airframe structure have great influence on the LSF characteristics. For example, the fluctuation of received power can reach 30 dB due to the AS. The proposed model also shows its effectiveness and reliability by comparing with the RT and measured data, as well as the good compatibility with standardized models for some typical scenarios.

Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fully-connected architectures, respectively.

The radio environment map (REM) visually displays the spectrum information over the geographical map and plays a significant role in monitoring, management, and security of spectrum resources. In this paper, we present an efficient 3D REM construction scheme based on the sparse Bayesian learning (SBL), which aims to recover the accurate REM with limited and optimized sampling data. In order to reduce the number of sampling sensors, an efficient sparse sampling method for unknown scenarios is proposed. For the given construction accuracy and the priority of each location, the quantity and sampling locations can be jointly optimized. With the sparse sampled data, by mining the sparsity of the spectrum situation and channel propagation characteristics, a SBL-based spectrum data hierarchical recovery algorithm is developed to estimate the missing data of unsampled locations. Finally, the simulated three-dimensional (3D) REM data in the campus scenario are used to verify the proposed methods as well as to compare with the state-of-the-art. We also analyze the recovery performance and the impact of different parameters on the constructed REMs. Numerical results demonstrate that the proposed scheme can ensure the construction accuracy and improve the computational efficiency under the low sampling rate.

Safety concerns surrounding autonomous vehicles (AVs) present significant barriers to their widespread adoption. In AVs, it is necessary to make speedy decisions to safely roam through complex dynamic environments. Interestingly, predicting the future environment can aid in making these early decisions from the partially observed data. This proactive approach is becoming increasingly vital as the number of vehicles on the roads continues to rise, necessitating advanced development strategies for AVs. In this scenario, simulations based on Digital Twin (DT) are proving to be effective in necessary computation during the development and inference phase of the AVs. In fact, the DT system can play a crucial role in making early decisions. Thus, in this research, we propose a Generative Adversarial Network (GAN) enhanced frame prediction system for single and multi-time ahead image forecasting to aid in environment prediction and early decision. We demonstrate the relative motion of entities within the frames and evaluate our system's efficacy through qualitative and quantitative analyses. As This GAN-enhanced system---inside a DT---can predict a few frames into the future and check for anomalies, this can help the AVs make swift decisions. Moreover, by harnessing this capability to create diverse synthetic scenarios, we can enhance the development of the DT system, thus unlocking a multitude of opportunities for training various models for autonomous vehicles (AVs).

As one of the most promising technologies to fulfill the vision for the sixth-generation (6G) networks, holographic multiple-input multiple-output (MIMO) is a significant technology under research. In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for holographic MIMO communications that includes mutual coupling (MC) is proposed. The holographic MIMO is modeled as an antenna array in compact area with limited element spacing. With comprehensive considerations of the factors of scatterers distribution, geometry relationships, and coupling coefficients, the channel impulse response (CIR) and statistical properties of the proposed channel model including the space-time correlation function (STCF), level crossing rate (LCR), average fade duration (AFD), and instantaneous channel capacity are investigated. Compared to the modified method of the equal area (MMEA) based simulation model, the simulation results demonstrate that the statistical properties of our proposed model confirms the theoretical results well and holographic MIMO has the potential to increase channel capacity.

This paper proposes a general unmanned aerial vehicle (UAV)-to-ground (U2G) channel model. The proposed model is consistent with real scenarios by considering the impacts of flight altitude and UAV posture on channel gain. Machine learning and ray tracing (RT) techniques are employed to improve the generation method of altitude-dependent parameters, i.e., path loss (PL) and shadow fading (SF). In addition, posture-related fuselage shadowing coefficient (FSC) is introduced to modify the channel gain, and three-dimensional (3D) geometry modeling of the fuselage is conducted to calculate the FSC. Numerical simulation results show that the flight altitude and UAV posture have obvious effects on channel gain. The proposed model with modified channel gain can effectively describe the PL, SF, and received power under fuselage shadowing. The validity and advantage of the improved channel gain are verified by comparing the simulation results with the measured ones.

Abstract Spectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approaches only take static SEMs into account and cannot forecast time‐domain changes and trends of SEMs in dynamic scenes. In this paper, a brand‐new temporal SEM prediction method for the high dynamic spectrum environment is proposed. This method is based on knowledge of radiation source and the optical flow driven by propagation channel models. First, a novel radiation source localization strategy is designed to obtain the radiation source movement information. Then, the optical flow field of the available SEMs is combined with the information regarding radiation source movement. In order to forecast future SEMs, a propagation model driven reconstruction technique is developed. Simulation findings demonstrate how well the suggested strategy is tailored to capture the spatiotemporal correlation of SEMs. This technique performs better than the state‐of‐the‐art in terms of single‐ and multiple‐step SEM predictions.

Identifying the significant, or dominant, features is important to reveal the cause-and-effect relations in many pattern recognition applications, such as medical diagnosis, gene analysis, cyber security, finance and insurance fraud detection, etc. Samples that are sparsely populated and binary-valued in highly imbalanced datasets pose a challenge to the identification of these features. This paper explores an approach based on the confusion matrix measurement of the feature values with respect to their potential classification outcomes. The approach is able to compute the Discriminative Significances of the features and rank the features unbiasedly with respect to the imbalance ratios of the datasets. Experiment results on real-world and experimental datasets show that the approach made consistent evaluations of the features and identified the most significant ones accordingly on the sparse and binary-valued samples of the class-imbalanced datasets.

Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time variant due to fast changeable altitudes of unmanned aerial vehicles and scattering environments. Considering the A2G channels under urban scenarios, this article presents three novel stochastic probability models for the Line-of-Sight (LoS) path, ground specular (GS) path, and building scattering (BS) path, respectively. By analyzing the geometric stochastic information of 3-D scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of the Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and non-LoS path probability models show good agreement with the ray-tracing (RT) simulation method.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role in reliable communications between UAV and ground terminal. This paper proposes a three-dimensional (3D) non-stationary hybrid model including large-scale and small-scale fading for U2G multiple-input-multiple-output (MIMO) channels. Distinctive channel characteristics under U2G scenarios, i.e., 3D trajectory and posture of UAV, fuselage scattering effect (FSE), and posture variation fading (PVF) are incorporated into the proposed model. The channel parameters, i.e., path loss (PL), shadow fading (SF), path delay, and path angle, are generated incorporating machine learning (ML) and ray tracing (RT) techniques to capture the structure-related characteristics. In order to guarantee the physical continuity of channel parameters such as Doppler phase and path power, the time evolution methods of inter- and intra- stationary intervals are proposed. Key statistical properties, including temporal auto-correction function (ACF), power delay profile (PDP), level crossing rate (LCR), average fading duration (AFD), and stationary interval (SI), are analyzed with the impact of the change of fuselage and posture variation. It is demonstrated that both posture variation and fuselage scattering have crucial effects on channel characteristics. The validity and practicability of the proposed model are verified by comparing the simulation results with the measured ones.

Unmanned aerial vehicle (UAV) is considered as an important component for future communication networks. In this paper, an air-to-ground (A2G) channel sounder is designed and implemented for UAV communication channel measurement and characterization. The channel impulse response (CIR) extraction is implemented on a field programmable gate array (FPGA) to improve extraction efficiency. Based on the channel characteristics under measured (or baseline) scenarios, a transfer learning neural network (TNN) framework is also proposed to predict the channel characteristics of other unmeasured (or transferred) scenarios. In the proposed framework, the baseline matrices of neural network parameters are obtained from the measurement data of baseline scenarios. The ray tracing (RT) simulation data is only used to obtain the extrapolation matrices where we utilize imperfect digital map and do not require a highly accurate RT simulation. Then the neural network driven by the baseline and extrapolation matrices is used to predict the channel characteristics of transferred scenarios. To verify the proposed prediction method, the channel characteristics including path loss, K-factor, and root mean square delay spread of a near-urban scenario are firstly measured. Then, the corresponding channel characteristics of a transferred dense-urban scenario are predicted by the proposed TNN method and validated by the measurement data. It is shown that the predicted channel characteristics are well consistent with the measured ones.

In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.

Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3-D) scattering space, 3-D velocity, 3-D antenna array, and especially 3-D rotations, a machine learning (ML)-integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, an ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the backpropagation-based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF), are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3-D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.

Line-of-sight (LoS) probability prediction is critical to the performance optimization of wireless communication systems. However, it is challenging to predict the LoS probability of air-to-ground (A2G) communication scenarios, because the altitude of unmanned aerial vehicles (UAVs) or other aircraft varies from dozens of meters to several kilometers. This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios. Before estimating the model parameters, we design a K -nearest neighbor (KNN) based strategy to classify LoS and non-LoS (NLoS) paths. Then, a two-layer back propagation neural network (BPNN) based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude. Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing (RT) data, the measurement data, and the results obtained using the standard models. Our model can also provide wider applicable altitudes than other LoS probability models, and thus can be applied to different altitudes under various A2G scenarios.

A general empirical path loss (PL) model for air-to-ground (A2G) millimeter-wave (mmWave) channels is proposed in this paper. Different from existing PL models, the new model takes the height factor of unmanned aerial vehicles (UAVs) into account, and divides the propagation conditions into three cases (i.e., line-of-sight, reflection, and diffraction). A map-based deterministic PL prediction algorithm based on the ray-tracing (RT) technique is developed, and is used to generate numerous PL data for different cases. By fitting and analyzing the PL data under different scenarios and UAV heights, altitude-dependent model parameters are provided. Simulation results show that the proposed model can be effectively used to predict PL values for both low- and high-altitude cases. The prediction results of the proposed model better match the RT-based calculation results than those of the Third Generation Partnership Project (3GPP) model and the close-in model. The standard deviation of the PL is also much smaller. Moreover, the new model is flexible and can be extended to other A2G scenarios (not included in this paper) by adjusting the parameters according to the simulation or measurement data.

Channel emulator is an important tool to evaluate communication system performance at the physical link, and network levels. In this paper, a new discrete 3D non-stationary geometry-based stochastic model (GBSM) for UAV to vehicle (U2V) channels is proposed, which considers 3D scattering space, 3D trajectory, and 3D antenna array. And a tailed channel emulator is developed on a field programmable gate array (FPGA) platform. All channel parameters, i.e., the power, delay, and phase are calculated by FPGA hardware for the first time instead of software or pre-storage method. Meanwhile, a Greedy CORDIC-based exponential calculation method for generating massive complex sinusoids is designed and implemented. The latency is reduced by 50% than traditional CORDIC method. By further utilizing the compact architecture with time division scheme, the hardware resource is significantly reduced from 16.51% to 7.55% for 16-bit data width. Meanwhile, the fixed-point output statistical properties are also derived for quantitatively validation. Finally, the U2V channel under the campus scenario is reproduced by the proposed emulator. The generated results demonstrate that the statistical properties are consistent well with the theoretical and ray tracing ones, which verifies the correctness of both proposed channel model and emulator.

In recent years, the application potential of visible light communication (VLC) technology as an alternative and supplement to radio frequency (RF) technology has attracted people's attention. The study of the underlying VLC channel is the basis for designing the VLC communication system. In this paper, a new non-stationary geometric street corner model is proposed for vehicular VLC (VVLC) multiple-input multiple-output (MIMO) channel. The proposed model takes into account changes in vehicle speed and direction. The category of scatterers includes fixed scatterers and mobile scatterers (MS). Based on the proposed model, we derive the channel impulse response (CIR) and explore the statistical characteristics of the VVLC channel. The channel gain and root mean square (RMS) delay spread of the VVLC channel are studied. In addition, the influence of velocity change on the statistical characteristics of the model is also investigated. The proposed channel model can guide future vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) optical communication system design.

Based on the geometry and ray tracing (RT) theory, a millimeter wave (mmWave) channel model and parameter computation method for unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communications are proposed in this paper. In order to speed up the parameter calculation, a reconstruction process of scene database on the original digital map is developed. Moreover, the effects of reconstruction accuracy on the channel parameter and characteristic are analyzed by extensive simulations at 28 GHz under the campus scene. The simulation and analysis results show that the simplified database can save up to 50% time consumption. However, the difference of statistical properties is slight in the campus scenario.

In this paper, a practical model for non-stationary Vehicle-to-Vehicle (V2V) multiple-input multiple-output (MIMO) channels is proposed. The new model considers more accurate output phase of Doppler frequency and is simplified by the Taylor series expansions. It is also suitable for generating the V2V channel coefficient with arbitrary velocities and trajectories of the mobile transmitter (MT) and mobile receiver (MR). Meanwhile, the channel parameters of path delay and power are investigated and analyzed. The closed-form expressions of statistical properties, i.e., temporal autocorrelation function (TACF) and spatial cross-correlation function (SCCF) are also derived with the angle of arrival (AoA) and angle of departure (AoD) obeying the Von Mises (VM) distribution. In addition, the good agreements between the theoretical, simulated and measured results validate the correctness and usefulness of the proposed model.

Predicting the severity of patient's condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient's data. It is a challenging problem to evaluate the patient's data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.

Class imbalance is a problem of crucial challenge in many real-world machine learning applications. Traditional machine learning algorithms are likely to produce good accuracy scores on such datasets due to an obvious bias towards the majority class. Thus, accuracy as a measure of performance for algorithms working on imbalanced data is not very clearly defined since the classifier has poor predictive accuracy over the minority class. While previous work has used several resampling techniques to aid in improving the predictive accuracy of the minority class, in this study, we explore and compare the effectiveness of the Synthetic Minority Oversampling and Random Oversampling techniques over multiple learning algorithms and resampling ratios for eight different performance measures against two datasets from diverse domains such as medicine and engineering. The results of this study show that the effectiveness of these resampling techniques is a multivariate function relative to both the learning algorithms and the resampling ratios, as well as the coherent characteristics of datasets. The choice of performance measures to evaluate models built using these resampling techniques also vary, thus giving us more relevant information useful for future research and applications.

In this paper, a new WINNER+-based 3-D non-stationary geometry-based stochastic model (GBSM) for multiple-input multiple-output channels is proposed, as well as extended evolving algorithms of time-variant channel parameters. Meanwhile, important statistical properties of the channel model, i.e., time-variant autocorrelation function, time-variant cross-correlation function, and time-variant Doppler power spectrum density are derived and analyzed. Moreover, we propose an efficient hardware implementation method, namely, sum-of-frequency-modulation (SoFM) method, to generate non-stationary channel coefficients. By utilizing compact hardware architecture with SoFM modules, the proposed 3-D non-stationary GBSM is realized on a field-programmable gate array hardware platform. Simulations and hardware measurement results demonstrate that our proposed channel simulator and emulator can get more accurate and realistic Doppler frequency than those of the existing models. In addition, hardware measurements of statistical properties are also well consistent with the corresponding theoretical ones, which verify the correctness of both the hardware emulation scheme and theoretical derivations.

In this paper, we propose a new simulator for nonstationary Rice fading channels under nonisotropic scattering scenarios, as well as the improved computation method of simulation parameters. The new simulator can also be applied on generating Rayleigh fading channels by adjusting parameters. The proposed simulator takes into account the smooth transition of fading phases between the adjacent channel states. The time‐variant statistical properties of the proposed simulator, that is, the probability density functions (PDFs) of envelope and phase, autocorrelation function (ACF), and Doppler power spectrum density (DPSD), are also analyzed and derived. Simulation results have demonstrated that our proposed simulator provides good approximation on the statistical properties with the corresponding theoretical ones, which indicates its usefulness for the performance evaluation and validation of the wireless communication systems under nonstationary and nonisotropic scenarios.

The state of the art of technology for near-duplicate image retrieval is mostly based on the Bag-of-Visual-Words model. However, visual words are easy to result in mismatches because of quantization errors of the local features the words represent. In order to improve the precision of visual words matching, contextual descriptors are designed to strengthen their discriminative power and measure the contextual similarity of visual words. This paper presents a new contextual descriptor that measures the contextual similarity of visual words to immediately discard the mismatches and reduce the count of candidate images. The new contextual descriptor encodes the relationships of dominant orientation and spatial position between the referential visual words and their context. Experimental results on benchmark Copydays dataset demonstrate its efficiency and effectiveness for near-duplicate image retrieval.

The Bag-of-Visual-Words model has become a popular model in image retrieval and computer vision. But when the local features of the Interest Points (IPs) are transformed into visual words in this model, the discriminative power of the local features are reduced or compromised. To address this issue, in this paper, we propose a novel contextual descriptor for local features to improve its discriminative power. The proposed contextual descriptors encode the dominant orientation and directional relationships between the reference interest point (IP) and its context. A compact Boolean array is used to represent these contextual descriptors. Our experimental results show that the proposed contextual descriptors are more robust and compact than the existing contextual descriptors, and improve the matching accuracy of visual words, thus make the Bag-of-Visual-Words model become more suitable for image retrieval and computer vision tasks.

A generalized spatial correlation approximation based on pulse sampling in angle domain is proposed. The new scheme can been used to evaluate the spatial correlation of received signals under arbitrary scattering scenarios. Different sampling pulses are compared which shows the raised-Cosine pulse is better than others. Numerical simulation results demonstrate that the approximate method has more high accuracy and lower complexity comparing with other traditional approximate methods.

Cyberspace has increasingly become a medium to express outrage, conduct protests, take revenge, spread opinions, and stir up issues. Many cyber attacks can be linked to current and historic events in the social, political, economic, and cultural (SPEC) dimensions of human conflicts in the physical world. These SPEC factors are often the root cause of many cyber attacks. Understanding the relationships between past and current SPEC events and cyber attacks can help understand and better prepare people for impending cyber attacks. The focus of this paper is to analyse these attacks in social dimensions and build a threat model based on past and current social events. A reasoning technique based on a novel combination of formal concept analysis (FCA) and hierarchical fact-proposition space (FPS) inference is applied to build the model.

We present a metric for quantitatively assessing the quality of Web searches. The Relevance-of-Searching-on-Target index measures how relevant a search result is with respect to the searcher's interest and intention. The measurement is established on the basis of the cognitive characteristics of common user's online Web-browsing behavior and processes. We evaluated the accuracy of the index function with respect to a set of surveys conducted on several groups of our college students. While the index is primarily intended to be used to compare the Web-search results and tell which is more relevant, it can be extended to other applications. For example, it can be used to evaluate the techniques that people apply to improve the Web-search quality (including the quality of search engines), as well as other factors such as the expressiveness of search queries and the effectiveness of result-filtering processes.

Many different methods and techniques have been investigated for the processing and analysis of microarray gene expression profiling datasets. It is noted that the accuracy and reliability of the results are often dependent on the measurement approaches applied, and no single measurement so far is guaranteed to generate a satisfactory result. In this paper, an algorithmic fusion approach is presented for extracting genes that are predictive to clinical outcomes (survival-fatal) of diffuse large B-cell lymphoma on a set of microarray data for gene expression profiling. The approach integrates a set of measurements from different aspects in terms of the discrepancy indications and merit expectations of the gene expression patterns with respect to the clinical outcomes. A combination of statistical and non-statistical criteria, continuous and discrete parameterizations, as well as model-based and modeless evaluations is applied in the approach. By integrating these measurements, a set of genes that are indicative to the clinical outcomes are better captured from the gene expression profiling dataset.

In the highly dynamic and information rich environment of military submarine operations, the Commanding Officer must make split second decisions that could ultimately result in the destruction or survival of Ownship or the accomplishment of its designated mission. Despite extensive training and expertise, the fog of war often shadows real world encounters. Pieces of the tactical puzzle may be scattered, broken, and/or even missing. It is the Commanding Officer's job to meld the fog into a clear tactical picture and intuitively decide upon the optimal course of action to obtain his mission goals and objectives. A mathematically derived optimal solution could be used as a decision making aid for the Commanding Officer to enhance Ownship's performance. A highly innovative hybrid Bayesian/differential game modeling approach is being developed to tackle this problem. The resulting theory and technology will facilitate tactical operations by addressing such practical problems as tradeoff evaluation for course of action and maintaining tactical advantage while avoiding counter detection.

A computerized authentication system should be able to admit new authentic entries continuously while maintain the existing entry records and an uninterrupted system operation. In this paper, we describe a competitive RBF neural network that is able to incrementally construct itself in response to the pattern samples presented to the system. The neural network is thus a suitable choice for authentication system applications. The accretion property of the neural network is made possible by allowing each pattern class (an authentic entry) being modeled in multiple hyper-ellipsoidal distributions, and mapping these distributions to multiple RBF neural units.

Previous researches on nonlinear shape restoration are based on the assumptions that the original shapes and distortions of the images have known formulations under certain conditions. The main contribution of this research is the development of a new restoration algorithm, called Multi-Steps Restoration. The algorithm is based on a linear interpolation theory that is able to detect and restore nonlinear shape distortions in any irregular quadrilateral-shaped patterns. The main idea of the algorithm is the use of two-dimensional spline functions in bicubic, biquadratic, and/or bilinear models to approximate the three-dimensional nonlinear distortion curves. The performance of the approach shown by experiment is promising.

Models and control strategies for dynamic obstacle avoidance in visual guidance of mobile robots are presented. Characteristics that distinguish the visual computation and motion control requirements in dynamic environments from that in static environments are discussed. Objectives of the vision and motion planning are formulated, such as finding a collision-free trajectory that takes account of any possible motions of obstacles in the local environments. Such a trajectory should be consistent with a global goal or plan of the motion and the robot should move at as high a speed as possible, subject to its kinematic constraints. A stochastic motion-control algorithm based on a hidden Markov model is developed. Obstacle motion prediction applies a probabilistic evaluation scheme. Motion planning of the robot implements a trajectory-guided parallel-search strategy in accordance with the obstacle motion prediction models. The approach simplifies the control process of robot motion.>

The author discusses: a tagged feature and class representation of the pattern recognition problem in a dynamic environment; univariate cooperative classifiers that are based on statistical feature evaluation and impose no constraint on the variations of the sets of classes and features; and inductive learning procedures that are used to create a class-feature space adaptive to the variations of the dynamic environment. The univariate classifier and the cooperative classifier apply a classify-by-rejection approach to a candidate class set. The classification is based on the individual evaluation of the features presented in the sample patterns and the classes. The tagged feature-class space permits convenient building of a hierarchical structure of the classifications A content-addressable data retrieved characteristic is possessed by both types of classifier. Experimental results on the classifiers are presented.>