Integrated sensing and communication (ISAC) is pivotal for enabling simultaneous environment perception and data transmission in intelligent transportation systems (ITS). However, mission-critical ITS management applications, such as collision avoidance and autonomous driving, require stable and reliable ISAC services. Unfortunately, dense urban canyons, with their skyscraper-induced occlusions, create persistent coverage blind zones, posing significant challenges to these applications. To address these challenges, this paper explores a novel aerial intelligent reflecting surface (AIRS)-assisted ISAC system, where multiple AIRSs dynamically reconfigure the wireless propagation environment to enhance multi-vehicle sensing and base station (BS)-to-multiuser communication. To maximize the minimum achievable communication rate while ensuring sensing performance, we formulate a joint resource allocation problem considering BS beamforming, AIRS trajectory optimization, AIRS phase shift control, and user association. Given its highly coupled and nonconvex nature, we develop an alternating optimization framework tackling each subproblem sequentially. Specifically, we employ the Lagrangian dual transform and semi-definite relaxation (SDR) for BS beamforming, the successive convex approximation (SCA) method for AIRS trajectory optimization, matrix decomposition and equivalent rank-constrained transformation techniques for AIRS phase shift design, and a penalty dual decomposition (PDD)-based approach for user association. Furthermore, considering uncertainties in the vehicle’s angle of departure (AoD) due to urban mobility, we derive a worst-case sensing performance bound and generalize the proposed algorithm to a more complex scenario. Simulations validate the algorithm’s effectiveness, demonstrating superior communication rates and sensing performance over benchmark schemes, while ensuring robustness against AoD uncertainties.

Uncrewed aerial vehicle (UAV)-assisted joint communication and localization (JCAL) system have great potential and capacity to make future Internet of Things efficient, safe, smart, reliable, and sustainable. Generally, the traditional UAV path planning methods set the flying duration and hovering duration of UAVs as constants, and ignore the importance of UAV operation time in emergency rescue and other scenarios. In this article, we consider the path planning and time scheduling problem of UAV-assisted JCAL system for minimizing the UAV operation time under the constraints of the localization accuracy, communication message, and energy loss. Specifically, we first formulate path planning and time scheduling problem for UAV-assisted JCAL system and derive Cramér-Rao bound (CRB) as the localization accuracy constraint. The variables in the constraints of localization accuracy, communication overhead, and energy loss are deeply coupled, which leads to nonconvex optimization problems. Next, to solve the high nonconvex problem, we divide the original problem into two subproblems, i.e., time scheduling subproblem and path planning subproblem. We use equivalent convex transformation and successive convex approximation (SCA) to transform the nonconvex constraints into convex forms for solving the subproblems, respectively. Lastly, aiming to the robust problem of target and channel parameters, we convert the robust constraints into convex constraint forms by equivalent proof and S-Procedure. On this basis, we develop a robust algorithm for solving the uncertainty of target and channel parameters. Simulation results verify the feasibility of the proposed methods.

Future vehicle localization will be committed to improving the positioning accuracy and energy efficiency of localization systems in the intelligent transportation. Recently, reconfigurable intelligent surface (RIS) as an emerging technology has gained widespread attention and is favorable to enhance the performance of vehicle localization systems because of its capacity of customizing the wireless channel. In this paper, in order to minimize the transmit power, we consider the joint active and passive beamforming problem of RIS-assisted vehicle localization system under the constraints of the localization accuracy and the phase shift parameters of the RIS. Specifically, we establish the model of RIS-assisted vehicle localization system and derive the Cramér-Rao bound (CRB) as the localization performance metric. Next, for the scenario of single vehicle localization, we derive the optimal RISs’ phases, and obtain the optimal solution for joint active and passive beamforming based on semidefinite programming relaxation of the non-convex beamforming problem and the corresponding equivalent analysis. Lastly, aimming to the scenario of multiple vehicles localization, we transform the nonconvex joint active and passive beamforming problem into semidefinite programming (SDP) and geometric programming (GP) form subproblems through alternating optimization. Simulation results verify the feasibility of the proposed methods.

This paper considers a reconfigurable intelligent surface (RIS)-aided multi-user simultaneous localization and communication (SLAC) system with statistical position uncertainty, where an RIS is deployed to simultaneously enhance the quality of service. To this end, we first derive the closed-form Cramér-Rao lower bound concerning position parameters as the localization metric and also provide the achievable rate metric for communication services. Then, the joint robust design of subcarrier groups, beamforming vectors, and the phase-shift matrix of the RIS is formulated as a stochastic bi-objective optimization problem to maximize expected localization and communication metrics. Due to the nonlinearity of the multi-objective function and the coupling between optimizing variables, the resulting problem is highly non-convex. Accordingly, we transform the expected achievable rate into an analytical form and further develop a novel unified successive convex approximation (U-SCA)-based iterative algorithm to obtain a robust resource allocation strategy. In particular, we derive closed-form solutions of beamforming vectors and the phase-shift matrix of RIS to decrease the computational complexity. In addition, we also analyse the convergence of the proposed U-SCA-based algorithm. Simulation results demonstrate the effectiveness of the presented method.

Multi-target tracking (MTT) plays a significant role in intelligent transportation systems, serving as an enabling technology for applications such as self-driving, surveillance, and navigation. To enhance the MTT performance, the unmanned aerial vehicles (UAVs) have emerged as effective assistants to MIMO radar system, due to their advantages of high flexibility, controllable deployment and cost-effectiveness. Towards this end, this work investigates a multi-UAV-enabled MIMO radar system, in which each UAV is equipped with a MIMO radar unit and dispatched to track multiple targets simultaneously. We are interested in the joint trajectory planning and transmit resource optimization (i.e. radar waveform optimization and transmit power allocation) to minimize the system power consumption, subject to constraints related to UAVs motion, system resources, and tracking accuracy. Specifically, the posterior Cramér-Rao Lower Bound (PCRLB) is derived and employed as a guideline for the joint optimization. Given the non-convex and inter-variable coupling nature of the formulated problem, we decompose it into three sub-problems and design an alternating optimization method. Firstly, for the UAVs trajectory planning, we obtain sub-optimal results leveraging the successive convex approximation (SCA)-based algorithm. Next, we present a feasible solution set for radar waveform optimization. For transmit power allocation, we perform a convex transformation and find the numerical solution. In addition, through introducing the Lagrange dual method, we further obtain the optimal analytical solution. Finally, simulation results demonstrate the effectiveness and advantages of the developed strategy.

In this paper, a novel reconfigurable intelligent surfaces (RIS)-assisted joint localization and communication (JLAC) scheme is presented to supply both position-sensing and data transmission functions for a multi-user system by a frequency division strategy. In particular, considering the parameter uncertainty, we formulate the robust resource design problem as a statistical mixed-integer form, aiming to maximize localization and communication performance by joint subcarrier group, beamforming, and phase-shift optimization. To tackle the formulated non-convex problem efficiently, we develop an iterative method based on the stochastic successive convex approximation technology to handle the original problem. Simulation studies are presented to demonstrate the effectiveness of the proposed JLAC scheme and method.

Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we present DIST, a deep learning-based method that imputes the gene expression profiles on unmeasured locations and enhances the gene expression for both original measured spots and imputed spots by self-supervised learning and transfer learning. We evaluate the performance of DIST for imputation, clustering, differential expression analysis and functional enrichment analysis. The results show that DIST can impute the gene expression accurately, enhance the gene expression for low-quality data, help detect more biological meaningful differentially expressed genes and pathways, therefore allow for deeper insights into the biological processes.

It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to identify upfront software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typically taken into consideration, although the latter is commonly overlooked in the academic literature. Many classification methods have been suggested to conduct fault prediction; yet associative classification methods remain uninvestigated in this context. This paper proposes an associative classification (AC)-based fault prediction method, building upon the CBA2 algorithm. In an empirical comparison on 12 real-world datasets, the AC-based classifier is shown to achieve a predictive performance competitive to those of models induced by five other tree/rule-based classification techniques. In addition, our findings also highlight the comprehensibility of the AC-based models, while achieving similar prediction performance. Furthermore, the possibilities of cross project prediction are investigated, strengthening earlier findings on the feasibility of such approach when insufficient data on the target project is available.

This letter presents a new mixed-order MUSIC algorithm for far-field and near-field sources localization using a sparse symmetric array. By exploiting the special array geometry, the proposed algorithm constructs a cumulant matrix to estimate the directions of arrival (DOAs) of both far-field and near-field sources using the conventional MUSIC method. With the estimated DOAs and the covariance matrix of the sparse array, the far-field and near-field sources are identified and the range parameters of near-field sources are also obtained by defining the range spectrum. Compared with the traditional algorithms, the proposed algorithm has moderate computation complexity, and provides higher resolution, and also improves the parameters estimation accuracy. Simulation results are provided to demonstrate the performance improvement of the proposed method.

As digital resources increasingly growing and the economic benefit of digital intellectual property rights being increasingly important, people has been increasingly emphasis on information security issues brought by the data remnants in storage devices. They try their best to prevent the potential risks. In this paper, we survey comprehensively related technologies, standards and trends of erasure, discuss the shortcomings of techniques on adding secure deletion to file systems and on cryptographic to prevent deleted data from being accessible. We focus on secure deletion mechanism in the NTFS file system, combining the asynchronous I/O multi-threading technology. Finally we present a novel data erasing algorithm, Quick Erase, which not only greatly exceeds the speed of the existing international data erasing algorithms but also can be easily combined with a variety of standard algorithms to form various high-speed mixed erase algorithms. It can be used to erase file data and metadata to ensure the security and reliability of data erasing. The test result of Quick Erase indicates that the erasure speed of one big file has reached 10-12 s/100 MB, faster than that of existing secure deletion tools (60-80 s/100 MB). The algorithm has good application prospects for practical applications.

Nowadays, the task of security monitoring for vast Internet content has the problem of time efficiency. In improving the efficiency, we have studied and compared several typical Multi-pattern searching algorithms such as AC and Wu-Manber algorithms both in English and Chinese environment. Testing results show that the classic Multi-pattern matching algorithms are less efficient in the Chinese environment than in English. And we analyze the factors that cause this: Chinese characters are much bigger a set than English 26 letters, which repeat much but Chinese dose not in a text, and Chinese key word is much shorter than English. According to these factors, this paper presents a novel fast multi-pattern matching algorithm, Byte-Coding algorithm (BC) and a fast semantic content filtering algorithm based on the simple semantic characteristics. By adding the weights of different sizes to the key words, we can improve the accuracy and the speed of filtering system. We thoroughly compare our algorithm with the conventional ones in the speed of filtering. The results show that in multi-pattern mode its speed is at least ten times faster than the traditional AC, WM algorithm and more scaleable with the number of patterns increasing; in simple semantic with frequency calculations mode, this algorithm is still suitable and much faster. The algorithm can also apply to multi-languages environment and rapid parallel or distributed monitoring system as a core module.

A conventional video file contains a single temporally-ordered sequence of video frames. Clients requesting on-demand streaming of such a file receive (all or intervals of) the same content. For popular tiles that receive many requests during a file playback time, scalable streaming protocols based on multicast or broadcast have been devised. Such protocols require server and network bandwidth that grow much slower than linearly with the file request rate. This paper considers "nonlinear" video content in which there are parallel sequences of frames. Clients dynamically select which branch of the video they wish to follow, sufficiently ahead of each branch point so as to allow the video to he delivered without jitter. An example might be "choose-your-own-ending" movies. With traditional scalable delivery architectures such as movie theaters or TV broadcasting, such personalization of the delivered video content is very difficult or impossible. It becomes feasible, in principle at least, when the video is streamed to individual clients over a network. This paper analyzes the minimal server bandwidth requirements, and proposes and evaluates practical scalable delivery protocols, for on-demand streaming of nonlinear media.

Recently proposed streaming protocols are able to deliver multimedia files on-demand with the required server bandwidth growing only logarithmically with the file request rate. The same efficiencies are achieved for network bandwidth if delivery is over a true broadcast channel. This paper considers the required network bandwidth for on-demand streaming over multicast delivery trees. We consider both simple canonical delivery trees, and more complex cases in which delivery trees are constructed using both existing and new algorithms for various randomly generated network topologies and client site locations. Our results quantify the potential savings from the use of multicast trees that are configured to minimize network bandwidth rather than the latency to the content server. Further, we show that it is possible to achieve reasonably close to the minimum possible bandwidth usage for both network and server simultaneously, with a practical on-demand streaming protocol.