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BACKGROUND: Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a cardiac cycle, reconstruct the 3D arterial anatomy from bi-planar views, and generate the complementary fusion map with myocardial images. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible. OBJECITVE: In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases. METHOD: A detection algorithm is first used to detect the key points (i.e. landmarks) of coronary arteries, and then an optical flow method is employed to track the trajectories of the selected key points. The ED and ES frames are identified based on all these trajectories. Our method was tested with 62 ICA videos from two separate medical centers. RESULTS: Comparing consensus interpretations by two human expert readers, excellent agreement was achieved by the proposed algorithm: the agreement rates within a one-frame range were 92.99% and 92.73% for the automatic identification of the ED and ES image frames, respectively. CONCLUSION: The proposed automated method showed great potential for being an integral part of automated ICA image analysis.

In this paper, a novel despeckling algorithm for synthetic aperture radar (SAR) images based on improved Frost filtering is proposed. A reconstructed decay factor which can better characterize the homogeneous and edge regions of SAR image is added into the Frost filter model by using the Lee filter coefficient to implement adaptively controlling the degree of filtering in different regions. The three-stage filtering strategy is performed for effective despeckling and edge preservation. The SAR image is pre-filtered by using the Frost filtering of the small window to obtain local statistics, and then the original SAR image is refine filtered twice utilizing these local statistical parameters. The two values are, respectively, biased toward smoothing and edge preservation by adjusting the variable constant and the window scales. Finally, the final filtering result is obtained by weighting the two filtering results with the reconstructed decay factor we proposed. The experiments demonstrate that compared with advanced approaches, the proposed method improves both despeckling and edge preservation ability.

Wireless visual sensor networks (WVSNs) comprise a large number of battery-powered visual sensors with limited processing and transmission capacity in the field of interest. Hence, energy conservation is an important issue in resource-constrained WVSNs. In-network data fusion, clustering networks has been proved to be an effective scheme in reducing energy consumption. However, in most clustering algorithms, clustering may introduce bottlenecks to WVSNs which bring extra delay in the processes of data collection and forwarding. Such delay can be reduced by the design of routing schemes. In this paper, we propose an approach of energy-delay trade-off (EDT) based on two-sided matching theory. First, we determine a balance clustering structure which can balance energy consumption and delay in random-densely-deployed WVSNs. Then, the problem formulation of EDT problem in the balance clustering structure is provided. Moreover, the transformation strategy based on two-sided matching (TS-TSM) is proposed. After the TS-TSM, the EDT problem is transformed into a stable two-sided matching (STSM) problem with single objective function and the computational complexity is reduced. Furthermore, most of floating-point operations in original EDT problem are converted into integer operations which are easy to parallel compute. Finally, an improved deferred-acceptance algorithm (IDAA) is provided to generate a stable matching solution as the EDT scheme. Evaluation results demonstrate that the EDT approach can reduce delay in data aggregation processes and keep the total energy consumption at low levels. Further simulations show that the EDT approach is a fast approach, and it is not inferior to the existing algorithms.

Access control technology is one of the key technologies to ensure safe resource sharing. Identity authentication and authority distribution are two key technologies for access control technology to restrict unauthorized users from accessing resources and resources can only be accessed by authorized legal users.However, user privacy protection and frequent permission changes are two thorny issues that need to be solved urgently by access control technology. To deal with these problems, this paper proposes a dynamic access control technology based on privacy protection. Compared with existing access control technologies, the main advantages of this paper are as follows: 1) encrypt and hide the attributes of entities, and use attribute-based identity authentication technology for identity authentication, which not only achieves the purpose of traditional identity authentication, but also ensures the attributes and privacy of entities are not leaked; 2) Binding resource access permissions with entity attributes, dynamically assigning and adjusting resource access control permissions through changes in entity attributes, making resource access control more fine-grained and more flexible. Security proof and performance analysis show that the proposed protocol safe under the hardness assumption of the discrete logarithm problem (DLP) and the decision bilinear Diffie-Hellman (DBDH) problem. Compared with the cited references, it has the advantages of low computational complexity, short computational time, and low communication overhead.

Cruise control (CC) is a fundamental and significant technology in autonomous-driving technology, and accuracy of speed control and ride comfort are the basic performances. This paper designs a new controller for cruise control-interval type-2 fuzzy logic controller (IT2 FLC), which takes relative speed and acceleration of main vehicle as input variables and the acceleration of the desired change as output variable. Finally, different working conditions are set up, and simulation is carried out under type-1 fuzzy logic controller (T1 FLC) and IT2 FLC, respectively. The simulation results show that this method can achieve a good control effect, and it can provide a new thinking for autonomous-driving technology and other control fields in the future.

Visual perception is an important part in the system of autonomous vehicles. The choice of image focus will directly affect decision-making and driving safety, so how to analyze the attention problem and select the appropriate attention area is of great significance. In this paper, an attention guidance for autonomous vehicles is proposed, which can help the system to understand attention areas. According to empirical analysis from different drivers, attention allocations of human drivers are summarized, and membership functions are established. Based on the theory of type-2 fuzzy sets and fuzzy comprehensive evaluation, the rationality of three drivers' visual focus are evaluated in the example.

This letter introduces our initial results of target coverage model in wireless visual sensor networks (WVSNs). In this letter, we develop the ${Q}$ -coverage model for target detection which is a basis application in WVSNs, and propose a novel coverage degree (CD)-coverage model. First, we present a new concept of CD-coverage and formulate the CD-coverage problem. Further, we propose a harmony search-based coverage-enhancing (HS-CE) approach to obtain an approximate optimal solution for the CD-coverage problem. The extensive simulations show the validity of CD-coverage model and HS-CE approach.

Localization is one of the essential problems in the Internet of Things (IoT) and other wireless sensor applications. Most traditional range-free localization algorithms ignore the anisotropy factors, which are frequently observed in wireless sensor networks (WSNs) and result in low positioning precision. To mitigate the impact of anisotropy on localization, this paper proposes an extended area multihop node localization method. The proposed method classifies and discusses the boundaries of the concave area within the communication range of the node and then uses the maximum split communication distance of the node to identify and mark the boundary of the concave area. When the shortest communication path between the nodes is affected by the concave area, the boundary of the concave area is expanded to obtain the new shortest communication path, and the node distance is obtained by comparing the changes in the communication path. After a large number of simulation tests, it is shown that the calculation accuracy of this scheme is better than that of similar modern mainstream localization algorithms.

At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in &ldquo, concave regions&rdquo, In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with &ldquo, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes.

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

In the practical application of WSN (wireless sensor network), location information of the sensor nodes has become one of the essential information pieces in the whole network. At present, some localization algorithms use intelligent optimization algorithm to optimize the node group directly. Although the overall localization error is reduced, the location deviation of individual unknown nodes will be larger, and the large number of iterations will cause a large energy consumption of nodes. Aiming at the above problems, this paper comes up with a two-stage WSN localization algorithm based on the degree of K-value collinearity (DC-K) and improved grey wolf optimization. The first stage is aiming at the defects of the existing collinearity algorithm, putting forward the concept of DC-K, according to the K-value to carry out the initial location in the first stage. The second stage is using the improved grey wolf optimization algorithm to optimize the location results which were obtained in the first stage, so as to get more accurate location results. The experimental results display that this localization algorithm with a better localization accuracy has high robustness and has fewer iterations in the optimization process, which greatly reduces the energy consumption of nodes.

Comparing to the multi-objective optimization, multimodal multi-objective optimization brings greater challenge since it involves both the decision space and objective space. The classic multi-objective optimization method MOEA/D could not locate more than one optimal solution in the decision space corresponding to the same optimal solution in PF due to the shortage of strategies for handing multimodality. Therefore, a modified MOEA/D whth niching strategy is proposed, in which the MOEA/D-DE is modified for balancing the convergence and diversity in the objective space while the niching strategy is adopted for save the multiple solutions in the decision space. Further more, the redundant deletion strategy works for removing the redundancy and saving the computational resource. The proposed algorithm is tested on the 22 newly proposed benchmark functions. Experimental results show the competitive performance of the proposed algorithm.

Wireless visual sensor networks (WVSNs) comprise a large number of camera-equipped visual sensors, which are often deployed densely to gain enhanced observations in field of interest. Therefore, there exists abundant image redundancy caused by the image content correlation (ICC) between cameras with the overlapped field of views (FoVs). Image compression is an important method to remove the image redundancy. Aiming at the problem of image compression, this paper designs a 3-D compression-oriented ICC (3D-COICC) model for WVSNs to quantitatively describe the ICC characteristics. First, the 3-D sensing model is developed by providing an improved method for the calculation of FoVs. After that, the 3D-COICC model is proposed by comparing the difference between foreshortening effects for one observed object, and then an algorithm of 3D-ICC is provided to estimate the ICC value. Furthermore, a scheme of correlation-based compression (CBC) is proposed to remove the redundancy of content between transmitted images, and thus the communication data are compressed. Where, an algorithm of image block restoration is provided to realize the transformation between two image blocks with the same pre-image. Evaluation results demonstrate that the developed 3-D sensing model can accurately depict the FoV of camera, and the 3D-COICC model outperforms the state of the art in accuracy. Further simulations show that the CBC scheme based on the 3D-COICC model is an effective method to remove the image content redundancy and improve the compression ratio.

This paper focuses on a full-duplex (FD) two-way relay network with one base station (BS), one FD amplify-and-forward (AF) relay, and one user, and the BS is equipped with massive multiple-input multiple-output (MIMO) antennas. To reduce the complexity and cost of BS, the paper proposes a practical antenna selection way in BS to complete transmission in two-way FD relay networks to optimize the system performance of outage probability or bit error ratio (BER). In the proposed scheme, BS can implement transmit antenna selection (AS) based on the instantaneous channel state information (CSI), thereby improving the system performance compared to the traditional FD relay networks. Furthermore, closed-form expressions for the outage probability and average BER are derived under Nakagami-m channels. The analytical results show that the superiority of proposed scheme compared with the conventional AF relay scheme and the correctness of the theoretical analysis verified by the Monte Carlo simulations.

A fuzzy identity-based signature (FIBS) is exactly like a traditional identity-based signature except that a signature issued under an identity \(\mathrm {id}\) can be verified under any identity \(\mathrm {id}'\) that is “close enough” to \(\mathrm {id}\). This property allows FIBS having an efficient application in biometric authentication and three schemes over lattices exist, two constructions in the random oracle model and one in the standard model. However, the identities can only support binary vectors, i.e., \(\{0,1\}^{\ell }\), which greatly limit the scope of its application. In this paper, a FIBS scheme from the hardness of lattice problems for identities living in a large universe, i.e., \(({\mathbb {Z}}_{q}^{n})^{\ell }\), is proposed, so that they can capture more expressive attributes and the new construction is proved to be existentially unforgetable against adaptively chosen identity and message attacks (EU-aID-CMA) in the random oracle model.

Node localization is important in many WSN applications. Most of existing algorithms are not applicable for networks in a concave area. To address this problem, a novel localization algorithm is proposed. The key of this algorithm is the design of neighborhood function which only uses distances between neighbor nodes to generate a new set of estimated locations from an old set. Both range-based and range-free localization can use it to estimate locations of nodes if distances between neighbor nodes are measured by hardware or estimated by distance estimation algorithms. Simulation results indicate that this algorithm can achieve accurate and reliable localization results in a concave area.