Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.

Summary Nowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an autonomous vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions is essential for the development of autonomous vehicles since these algorithms must be able to manage dynamic and complex urban crossings. In this research an optimal deep BiLSTM‐GAN classifier to detect the movement of smart vehicles, initially the preprocessing stage is performed to decrease noise in the received data after that the essential regions are next be extracted in the region of interest (ROI) to make the right decision. The extracted data are forwarded to the GAN for road segmentation as well as the optimized deep BiLSTM classifier, which recognizes the traffic sign, simultaneously making it possible to do a modified Hough line‐based maneuver prediction using the segmented information from the roads. Finally, the GAN determines the lane, and the BiLSTM predicts the traffic sign. The K‐nearest neighbor (KNN)‐based autonomous vehicle movement controllers are used to make the decision based on the predicted traffic sign and information about the lane. The proposed HSO algorithm was developed as the outcome of the common fusion of hawk and swarm optimization. Based on lane detecting achievements, at training percentage (TP) 90, the accuracy is 91.75%, Peak signal‐to‐noise ratio (PSNR) is 64.84%, mean square error (MSE) is 28.78, and mean absolute error (MAE) is 20.20, respectively, similarly based on the traffic sign prediction achievements at TP 90, the accuracy is 93.71%, sensitivity is 95.15%, specificity is 93.91%, and MSE is 28.78%, respectively.

Abstract To provide high‐quality imaging by decreasing the sparse view imaging artefact from the computed tomography (CT) images, this study addresses a new artefact suppression technique for sparse data for both single material and multi‐material objects that have diverse materials. It begins with a pre‐reconstructed image and a network of target patches that have been trained beforehand and then uses the forward projection (FP) approach to resolve the structural mutation brought on by sparse‐view projection. As an edge‐preserving operator to commit to the forward operator for sinogram correction, the bilateral filter was used. Both simulated and actual data have been gathered and evaluated in experiments. The suggested forward operator and normalized compensation (FONC) method produce results that have far smaller artefact and errors than those of more traditional techniques. For simulation # blade, the Normalized Mean Square Distance (NMSD) of the proposed method was reduced by 8.94%, Structural Similarity Index (SSIM) and the Universal Quality Index (UQI) were increased by 78.17% and 80.49%, respectively, which also demonstrate better uniformity of the results to practical data # pan, where the root mean squared error was reduced by 13.93%. SSIM and UQI were increased by 25.66% and 37.02%, respectively. The results conclusively show that the planned strategies are successful in eliminating artefact for irregular objects.

Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.

This study presents a multi‐method fusion and optimisation framework that can optimally combine different existing methods to further enhance the segmentation performance. The framework, in which the original accumulating process is improved and a new combination process is added, is the extension of the previously developed ‘accumulated local fuzzy c‐means with spatial information’ method. In the improved accumulating process, different segmentation methods are utilised in local windows to judge whether each pixel belongs to the object. In the new combination process, the accumulated results of different segmentation methods are weighted combined, where the weights of different methods are optimised by the genetic algorithm with the objective of minimising standard deviations of both the object and the background pixels. Typical images and all images in the Weizmann's Segmentation Evaluation Database are tested in the experiments. The results show that the authors’ method can perform better than some state‐of‐the‐art methods, and combining more methods in the framework can bring better performance. Moreover, the proposed multi‐method combination framework is parameterless, which increases its adaptability in various applications.

This study presents an adaptive image segmentation method based on the fuzzy c‐means with spatial information (FCM_S). First, the advantages of the local FCM_S over the global FCM_S and its segmentation characteristics are introduced, on the basis of which, the accumulated local FCM_S is proposed to classify each pixel in an image by using information from different local windows that contain them. The local window size is calculated automatically, and the classification results of all pixels are stored together in the accumulated result. The grey levels of the background and the object pixels in the accumulated image, which is converted from the accumulated result, are distributed around 0 and the maximal grey level. Thus, it can be segmented by the grey level where the change rate of the count of object pixels reaches the minimum. Experiments are performed on 16 images from the Weizmann's database, as well as two real‐world and four synthetic images. The results validated that the proposed method can segment images with inhomogeneity well and can gain better area overlap measure when compared with some new segmentation methods. Moreover, the proposed method is parameterless.

Aiming at the difficulty of choosing filtering parameters of Non-Local Means (NLM) denoising algorithm for Cone-Beam Computed Tomography (CBCT) projection images, an adaptive NLM denoising algorithm is proposed. First, a simplified noise estimation method based on sub-block division is proposed to replace the approach of designating the background region, which can adaptively identify the background region and estimate the noise level when the background region is changed obviously. And then an algorithm of adaptively obtaining filter strengths of projection images is proposed by analyzing the impact of filter strength to filtering result in NLM denoising algorithm, in which the filter strength is adjusted according to the characteristics of current image and the noises in all projection images are suppressed to a similar level. The experimental result shows that the proposed algorithm can accurately estimate the noise level of projection image and markedly improve the slice image quality.

Reconstruction speed and image quality are the keys of 3D image reconstruction in cone beam CT (CBCT), this article have researched on the 3D image reconstruction in CBCT, and a self-adapting fast reconstruction method for high resolution images is presented. The method is based on the FDK algorithm and Z-line data first algorithm, which introduces the reconstruction task division algorithm based on Z-line truncation, and load algorithm of filtered projection images based on the VOI reconstruction region. It divide the reconstruction task in a self-adapting method according to the task amount and memory size, thus reduce the data load amount and frequency of filtered projection images. The experimental results show that the method achieves fast reconstruction speed for high resolution images in CBCT, and the image quality almost has no difference with FDK algorithm.