Recently, it has been found that cloud storage still security risks, and research on the privacy of user data information is in early stage. This paper studies risks data, designs an image encryption scheme based neural networks. First, existing network model improved to obtain a new bidirectional activation (BA) network, establish many-to-one mapping relationship between key chaotic initial value, hide original system, improve randomness system. Then, medical dynamic index scrambling...

This paper provides an in-depth review of advancements in robotic motion control systems through the application Deep Reinforcement Learning (DRL). The study highlights growing complexity tasks. It emphasizes need for adaptive strategies dynamic, uncertain environments where traditional methods fall short. By categorizing DRL algorithms into value-based approaches, such as Q-Networks (DQN), and policy gradient methods, like Proximal Policy Optimization (PPO), offers a comparative analysis...

The packing cotton picker has become the mainstream cotton-picking equipment due to its advantages of integrated picking and non-stop unloading. Precision is an inevitable development trend, accurate detection bale's dimension during process first step toward achieving this. Therefore, this paper proposes a geometry-based method determine bale in picker. By establishing mathematical model process, relationship between rotation angle rocker arm component derived. reliability...

Accurately predicting the wind power output of a farm across various time scales utilizing Wind Power Forecasting (WPF) is critical issue in trading and utilization. The WPF problem remains unresolved due to numerous influencing variables, such as speed, temperature, latitude, longitude. Furthermore, achieving high prediction accuracy crucial for maintaining electric grid stability ensuring supply security. In this paper, we model all turbines within graph nodes built by their geographical...

Widely used as fundamental security components in most cryptographic applications, random number generators (RNGs) rely mainly on randomness provided by entropy sources. If the is less than expected, RNGs may be compromised and thus impair of whole applications. However, common assumptions (e.g., outputs are independent identically distributed, i.e., IID) not always hold. For example, many sources based some physical phenomena that fragile sensitive to external factors temperature), which...

Return-oriented programming (ROP) is a code reuse attack that chains short snippets of existing to perform arbitrary operations on target machines. Existing detection methods against ROP exhibit unsatisfactory accuracy and/or have high runtime overhead. In this paper, we present ROPNN, which innovatively combines address space layout guided disassembly and deep neural networks detect payloads. The disassembler treats application input data as pointers aims find any potential gadget chains,...

Interactions among microorganisms have been the key to understand microbial communities. As an important member of microorganisms, bacteria are closely related human diseases. Therefore, studying interaction between plays role in microbiome research. There a large number published medical literatures that contain small-scale data about interactions bacteria. These often record discovered by co-cultural experiments for two or more species. Mining and organizing them into databases will...

Vehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge multi-pose multi-angle vehicle distribution has largely been overlooked. This paper proposes an approach for recognition, addressing aforementioned issues. By utilizing faster regional convolution neural networks, this method automatically captures features...

Voxel-based 3D object detection methods have been applied in various applications such as autonomous driving, robot navigation, and Augmented Reality. However, the sparse unstructured characteristics of point cloud voxels prevent high-performance voxel encoding usually require generalized platforms, CPUs. In this paper, an FPGAbased Voxel Encoding Accelerator (VEA) is proposed, which contains a generator feature extender. The decouples storage information storage, leading to high-speed...

Return-oriented programming (ROP) is a code reuse attack that chains short snippets of existing to perform arbitrary operations on target machines. Existing detection methods against ROP exhibit unsatisfactory accuracy and/or have high runtime overhead. In this paper, we present DeepReturn, which innovatively combines address space layout guided disassembly and deep neural networks detect payloads. The disassembler treats application input data as pointers aims find any potential gadget...

Multi-line LiDAR is widely used in autonomous vehicles, so point cloud-based 3D detectors are essential for driving. Extracting rich multi-scale features crucial driving due to significant differences the size of different types objects. However, real-time requirements, large-size convolution kernels rarely extract large-scale backbone. Current commonly use feature pyramid networks obtain features; however, some objects containing fewer clouds further lost during downsampling, resulting...

Abstract Convolution neural networks have been widely used in the field of computer vision, which effectively solve practical problems. However, loss function with fixed parameters will affect training efficiency and even lead to poor prediction accuracy. In particular, when there is a class imbalance data, final result tends favor large‐class. detection recognition problems, large‐class dominate due its quantitative advantage, features few‐class can be not fully learned. order learn...

Support vector machine (SVM) is a new general learning machine, which can approximate any function at accuracy. The baseband predistortion method for amplifier studied based on SVM. Simulation shows good linearization results and generalization performance.