High-performance FPGA implementation of the secure hash algorithm 3 for single and multi-message processing

<span>The secure hash function has become the default choice for information security, especially in applications that require data storing or manipulation. Consequently, optimized implementations of these functions in terms of Throughput or Area are in high demand. In this work we propose a new conception of the secure hash algorithm 3 (SHA-3), which aim to increase the performance of this function by using pipelining, four types of pipelining are proposed two, three, four, and six pipelining stages. This approach allows us to design data paths of SHA-3 with higher Throughput and higher clock frequencies. The design reaches a maximum Throughput of 102.98 Gbps on Virtex 5 and 115.124 Gbps on Virtex 6 in the case of the 6 stages, for 512 bits output length. Although the utilization of the resource increase with the increase of the number of the cores used in each one of the cases. The proposed designs are coded in very high-speed integrated circuits program (VHSIC) hardware description language (VHDL) and implemented in Xilinx Virtex-5 and Virtex-6 A field-programmable gate array (FPGA) devices and compared to existing FPGA implementations.</span>

FPGA-based Implementation of SHA-256 with Improvement of Throughput using Unfolding Transformation

Security has grown in importance as a study issue in recent years. Several cryptographic algorithms have been created to increase the performance of these information-protecting methods. One of the cryptography categories is a hash function. This paper proposes the implementation of the SHA-256 (Secure Hash Algorithm-256) hash function. The unfolding transformation approach was presented in this study to enhance the throughput of the SHA-256 design. The unfolding method is employed in the hash function by producing the hash value output based on modifying the SHA-256 structure. In this unfolding method, SHA-256 decreases the number of clock cycles required for traditional architecture by a factor of two, from 64 to 34 because of the delay. To put it another way, one cycle of the SHA-256 design can generate up to four parallel inputs for the output. As a result, the throughput of the SHA-256 design can be improved by reducing the number of cycles by 16 cycles. ModelSim was used to validate the output simulations created in Verilog code. The SHA-256 hash function factor four hardware implementation was successfully tested using the Altera DE2-115 FPGA board. According to timing simulation findings, the suggested unfolding hash function with factor four provides the most significant throughput of around 4196.30 Mbps. In contrast, the suggested unfolding with factor two surpassed the classic SHA-256 design in terms of maximum frequency. As a result, the throughput of SHA-256 increases 13.7% compared to unfolding factor two and 58.1% improvement from the conventional design of SHA-256 design.

Research on Energy Efficiency Management of Forklift Based on Improved YOLOv5 Algorithm

As an important tool for loading, unloading, and distributing palletized goods, forklifts are widely used in different links of industrial production process. However, due to the rapid increase in the types and quantities of goods, item statistics have become a major bottleneck in production. Based on machine vision, the paper proposes a method to count the amount of goods loaded and unloaded within the working time limit to analyze the efficiency of the forklift. The proposed method includes the data preprocessing section and the object detection section. In the data preprocessing section, through operations such as framing and clustering the collected video data and using the improved image hash algorithm to remove similar images, a new dataset of forklift goods was built. In the object detection section, the attention mechanism and the replacement network layer were used to improve the performance of YOLOv5. The experimented results showed that, compared with the original YOLOv5 model, the improved model is lighter in size and faster in detection speed without loss of detection precision, which could also meet the requirements for real-time statistics on the operation efficiency of forklifts.

Research on News Text Classification Based on Deep Learning Convolutional Neural Network

Aiming at the problems of low classification accuracy and low efficiency of existing news text classification methods, a new method of news text classification based on deep learning convolutional neural network is proposed. Determine the weight of the news text data through the VSM (Viable System Model) vector space model, calculate the information gain of mutual information, and determine the characteristics of the news text data; on this basis, use the hash algorithm to encode the news text data to calculate any news. The spacing between the text data realizes the feature preprocessing of the news text data; this article analyzes the basic structure of the deep learning convolutional neural network, uses the convolutional layer in the convolutional neural network to determine the change value of the convolution kernel, trains the news text data, builds a news text classifier of deep learning convolutional neural network, and completes news text classification. The experimental results show that the deep learning convolutional neural network can improve the accuracy and speed of news text classification, which is feasible.

Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm

In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.

AIM against survey fraud

Objectives Although there exists a variety of anonymous survey software, this study aimed to develop an improved system that incentivizes responses and proactively detects fraud attempts while maintaining anonymity. Materials and Methods The Anonymous Incentive Method (AIM) was designed to utilize a Secure Hash Algorithm, which deterministically assigned anonymous identifiers to respondents. An anonymous raffle system was established to randomly select participants for a reward. Since the system provided participants with their unique identifiers and passwords upon survey completion, participants were able to return to the survey website, input their passwords, and receive their rewards at a later date. As a case study, the validity of this novel approach was assessed in an ongoing study on vaping in high school friendship networks. Results AIM successfully assigned irreversible, deterministic identifiers to survey respondents. Additionally, the particular case study used to assess the efficacy of AIM verified the deterministic aspect of the identifiers. Discussion Potential limitations, such as scammers changing the entry used to create the identifier, are acknowledged and given practical mitigation protocols. Although AIM exhibits particular usefulness for network studies, it is compatible with a wide range of applications to help preempt survey fraud and expedite study approval. Conclusion The improvements introduced by AIM are 2-fold: (1) duplicate responses can be filtered out while maintaining anonymity and (2) the requirement for the participant to keep their identifier and password for some time before returning to the survey website to claim a reward ensures that rewards only go to actual respondents.