Internet Technology Letters最新文献

Image preprocessing is a method to transform raw image data into clean image data. The objective of preprocessing is to improve the image data by suppressing undesired distortions. Enhancement of some image features which are relevant for further processing of image and analysis task is also done in preprocessing. Screening and diagnosis of various eye diseases like diabetic retinopathy, Choroidal Neovascularization(CNV), DRUSEN, etc. are possible using digital retinal images. This paper aims to provide a better understanding and knowledge of the computer algorithms used for retinal image preprocessing. In this paper, various image preprocessing techniques are incorporated such as color correction, color space selection, noise reduction, and contrast enhancement on retinal images. Retinal blood vessels are better seen in Green color space instead of Red or Blue color space. Noise reduction through Block matching and 3D(BM3D) techniques show a significant result as compared to Total Variation Filter (TVF) and Bilateral Filter (BLF). Contrast enhancement through Contrast Limited Adaptive Histogram Equalization (CLAHE) outperforms Global Equalization (GE) or Adaptive Histogram Equalization (AHE). Evaluation parameters such as Mean square error, Peak Signal Noise ratio, Structured similarity index measures, and Normalized root mean square error values for BM3D noise filtering are 0.0029, 25.3370, 0.6839 and 0.0998 respectively which shows that BM3D outperforms the others.

With the widespread adoption of Internet of Things (IoT) technologies, botnet attacks have become the most prevalent cyberattack. In order to combat botnet attacks, there has been a considerable amount of research on botnet attacks in IoT ecosystems by graph-based machine learning (GML). The majority of GML models are vulnerable to adversarial attacks (ADAs). These ADAs were created to assess the robustness of existing ML-based security solutions. In this letter, we present a novel adversarial botnet attack (ADBA) that modifies the graph data structure using genetic algorithms (GAs) to trick the graph-based botnet attack detection system. According to the experiment results and comparative analysis, the proposed ADBA can be executed on resource-constrained IoT nodes. It offers a substantial performance gain of 2.15 s, 52 kb, 92 817 mJ, 97.8%, and 27.74%–41.82% over other approaches in term of Computing Time (CT), Memory Usage (MU), Energy Usage (EU), Attack Success Rate (ASR) and Accuracy (ACC) metrics, respectively.

With the continuous improvement of people's mental pressure and life pace, people's study and life pressure would increase, leading to the increase of people's depression. Depression is a mental illness, a chronic mental illness that is inconsistent with the patient's physical condition. In recent years, as people know more and more about depression, and they have more and more research on depression, many research scholars have provided new ideas for the treatment of depression, and this paper takes this as the research direction and research basis. This paper introduces the background of EEG (electroencephalogram, EEG) and evoked potential and artificial intelligence (Artificial intelligence, AI) methods, and then analyzes the patients with depression based on AI, and summarizes the application of electronics. The concept analysis of depression, EEG and evoked potential is put forward. At the end of the article, the application of machine learning in depression is studied. At the same time, with the continuous development of machine learning in artificial intelligence, the EEG and evoked potential related work in patients with depression are also facing new opportunities and challenges.

The real-time sports and physical sign data display of various intelligent sports equipment and software can provide help for professional athletes to formulate sports strategies. However, for those who do not have any special knowledge, visual data cannot help them to make correct sports plans. Based on the above problems, this paper designed and implemented a cloud computing platform that can collect the user's movement and physical sign information, so as to provide personalized sports prescription service for them. In the performance test of the platform, the experimental results showed that the 50% line response time value and 90% line response time value were the maximum when the number of test threads reaches 1000 and 900, and the maximum value was 2896 and 3136 ms respectively; when the number of test threads was 200, the minimum value was 86 and 132 ms. Therefore, it is very necessary to develop and apply the sports health CMP based on the Internet of Things (IoT) technology.

At present, the living habits of college students are relatively poor, and the amount of exercise is reduced, leading to their physical fitness getting worse and worse. Therefore, people began to study the physical health monitoring of college students. Machine learning and high-performance computing in medical applications provide technical support for intelligent medical technology. With the rapid development of computer network, human beings have entered the information and digital era, and sports health big data has become more and more popular. The combination of sports health big data and student physical health monitoring technology, in a sense, can realize automatic data processing through intelligent medical and sports health database and other information technologies, thus promoting the popularization of health monitoring technology. However, the current health monitoring equipment has many problems, such as complex collection, low accuracy and limited processing of health data. To solve this problem, this paper developed an Application (APP) based on sports health big data technology that can monitor multiple vital signs such as human heart rate and body temperature, and analyze the physical health of college students, so that college students can easily understand their health status in daily life, so as to promote the healthy development of students' physique and encourage them to actively participate in physical exercise. The experiment proved that, in the heart rate monitoring, when the speed is 6 km/h, the error rate of the college students' physical health monitoring APP designed in this paper is 8.15%. The average accuracy rate of student steps monitoring is 97.12%. This showed the accuracy and availability of the APP's monitoring function for human vital signs. It has certain application value and significance to help students improve their physical quality.

This paper introduces the application of blockchain technology in the field of IoT medical information, proposes a secure and trustworthy framework for storing and sharing medical information based on blockchain, containing functions such as tamper-proof storage of IoT medical data, desensitization processing of sensitive information and sharing of medical information security, and predicts the future market state transfer and the construction time of the model framework from the perspective of economics.

This letter studies an OFDM based intelligent reflecting surface (IRS)-aided multiuser simultaneous wireless information and power transfer (SWIPT) system. With subcarrier allocation, users use the assigned subcarriers for information decoding (ID), and use signals received on subcarriers allocated to other users for energy harvesting (EH). An optimization problem is formulated to maximize the system energy efficiency by jointly optimizing subcarrier and power allocation and IRS reflection coefficients matrix. It can be solved by an alternating optimization algorithm. Simulation results reveal that energy efficiency can be enhanced in our proposed scheme compared with the benchmark schemes.