With the increasingly diverse functional requirements of contemporary electronic products, the complexity of CMOS circuits often used in chips becomes higher and the number of transistors used increases. To solve the resulting performance problems of CMOS circuits, researchers have searched for many transistor sizing technologies. This paper summarizes three methods of CMOS circuit optimization. The paper introduces these three methods in terms of principle, effect, and application scenarios, and compares them respectively. Through analysis and simulation, it can be found that the use of these methods in circuit design can effectively achieve the purpose of improving speed, reducing power consumption, and improving the overall performance of the circuit. This lays a solid foundation for finally being able to present a good product with excellent performance and enhance the market competitiveness of the product. CMOS circuits are widely used, and circuit optimization is of great importance to the overall circuit design, and better optimization methods can even promote the development of the entire electronics and chip manufacturing fields.

In recent years, as the rapid advancement of the electronic industry and the higher requirements for instruments and circuits, the absolute value circuit has found an increasingly wide utilization in digital signal processing. An absolute value detector is one of the most widely used algorithms used for detecting spikes. Based on traditional designs of absolute value circuits, many optimized designs with better performance have been proposed. Based on previous analysis and research, absolute value circuits can be optimized into different sructures to meet different application requirements. This paper introduces three different absolute value circuits for different uses in digital signal processing by comparing the optimized parts to traditional absolute value circuits and introducing how the optimization can meet the needs of different applications. These optimized absolute value circuit meet the requirements of higher precision and more stable temperature in biological signal processing field, more convenient to apply in the analog-to-digital conversion in ADC field and minimization the latency energy in chip signal processing field. Absolute value circuits can be optimized in different ways to be applied in more fields in the future.

With the continuous development and improvement of sensors, sensor technology is also constantly making breakthroughs. It is also widely used in various fields of medicine today. This article summarizes the practice of two types of SLAM (Simultaneous Localization and Mapping) in medicine. The detection and matching of human luminal feature points under SLAM were studied so as to reconstruct the human internal environment so that doctors can obtain three-dimensional reconstruction feedback within the surgical range and improve the safety of surgery. And for different surgical environments, the same SLAM is improved on the basis of innovation, which is more suitable for different scenarios. Three algorithms are described in this article for taking images of the inside of the abdominal cavity and collecting data based on the images using different algorithms. Based on the collected data, the internal abdominal cavity is reconstructed in 3D. Finally, a three-dimensional visualization system for the abdominal cavity was constructed. It has important research significance in the field of medical auxiliary research.

This paper presents the utilization of Simultaneous Localization and Mapping (SLAM) technology in medical endoscopic imaging. The fundamental components, hardware configuration, and process of SLAM are introduced in detail, with a focus on sensor acquisition, data preprocessing, feature extraction and matching, state estimation and update, map construction, and optimization modules. The application of SLAM in the medical field is then discussed, specifically highlighting real-time localization and reconstruction of endoscopic imaging. The integration of SLAM technology can assist doctors in accurately identifying the site of lesions, thereby enhancing surgical precision and safety. Furthermore, the paper introduces various commonly used SLAM algorithms, including the Kalman filter, extended Kalman filter, particle filter, optimization algorithms, among others. It emphasizes the significance of algorithm selection and optimization tailored to different scenarios and requirements. In conclusion, the application of SLAM technology in medical endoscopy imaging holds immense potential, offering improved accuracy and visualization during surgical procedures. This technology provides valuable support for doctors in diagnosis and treatment, ultimately enhancing patient outcomes.

Simultaneous Localization and Mapping (SLAM) technology has many applications, such as intelligent robots, autonomous driving, and smart cities. It is a key technology for achieving autonomous robot navigation and environmental awareness. Robots need to perceive the surrounding environment in real-time and construct maps while conducting autonomous localization and path planning, which involves algorithms of SLAM technology. This article aims to introduce the research on visual SLAM algorithms in dynamic scenes. This article will first introduce the traditional SLAM algorithm, its research status, and its limitations. Next, introduce the research on the visual SLAM algorithm in dynamic scenes. Finally, the current visual SLAM algorithm's problems and future research directions were discussed. The research in this article will have significant value for the research and application of autonomous robot navigation, environmental perception, and smart cities.

Due to developments in surgery, minimally invasive surgery (MIS) has become a common choice for surgical patients. Because of the small surgical incision in MIS, its postoperative effects will be less fat liquefaction, infection, splitting, numbness of the incision and weakness of the healing abdominal wall muscles compared to traditional surgery. However, many bottlenecks have been encountered in MIS, such as uneven illumination affecting the recognition of organ images. In other words, such problems are about improving the ability of Simultaneous Localization and Mapping (SLAM) to extract information about the environment. This paper summarises some relevant research on SLAM improvement methods. Scientists are now looking for more suitable methods for constructing maps of the human internal environment, including improvements on existing SLAM techniques. In the context of SLAM, what SLAM is and the history of SLAM is described. Traditional SLAM methods as well as SLAM improvement methods are also mentioned. Finally, its advantages and disadvantages are discussed, and its prospects are predicted.

EKF (Extended Kalman Filter) is a non-linear state estimation algorithm based on the development of Kalman filtering technology. In SLAM (Simultaneous Localization and Mapping), EKF technology is widely used to realize robots' independent positioning and map construction. EKF technology mainly estimates the state of the robot by using the information provided by the sensor, and then realizes the robot's self-positioning and the environment's map. This paper uses EKF algorithm for testing, and analyses the simulation results, which compared with Kalman filtering technology, the main difference between the two is that EKF can handle non -linear dynamic systems. In SLAM, robots often detect environmental detection through sensors such as laser radar, camera, and then update the position and posture of the robot by the receiving sensor data. In this process, the EKF technology can perform non -linearity by sensor data by sensor data Transformation makes the state of the robot more accurately estimate. Specifically, Extended Kalman Filtering technology can be divided into two steps: prediction and update. In the predicted phase, EKF uses the current robotic status and motion equation to predict the robot status of the next moment; in the update stage, EKF uses sensors to measure data to update the current state of the robot. By repeating the prediction and updating two steps, you can get the trajectory of the robot movement and the map of the environment where the robot is located.

This paper reviews the application progress of Simultaneous localization and mapping (SLAM) in complex indoor environments. SLAM is a technology used to manufacture mobile robots and autonomous vehicle. It can achieve autonomous localization and mapping in unknown environments. In dealing with complex indoor environments, SLAM technology faces many challenges, such as missing local perspectives, detecting moving objects, constructing dense maps, and real-time requirements in large-scale environments. However, with the development of technology, more and more SLAM technologies are being applied to complex indoor environments, among which the fusion of multimodal vision and deep learning technology, enhanced camera positioning technology, and navigation algorithms based on intelligent platforms are currently relatively advanced technologies. This article will briefly introduce the main development process of SLAM technology in dealing with complex indoor environments and the application of deep learning technology. By utilizing this technology, map features can be extracted more accurately, objects can be recognized, and obstacle avoidance can be achieved. This provides a good development direction for SLAM technology.

The Simultaneous Localization and Mapping (SLAM) method is widely used in the positioning and mapping of robots. In the medical field, SLAM is used in auxiliary medical robots and surgical robots. In endoscopic surgery, SLAM performs endoscopic positioning and scene graph construction for the surgical environment based on the information collected by the endoscope. For research on endoscopic SLAM, this article will first introduce the application of SLAM in endoscopic surgery in recent years. This paper summarizes the innovations and future work of relevant literature in recent years and identifies existing problems in SLAM in endoscopic surgery. Next, this article will introduce the combination of deep learning and SLAM in endoscopic surgery and list some specific applications. Finally, this paper will give a prospect for the future application of SLAM in endoscopic surgery. The research in this paper will be of great value to applying SLAM in endoscopic surgery and conducive to the development of future endoscopic SLAM.

As a combination of modern imaging technology, stereotactic technology, computer technology, and artificial intelligence technology, the surgical navigation system has been rapidly developed and applied in recent years. In recent decades, navigation systems have been used to aid in maxillofacial surgery, but rarely in the oral area. By comparing the applications of navigation systems in oral and maxillofacial surgery, we summarize their advantages and problems and list the advantages and disadvantages of the development process and methods of Simultaneous Localization and Mapping(SLAM). We propose to use tooth contours as natural markers to construct a model of AR oral structure, subtracting the operational time problems caused by making and using artificial markers. The AR structure model of the oral cavity was constructed, the method of applying SLAM technology to the oral surgery was proposed reasonably, and the method optimization scheme was proposed for the more complicated oral root canal surgery.

Gastrointestinal diseases are a relatively common disease, and the advent of endoscopic technology is important for the early detection and treatment of this disease. In order to adapt to current medical conditions, endoscopic robots that can move autonomously are known to be the trend of development. Most of the current capsule endoscopic robots use simultaneous localization and mapping SLAM technology to localize the capsule robot and create a map of the intestinal tract. his paper summarizes the classification of capsule endoscopy robots by referring to several articles on capsule endoscopy robots. Their working methods, advantages and disadvantages are analyzed by comparing the various types of capsule endoscopy robots horizontally. This paper also analyses the application of SLAM technology to capsule endoscopy robots. The future development of capsule endoscopy robot is prospected by combining the mainstream SLAM software and hardware technologies today. This provides the development direction for the future development of capsule endoscopy robot.

Minimal invasive surgery (MIS) is the mainstream trend in developing surgical technology. As the endoscope is a significant tool in the surgical process, whether it can track the inner cavity and realize accurate real-time 3D reconstruction has a vital impact on the smooth progress of MIS. However, there are still problems in the endoscopic environment, such as severe image distortion, the effect of lighting conditions, and the inability to extract lumen textures. Orinted fast and rotated brief simultaneous localization and mapping (ORB-SLAM) is currently a relatively advanced simultaneous localization and mapping (SLAM) method with better performance. The ORB-SLAM-based endoscope 3D reconstruction method can improve performance and overcome the challenge of endoscope 3D reconstruction. This paper will first introduce several existing endoscope 3D reconstruction methods based on ORB-SLAM and analyze the limitations and issues of these methods through their experimental results. Then the paper will explore the solutions to the defects in this method from other methods and compare the characteristics and the result of experiments. Secondly, through the summary of the above methods and the introduction of the integration of the ORB-SLAM-based methods and other current advanced technologies, the future development trend and huge development potential of ORB-SLAM-based endoscopic 3D reconstruction are introduced. This paper will be of profound affection to further improve the optimization and application of the ORB-SLAM-based endoscope 3D reconstruction method.

Wireless communication technology's quick advancement has facilitated the ongoing formation of numerous new communication business scenarios and the thorough connectivity of man-machine-object-space. The conflict between few channel resources and high demand is becoming more and more obvious, which adds to the stress on the entire network. In order to enhance communication quality with constrained frequency resources, more optimal channel allocation techniques are urgently required. This paper discusses the increasing demand for frequency resources due to the exponential growth of data traffic, which has necessitated more efficient channel allocation methods to optimize communication quality with limited frequency resources. The evolution of mobile wireless cellular communication systems from the first generation to the current fifth generation, and the imminent emergence of the sixth generation, are also discussed. The paper highlights the importance of ultra-dense networks and device-to-device communication in meeting the capacity and coverage requirements of 5G networks. The advantages and disadvantages of different frequency reuse methods, specifically Fractional Frequency Reuse (FFR) and water injection algorithm, are analyzed. The paper summarizes these methods' principles and application effects in specific situations, emphasizing the need for appropriate frequency reuse methods to achieve optimal channel allocation and communication quality.

In the 5G era, satellite communication has been widely applied in more scenarios. Orthogonal Frequency Division Multiplexing (OFDM) has been adopted to the 3GPP standard and is the primary sync encoding method for satellite communication. However, the OFDM-based system is extremely susceptible to carrier frequency offset (CFO), and Doppler shift make a major impact on the CFO. Therefore, the traditional methods of synchronization based on OFDM cannot meet the requirement of satellite communication with large frequency offset and delay. There are many studies focus on improving the methods of CFO estimation and compensation to adapt the technologies of synchronization based on OFDM to the satellite communication system. In this paper, ZC based and matrix-vector multiplication method, multi-symbol merging method and preamble and PSS symbols based and filter frequency locked loop-based method of synchronization of OFDM-based satellite communication will be introduced. The accuracy and complexity of these methods both meet the requirement of 5G satellite communication system. Future researchers may focus on reducing complexity and expanding application scenarios.

5G, the fifth generation of mobile communication systems, is expected to bring about a revolution in the world of wireless communication. Its key features include high data transmit rates, low latency, and high system capacity, which make it ideal for supporting a range of advanced technologies and applications. One of the most promising areas of application for 5G is in the development of smart cities. In this article, we provide an overview of the development of mobile communication systems from 1G to 5G, highlighting the key features and benefits of each generation. We also explore the specific features and applications of 5G that make it an ideal technology for building smart cities, including smart driving, smart healthcare, smart energy, and more. Additionally, we explain why 5G is critical for meeting the requirements of smart cities, such as the need for high-speed and reliable connectivity. Finally, we discuss the importance of 5G in constructing smart cities in the future, and the potential benefits that it could bring to society as a whole. With the potential to transform the way we live, work, and interact with our environment, 5G is set to play a critical role in shaping the cities of tomorrow.

The advancement of the Internet of Things (IoT) and Vehicle-to-Vehicle (V2V) communication networks has led to increased research efforts toward developing efficient and innovative V2V communication systems. These systems are designed to achieve the goal of improving road safety and enabling intelligent transportation. A critical aspect of this effort is channel modeling. In this context, this paper summarizes the characteristics of V2V communication channels. It categorizes them into three main types: Deterministic models, Non-geometry stochastic models (NGSMs), and Geometry-based stochastic models (GBSMs). It then reviews recent literature on these three types of channel modeling methods and discusses future development directions and challenges for V2V channel modeling. The study highlights the importance of accurate channel modeling for successful V2V communication systems and emphasizes the need to consider the unique features of V2V channels. It also identifies the limitations of existing channel models and suggests areas of research that can lead to more reliable and efficient V2V communication systems.

The last decade has seen breakthroughs in communication technology. The increasingly complex signal transmission environment has placed higher demands on signal modulation recognition. Traditional modulation recognition approaches cannot guarantee satisfactory recognition accuracy. Fortunately, with the continuous advancement of deep learning algorithms, convolutional neural network-based communication signal modulation recognition techniques have become the mainstream of current research. Therefore, this paper first reviews the development history of signal modulation recognition techniques and introduces the concepts of signal modulation theory. It includes ASK, PSK and FSK modulation methods, which are common today. Subsequently, I analyze the principles of signal modulation recognition and the implementation method of CNN in modulation recognition. To further explore the shortcomings of CNNs, I propose two optimized models, the residual network model and the CLDNN model. After comparing the performance, the former has higher performance, but its computational complexity is higher while the latter takes into account the high recognition accuracy while still reducing the network parameters as much as possible to keep the complexity at a low level.

Underwater communication technology has seen significant development in recent years, with a focus on finding efficient and secure techniques for exploring the underwater world. While acoustic and electromagnetic waves have limitations in water, the use of optical communication has become a focus of research due to its high bandwidth and low delay. Research has shown that blue and green lights are absorbed less than other wavelengths in water, making them suitable for underwater optical communication. In recent years, people have been working with optical communication in harsh environments. The optical system and the acoustic system have been combined to achieve certain development. Several high-speed underwater laser transmission systems have also been developed, with varying transfer rates and distances. This paper focuses on the theory of underwater optical systems and their communication channel characteristics.

With the rapid development of artificial intelligence, it is gradually applied to more and more fields. Ai’s market and technological potential value are unpredictable, which involves interaction between Ai and humans. In addition, optical character recognition is one of the essential roles in the interaction between machines and humans. Recognition of human language from a machine can significantly increase the user's experience and potential efficiency. Hence, this paper will focus on analyzing today’s Optical character recognition (OCR) techniques based on feature extraction and neural networks. This article first provides an overview of the important significance of English text recognition, and then selects two typical methods for overview. Then, comparative experiments are conducted on the above methods and their analysis is conducted. By comparing each technique, to decide the restriction and advantage, based on the analysis to conclude and make predictions in OCR optimization.

Unsteady flow is the dominant flow state in real life; thus, the simulation of it is of vital importance, especially in engineering, for example, the flutter or buffeting of the aerofoil. In the past decades, the progress in computational science greatly paced the development of computational fluid dynamics (CFD), providing powerful tools for simulating unsteady flow via numerical methods. However, the unsteady flow state depends on more variables than a steady flow, including the external conditions in different time moments and the flow's properties that vary with time. The calculation is still too massive, even using CFD. Therefore, CFD algorithms with higher efficiency and less reduction in accuracy are still needed to optimize the technique. This paper reviews the main CFD computational methods that have been maturely developed and proven effective, including direct numerical simulation (DNS), classic turbulence models and reduced order model (ROM), illustrating the main mechanisms and displaying their features. The paper also sheds light on these methods' latest research progress.