Search Results (37)

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included “urologic surgery”, “artificial intelligence”, “machine learning”, “neural network”, “automation”, and “robotic surgery”. Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ‘’master–slave’’ robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments. Full article

In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents. Full article

Space technology for small satellites has made significant progress in the academic and industrial fields, and an alternative focused on educational institutions is the CubeSat standard, created to promote various scientific projects of space exploration. In this context, a fundamental module of any satellite is the telemetry subsystem, which controls the communication with the Earth through electronic circuits dedicated to remote communication; also, the measurement and power supply modules are integrated into a CubeSat. Its construction costs range from USD 2500 to 55,000, with suppliers from Europe and the United States. This motivates the development of the present project, aimed at an academic–experimental CubeSat-1U prototype, to limit this technological dependence, focusing on the measurement generated by the acceleration sensors, angular velocity, magnetic fields, barometric pressure, temperature and ultraviolet light intensity, and the energization of each of them. For this, the main objective of the research is to identify the four basic subsystems of the CubeSat-1U: (a) energization subsystem, (b) sensing subsystem, (c) transmission and reception subsystem, and (d) control subsystem. To describe in detail the construction of (a) and (b), a set of diagrams is performed, defining their operation and its interaction. To explain the subsystem’s construction, the components selection and integration are presented. As a result, the electrical measurements generated by the power system, the output of the sensors in laboratory conditions, and images of the developed circuits are presented, having as a contribution to the methodology of design, integration, and development of the four subsystems, the feasibility of construction and its implementation in an academic satellite. Full article

In terms of manned aircraft, pilots usually detect icing conditions by visual cues or by means of ice detector systems. If one of these cues is seen by the crew or systems detect icing conditions, they have to apply the evasive procedure as defined within the aircraft flight manual (AFM). However, as regards unmanned aircraft, there are not pilots on board and, consequently, nobody can act immediately when icing conditions occur. This article aims to propose new techniques of sending information to ground which make possible to know the aircraft performance correctly in icing conditions. For this goal, three contributions have been developed for the unmanned aircraft Milano. Since icing conditions are characterized quantitatively by the droplet size, the liquid water content, and the total air temperature, when these parameters are between certain limits ice formation on aircraft may occur. As a result of these contributions, in that moment, high-quality images of the wing leading edge, tail leading edge and meteorological probes will be captured and sent to ground making possible that remote pilots or artificial intelligent (AI) systems can follow the appropriate procedures, avoid encounters with severe icing conditions and perform real-time decision making. What is more, as information security is becoming an inseparable part of data communication, it is proposed how to embed relevant information within an image. Among the improvements included are image compression techniques and steganography methods. Full article

The distribution of energetic ion flux in the ring current region, such as a meteorological cumulonimbus cloud, stores up the particle energy for a geomagnetic substorm. It is helpful to study the geomagnetic substorm mechanism by using a lunar base ENA imaging simulation of the dynamic evolution of the ring current, and establishing the corresponding relationship between key node events of the substorm. Based on the previous observation experience and our simulation results of the dynamic evolution of the ring current, we propose a macroscopic model of substorms related to the dynamic evolution of ring currents and present the possibility of confirming the causal sequence of some of those critical node events of substorms with the lunar base ENA imaging measurement. IBEX, operating in the ecliptic plane, may even give examples of the telemetry of ring current ion fluxes through ENA measurements during substorms/quiets. Full article

The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD09GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (µm) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD09GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 µg/m${}^3$, respectively. Computed LST values from MOD09GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system. Full article

Industry 4.0 has considerably advanced multiple manufacturing fields through digitalization and intelligentization. Many technologies, such as supervisory control, data acquisition, and data analytics, have been used widely in manufacturing sites to enhance production efficiency. Therefore, this created a cloud-based anomaly detection module for epidemic prevention at the manufacturing site. Image process technologies, deep learning algorithms, and cloud computing were employed in the proposed module to automatically identify labor anomaly behavior in the manufacturing site and prevent the epidemic. This study used image processing technologies and deep learning to recognize and train the manufacturing site image. Accordingly, the analyzed result could be incorporated into the cloud system using the Message Queuing Telemetry Transport (MQTT) protocol. Therefore, the administrators and laborers can be notified regarding the anomaly behavior. The author used the image data obtained from the cylinder head process site as a data source for DA. As per the experimental results, the proposed method has an accuracy of 90%. Therefore, deep learning algorithms provide a practical approach to anomaly detection for epidemic prevention. Furthermore, this study’s primary contributions are designing an improved approach and connecting the manufacturing site to the cloud side using the proposed module. Full article

It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research. Full article

Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care. Full article

This paper proposes an indoor location-based augmented reality framework (ILARF) for the development of indoor augmented-reality (AR) systems. ILARF integrates an indoor localization unit (ILU), a secure context-aware message exchange unit (SCAMEU), and an AR visualization and interaction unit (ARVIU). The ILU runs on a mobile device such as a smartphone and utilizes visible markers (e.g., images and text), invisible markers (e.g., Wi-Fi, Bluetooth Low Energy, and NFC signals), and device sensors (e.g., accelerometers, gyroscopes, and magnetometers) to determine the device location and direction. The SCAMEU utilizes a message queuing telemetry transport (MQTT) server to exchange ambient sensor data (e.g., temperature, light, and humidity readings) and user data (e.g., user location and user speed) for context-awareness. The unit also employs a web server to manage user profiles and settings. The ARVIU uses AR creation tools to handle user interaction and display context-aware information in appropriate areas of the device’s screen. One prototype AR app for use in gyms, Gym Augmented Reality (GAR), was developed based on ILARF. Users can register their profiles and configure settings when using GAR to visit a gym. Then, GAR can help users locate appropriate gym equipment based on their workout programs or favorite exercise specified in their profiles. GAR provides instructions on how to properly use the gym equipment and also makes it possible for gym users to socialize with each other, which may motivate them to go to the gym regularly. GAR is compared with other related AR systems. The comparison shows that GAR is superior to others by virtue of its use of ILARF; specifically, it provides more information, such as user location and direction, and has more desirable properties, such as secure communication and a 3D graphical user interface. Full article

Environmental monitoring, public safety, safe production, and other areas all benefit greatly from the use of gas detection technologies. The infrared image of a gas could be used to determine its type from a long distance in gas detection. The infrared image can show the spatial distribution of the gas cloud and the background, allowing for long-distance and non-contact detection during safety production and hazardous chemical accident rescue. In this study, a gas detection system based on multispectral infrared imaging is devised, which can detect a variety of gases and determine the types of gas by separating the infrared radiation. It is made up of an imaging optical system, an uncooled focal plane detector, a filter controller, and a data gathering and processing system. The resolution of the infrared image is 640 × 512 and the working band of the system is 6.5~15 μm. The system can detect traces of pollutants in ambient air or gas clouds at higher concentrations. Ammonia, sulfur hexafluoride, methane, sulfur dioxide, and dimethyl methyl phosphonate were all successfully detected in real time. Ammonia clouds could be detected at a distance of 1124.5 m. Full article

We doubt whether the “Energetic Neutral Atom (ENA) ribbon” signals, especially the peak ones, scanned remotely by IBEX-Hi at the lunar resonance orbit, are really from the heliopause, which involves assessing the scale of solar wind particle energy loss throughout the solar system. The ENA imaging simulation results at the Earth’s orbit show that the scale of the planetary magnetosphere with a telemetry distance of AU magnitude is too small to contribute to the IBEX-Hi ribbon. However, the simulated effective ENA differential fluxes provide a reference for the physical scale evaluation of the huge magnetic structure in the heliopause. The ENA differential flux of the “ENA emission cone” generated by the charge exchange between the solar wind ion flow and local neutral gas near the Earth’s orbit is also comparable to the measured peak of the IBEX-Hi ribbon, which may be the main ENA emission source of the ribbon’s measured peak. The 2D ENA imaging measurements at the Lagrange points proposed here can be used to investigate the ENA ribbon origination by using the energy spectral lag vs the disparity of the ENA images. Full article

Since unqualified products cause enterprise revenue losses, product inspection is essential for maintaining manufacturing quality. An automated optical inspection (AOI) system is an efficient tool for product inspection, providing a convenient interface for users to view their products of interest. Specifically, in the screw manufacturing industry, the conventional methods are the human visual inspection of the product and for the inspector to view the product image displayed on the dashboard of the AOI system. However, despite the inspector and the approach used, inspection results strongly depend on the inspector’s experience. Moreover, machine learning algorithms could improve the efficiency of human visual inspection, thus addressing the above problem. Based on these facts, we improved anomaly detection efficiency during product inspection, using product image data from the AOI system to obtain valuable information. This study notably used the visual geometry group network, Inception V3, and Xception algorithms to detect qualified and unqualified products during product image analytics. Therefore, we considered that the analyzed results could be integrated into a proposed cloud system for human–machine interaction. Thus, administrators can receive reminders concerning the anomaly-inspected notification through the proposed cloud system, comprising a message queuing telemetry transport protocol, an application programming interface, and a cloud dashboard. From the experimental results, the above-mentioned algorithms had more than 93% accuracy, especially Xception, which had a better performance during the defective type classification. From our study, the proposed system can successfully apply the obtained data in data communication, anomaly dashboards, and anomaly notifications. Full article

Virtual reality (VR) simulators enable the evaluation of engineering systems and robotic solutions in safe and realistic environments. To do so, VR simulators must run algorithms in real time to accurately recreate the expected behaviour of real-life processes. This work was aimed at determining a suitable configuration for processing images taken from a virtual unmanned aerial vehicle developed in Unity using OpenCV. To this end, it was focused on comparing two approaches to integrate video processing in order to avoid potential pitfalls such as delays and bottlenecks. The first approach used a dynamic link library (DLL) programmed in C++, and the second an external module programmed in Python. The native DLL ran internally on the same Unity thread, as opposed to the Python module that ran in parallel to the main process and communicated with Unity through the Message Queue Telemetry Transport (MQTT) protocol. Pre-transmission processing, data transmission and video processing were evaluated for a pair of typical image-processing tasks like colour and face detection. The analysis confirmed that running the Python module in parallel does not overload the main Unity thread and achieves better performance than the C++ plugin in real-time simulation. Full article

Guaranteeing security in information exchange is a challenge in public networks, such as in the highly popular application layer Message Queue Telemetry Transport (MQTT) protocol. On the one hand, chaos generators have shown their usefulness in masking data that can be recovered while having the appropriate binary string. Privacy can then be accomplished by implementing synchronization techniques to connect the transmitter and receiver, among millions of users, to encrypt and decrypt data having the correct public key. On the other hand, chaotic binary sequences can be generated on Rapsberry Pis that can be connected over MQTT. To provide privacy and security, the transmitter and receiver (among millions of devices) can be synchronized to have the same chaotic public key to encrypt and decrypt data. In this manner, this paper shows the implementation of optimized chaos generators on Raspberry Pis that are wirelessly connected via MQTT for the IoT protocol. The publisher encrypts data that are public to millions of interconnected devices, but the data are decrypted by the subscribers having the correct chaotic binary sequence. The image encryption system is tested by performing NIST, TestU01, NPCR, UACI and other statistical analyses. Full article