The conventional methods for indoor localization rely on technologies such as RADAR, ultrasonic, laser range localization, beacon technology, and others. Developers in the industry have started utilizing these localization techniques in iBeacon systems that use Bluetooth sensors to measure the object’s location. The iBeacon-based system is appealing due to its low cost, ease of setup, signaling, and maintenance; however, with current technology, it is challenging to achieve high accuracy in indoor object localization or tracking. Furthermore, iBeacons’ accuracy is unsatisfactory, and they are vulnerable to other radio signal interference and environmental noise. In order to address those challenges, our study focuses on the development of error modeling algorithms for signal calibration, uncertainty reduction, and interfered noise elimination. The new error modeling is developed on the Curve Fitted Kalman Filter (CFKF) algorithms. The reliability, accuracy, and feasibility of the CFKF algorithms are tested in the experiments. The results significantly show the improvement of the accuracy and precision with this novel approach for iBeacon localization. Indoor localization technology is a crucial area of study within the realm of Wireless Sensor Networks (WSN) ,2,3. CurA developed KF state estimate algorithm based on the modified Least Squares Algorithm (LSA) is proposed to enhance the accuracy of iBeacon measurement distance;A developed system calibration process has been proposed to calibrate the RSSI and estimated distance for indoor localization using iBeacon;A novel CFKF error modeling approach has been proposed to enhance the estimation accuracy of iBeacon systems in field experiments.This paper introduces novel error modeling algorithms for indoor localization. Based on a CFKF error-modeling filter-adjustment strategy, the proposed methodology reduces measurement errors in the iBeacon localization system. The CFKF error modeling algorithm considers iBeacon sensor calibration, improving the measured data through error modeling. This research compares the proposed CFKF error modeling with recent existing error filtering methods according to accuracy and precision. The experimental results show that the CFKF error modeling approach provides the most accurate and precise results for iBeacon localization. The main contributions of the article are:The paper is structured as follows: The iBeacon system utilizes a localization technique with the BLE method to determine the objects’ locations . The disThe iBeacon employs an identifier in 128-bit known as UUID for transmitting its information. The UUID holds information about the organization that the iBeacon belongs to, thus identifying the company that it is associated with . In the The Minor number is employed to distinguish a lower-level group within a set of iBeacons. As an illustration, in a retail chain, it can be utilized to recognize a specific item within a single shop .The Major number, as specified in Bluetooth and iBeacon specifications, is utilized to identify the ID of a particular class of iBeacons that belong to a particular body. As an illustration, in a retail chain, it is utilized to distinguish the particular shop in which a particular class of beacons is situated .The advertising interval is typically set to 100 milliseconds in Bluetooth. While extending the advertising interval can increase battery life, it also decreases the frequency of signal transmission in communication, requiring a balance to be struck between the length of the advertising interval and the intended usage .The range measurement accuracy in iBeacon depends on the calibration constant, “measured single power”. Smartphones use the constant to determine the RSS. It is assumed that the iBeacon is located in free space, with signal loss primarily impacted by inverse-square fading; however, in real-world experimentation, the measured range experiences fluctuations due to interference caused by multiple paths. When the RF signal is transmitted through multiple pathways, the radio energy can interfere with other radio transmissions. This interference can either strengthen the signal (constructive interference) or weaken it (destructive interference). Multipath interference often occurs when the iBeacon is placed near the ceiling or a barrier, causing the radio signal to reflect when it hits the surface.As shown in We need to know the values of The next step after obtaining the estimated data is to plot it on a Matlab figure. The system then calculates the error rates, revealing that the CFKF algorithm is the most accurate and precise among the others. This completes the calibration stage. To calculate the distance between the iBeacon and the mobile receiver at a value other than 1 m using the received To calculate a distance different from 1 m using the In For optimal compatibility with the Least Square Algorithm (LSA), the algorithm is initialized with Here, the KF State Update Estimate equation is modified by incorporating the Least Square Algorithm (LSA):At this stage, the distance measurement values are calculated using Equation (3) after processing and calibrating the raw data collected by the mobile receiver using the CF algorithm to determine According to the derivation of LSA:Therefore,The new State Update equation for KF is formulated as follows:The Kalman Gain equation:A covariance update equation is presented below:The experiment for the iBeacon localization system has been conducted in this section. The initial step was to calibrate the system, and multiple algorithms were evaluated to determine the most effective one for maximizing localization accuracy. The measurement phase was then carried out to determine the estimated distance. Finally, an experiment was conducted in a real-life environment. The results indicated that the error modeling using CFKF provided the highest accuracy for the system.The experiment utilized Estimote iBeacons, and their specifications can be found in The specifications of the smartphone can be found in As shown in The datasets received by the mobile phone from the iBeacon can be found in the repository . The datCF error modeling for raw data (CF-RSSI):−5, the mean of the range between −0.000246 and 0.000198) is computed using Equation (11),The algorithm bias and Mean Absolute Error (MAE) in From We repeated the calibration processes from 1 m to 15 m. Same as the previous calibration, 1500 raw data samples were collected and processed for each distance. The calibrated RSSI values have been recorded in As illustrated in In this scenario, the measured distance was determined using Equation (3), and the CF and CFKF error modeling is processed using Equations (11) and (21), respectively. The results are presented as follows:CF error modeling for the distance (CF-Distance)CFKF error modeling for the distance (CFKF-Distance):The comparison of the results between the KF algorithm and the CFKF algorithm can be seen in As demonstrated in According to At this stage, we set up the experiment to validate the feasibility of our error modeling in an ample area, as illustrated in In this case, we repeated the calibration and calculation process with 1000 received data sets. The comparison of the results among the multiple algorithms is shown in the distance diagram in As presented in According to In this study, the development and experimentation of CFKF error modeling in iBeacon localization research have been carried out. Firstly, the experimental setup was established, and the iBeacon was positioned a meter away from the mobile receiver during the distance measurement error modeling calibration. The first step was to utilize the CF to process the raw data and calculate the measured power at 1 m. Subsequently, the measured distance was calculated by the distance equation using the received RSSI. To verify the accuracy, various algorithms were tested in the field experiments, with the results indicating that the CFKF error modeling provided the best precision and accuracy among all of them. This CFKF approach can be utilized in RSSI-based iBeacon/Bluetooth localization. In future research, machine learning algorithms could be combined with CFKF error modeling to improve localization performance. The state-of-the-art literature introduces machine learning algorithms based on RSS fingerprinting methods ,43. Our


Preference behavior can be an important indicator of animal welfare. The effects of different wavelengths of light on laying hens are not completely known. We performed a preference test with few laying hens with three different light sources  in three thermal environmental conditions . We recorded video footage during the entire period of the experiment and automatically estimated the permanence of the birds in each room of the Environmental Preference Chamber using YOLO v4. The results showed that birds prefer white and red light under thermal comfort conditions and do not show preference under heat stress conditions.As for all birds, the behavior of chickens is largely determined by environmental conditions. In many production systems, light intensity is low and red feather strains have low contrast with the background, making it impossible to use conventional image segmentation techniques. On the other hand, studies of chicken behavior, even when using video camera resources, depend on human vision to extract the information of interest; and in this case, reduced samples are observed, due to the high cost of time and energy. Our work combined the use of advanced object detection techniques using YOLO v4 architecture to locate chickens in low-quality videos, and we automatically extracted information on the location of birds in more than 648 h of footage. We develop an automated system that allows the chickens to transition among three environments with different illuminations equipped with video cameras to monitor the presence of birds in each compartment, and we automatically count the number of birds in each compartment and determine their preference. Our chicken detection algorithm shows a mean average precision of 99.9%, and a manual inspection of the results showed an accuracy of 98.8%. Behavioral analysis results based on bird unrest index and permanence time indicate that chickens tend to prefer white light and disfavor green light, except in the presence of heat stress when no clear preference can be observed. This study demonstrates the potential of using computer vision techniques with low-resolution, low-cost cameras to monitor chickens in low-light conditions. Illumination is a key environmental factor impacting egg production, as it alters the bird’s physiology, behavior, and well-being ,2. LightWhite light is commonly used for rearing laying hens ,10 becauIt is possible to monitor the behavior of chickens in different environmental conditions using video cameras. These behaviors can be analyzed either manually or automatically, using computer vision techniques and deep learning models for object detection . TraditiNew tools based on machine vision are being developed with the aim of extracting information on the behavior of birds automatically in recorded videos. Sudebi et al.  developeCommercial laying aviaries normally operate with low illumination levels. Red hues, more common in cage-free production systems, provide low contrast between the bird and the background of the image in these low-light conditions, making it difficult to recognize the birds using computer vision techniques. The bird detection model developed by Siriani et al. , based oOur hypothesis is that laying hens have preferences for specific wavelength of illumination and that the ambient temperature can affect this preference behavior. Thus, we use the bird detection system proposed by Siriani et al.  to analySix H&N Brown Nick layer hens, 35-weeks old at the beginning of the pilot experiment, were housed in an Environmental Preference Chamber (EPC) at the Faculty of Agricultural Engineering of the Campinas State University. The EPC comprises three mutually insulated compartments, measuring 1.6 m × 1.4 m × 3.0 m (L × W × H), enriched with a wood shavings bed (5 cm deep), a perch (60 cm length and 30 cm height), and a nest. The compartments are interconnected through automatic doors that allow the birds to move freely and choose their preferred environment, as shown in After the training period, the birds were subjected to treatments with different light colors and ambient temperature, according to the experimental design shown in ® iM5, São José, SC, Brazil). We installed one ceiling-mounted camera at the center of each EPC compartment with the camera optical axis perpendicular to the floor of the compartment so that the field of view of the camera covers the entire floor area of its respective compartment.The behavior of the birds was recorded using low-cost video cameras with a resolution of 702 × 480 pixels and a capture rate of 30 fps  single-stage framework for object classification and detection ,33,34. Un is the sample size, The number of frames used to train our model was defined using the sample size estimation strategy proposed by Agranonik and Hirakata , which iwww.makesense.ai, accessed on 10 March 2021) to delimit a rectangular region representing the space occupied by birds in the video frames and to manually annotate the bounding boxes corresponding to each bird, as illustrated in We selected 1041 random frames containing birds to compose our training dataset. Our validation dataset consisted of 200 additional frames selected at random from the original videos. −4, and a multi-step learning rate scheduling policy, with an initial learning rate of 1.3 × 10−3, which is divided by 10 after 8000 and 9000 iterations. The performance of this model applied to chicken detection within the EPC compartments is discussed in Our object detection system is based on the Python implementation of the YOLO v4 model provided in the Darknet framework . We modiWe assess the effectiveness of the YOLO v4 model based on its mean average precision (mAP) on the 200 frames of our validation set. The mAP is a widely used metric to assess the accuracy of detection models based on the YOLO network . It is cWe further validate the model by manually inspecting an additional 299 randomly selected scenes. Each scene comprises one video frame from each EPC compartment, for a total of 897 synchronized frames. Since the video frames are acquired simultaneously, the same bird may occasionally be partially observed in two cameras, as illustrated in Upon validation, we used the YOLO v4 model to detect chickens in the 648 h of videos acquired in the EPC compartments. These detections were used to estimate the mobility of the birds according to two criteria: unrest index and permanence time.Del Valle et al.  developejt is the time of permanence of the m is the number of birds observed in the compartment during all the recordings of one experimental scenario described in To further assess the mobility of the chickens, we also calculate the cumulative time spent by the birds in each compartment, according to:Since our algorithm is unable to differentiate individual birds, dwell time was approximated by summing the total number of chickens identified in each frame, divided by the capture rate of 30 frames per second. The result is the total time in seconds that the chickens spend in the video. Given the high frame rate of the videos collected in our experiments and the high precision of the bird detection model, we expect this approach to accurately estimate permanence time. Differences between the total length of stay of the birds were tested among the different light and temperature treatments using one-way ANOVA and later compared using Tukey’s mean test at a significance level of 5%.Illumination exerts an important influence on the behavior and welfare of birds. Our work analyzed the behavior of laying hens in more than 35 million frames or 648 h of recorded videos, positioning itself as one of the first studies to use computer vision to extract knowledge about the preference of hens to different light treatments. Our measured bird detection accuracy was 99%, and we used low-quality images obtained by low-cost cameras.Several studies have evaluated the behavior of chickens using video cameras, but analysis always depends on human observation. Mendoza et al.  verifiedThe use of low-resolution images can be a viable option for environments where capturing high-quality images is not possible, either due to equipment or budget limitations. This approach can be applied in different areas of animal monitoring. One of the main advantages of using low-resolution images is the reduction in image file size, which makes storing and sharing collected data easier. This is especially useful in environments with limited bandwidth for data transfer, such as remote or hard-to-reach areas. One more benefit is the low-cost of capture equipment and the low-cost of computers for processing these data.However, it is important to note that using low-resolution images can affect the accuracy of object detection, especially in situations where the objects to be detected are small or have complex features. Therefore, it is necessary to carefully evaluate the limitations and advantages of using low-resolution images in each specific context. In summary, the bird detection model in low-resolution images is an effective and economical solution for animal monitoring in real environments. The application of advanced image processing and machine learning techniques can maximize detection accuracy, allowing the implementation of more animal monitoring projects in different parts of the world.The experimental results indicate that chicken have a preference for white light, followed by red, and lastly green. According to Govardovskii and Zueva  and HartIn all the scenarios under consideration, green light was the least preferred by the birds. These results are similar to those presented by Rierson , who alsThermal discomfort seems to prevail over lighting preference for chickens. In the cold environment, the birds express a clear preference for white light, concentrating in groups of three or four birds in the EPC compartment illuminated with white light. On the other hand, under heat stress, the birds were equally distributed among the light treatments in groups of two birds per compartment, on average. Red lighting has the potential to reduce stress in laying hens and reduce the occurrence of feather pecking and cannibalism ,14,52, wOurs study is limited to an EPC experiment with few animals, allowing assessment of the preference behavior of few chickens. However, we were able to accurately assess flock preference in long footage, using a computer vision system based on deep learning, allowing us to confirm our initial hypothesis. The results presented correspond only to these chickens and to the conditions described in the experiment and can subsidize other studies of preferred behavior both in the method of analysis approached and for the comparison of the results shown.We present a high-performance system to automatically monitor the behavior of layer hens using computer vision techniques. We detect the birds using the YOLO v4 model with nearly perfect accuracy and use these automatic detections to determine the distribution of birds among compartments over 648 h of footage, surpassing any previous behavioral assessment experiment ever published. Our behavior preference analysis revealed that birds prefer white light, followed by red, under the experimental conditions described in this article. This study also indicates that chickens generally avoided green light and were more agitated under red illumination. However, temperature influenced the behavior of the birds more than the color of the light source. Under heat stress, the birds preferred to move away from one another, evenly splitting between the three light treatments. Under cold stress, on the other hand, the birds concentrated more on the chamber with white illumination.This study indicates that light color may be an important environmental factor influencing the well-being and consequently the productivity of layer hens. Most importantly, this work highlights the importance of techniques to mitigate thermal stress, whose impact is significantly more prevalent than light color differences. Overall, our findings shed light on the interplay between temperature and illumination conditions as potential stressors for commercial birds. Hence, integrated management strategies for commercial layer chicken operations should be applied. We demonstrated the technical viability of using low-resolution standard security video cameras  in poor lighting conditions, in a high-performance computer vision system that allowed us to accurately study the preference behavior of chickens.


Schizophrenia is a severe mental disorder characterized by negative  and positive  symptoms. Schizophrenia prodromal symptoms typically emerge during the adolescence. These symptoms consist of attenuated and/or intermittent psychotic symptoms and define the ultra-high risk (UHR) states of schizophrenia. Approximately, 30% of UHR individuals develop schizophrenia. It is important to find biomarkers associated with the conversion of schizophrenia in UHR individuals in order to develop preventive and therapeutic strategies, as well as to gain knowledge in the ethiopathology of the disorder. In this regard, epigenetic variations have been identified as potential biomarkers associated with the conversion to schizophrenia, but the lack of research prevent to draw final conclusions.The objective of this communication is to report the study protocol of the research project named “Epigenetic variations associated with the conversion of schizophrenia in ultra-high risk adolescents”. Its aim is to analyze the epigenetic marks that predict the development of schizophrenia in UHR youths.A 4-year observational follow-up study will be conducted, assessing three groups of adolescents who have sought clinical hep: 1) UHR group composed of individuals who have developed schizophrenia at the end of the follow-up; 2) UHR group composed of individuals who did not develop schizophrenia at the end of the follow-up; 3) a group composed of non-UHR individuals. Epigenetic marks will be analyzed in the peripheral blood of all the participants each 6 months. Clinical  and sociodemographic data  will be also assessed through standardized questionnaires.We hypothesize that specific epigenetic marks will predict the conversion of schizophrenia in the UHR group. Moreover, interactions between epigenetic variations and sociodemographic data will differentiate the three groups after the follow-up.The findings of this project will help to develop preventive and therapeutic strategies, as well as to gain knowledge in the etiopathological pathways of schizophrenia.None Declared


The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of ‘smart farming’. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food safety, and efficiency in the agri-food supply chain. This paper presents a customized smart farming system that uses a low-cost, low-power, and wide-range wireless sensor network based on Internet of Things (IoT) and Long Range (LoRa) technologies. In this system, LoRa connectivity is integrated with existing Programmable Logic Controllers (PLCs), which are commonly used in industry and farming to control multiple processes, devices, and machinery through the Simatic  The fourth industrial revolution, commonly known as “Industry 4.0”, has emerged as a hot topic of research and discussion among industry and academia, especially in the fields of management and engineering ,2. IndusIn agriculture, relying on traditional farming methods to meet the growing demand for food is no longer sufficient. The production process of planting, sowing, reaping, irrigation and cultivation must now respect certain climatic conditions, such as air temperature, humidity, and precipitation. These conditions affect the spread of pests and diseases, which cause significant losses in global food production . In factTo address these issues, smart agricultural and farming systems that integrate IoT and communication models are being introduced. These systems automate farm operations in a collaborative and intelligent manner, improving the reliability of crop production management. They provide better control of planting conditions and natural resource utilization without human interaction. The smart farm environment enables decision making by gathering information from different sensors and analyzing it according to needs. The automation of certain processes using IoT sensors, communicating devices, control units, and computers replaces manual work schedules and improves production. As smart farming systems help the interoperability of multiple heterogeneous devices, information sharing and processing is important for controlling the operations of the farm. Multiple sensors are deployed in the smart farm such as humidity and temperature sensors forming a Wireless Sensor Network (WSN). These sensors collect the environment information and send it to the server where data are processed. To deploy a large-scale WSN, Low-Power Wide-Area Network (LPWAN) technologies such as LTE, SigFox and LoRa are better suited due to their wide transmission range, scalability and low power consumption ,9. LoRa This paper presents a novel approach to integrating LoRaWAN communication with traditional Programmable Logic Controllers (PLCs), which have long been used in agriculture for automating various processes and controlling machinery. The proposed system facilitates the creation of smart farms by enabling the integration of LoRa connectivity with the existing automated processes that have been in use for decades, without the need for replacing the old control systems. To accomplish this integration, the proposed system employs a LoRa shield on a Simatic This innovative approach has the potential to significantly benefit the agriculture industry. By incorporating LoRa connectivity into the existing PLCs, the system can leverage previously automated processes and thus reduce the need for expensive replacement of old control systems. The remote management capabilities can help reduce labor costs and increase productivity. In addition, the real-time monitoring of climatic conditions, such as temperature and humidity, can assist in detecting and preventing the spread of pests and diseases, thereby contributing to the overall improvement of global food production.Certainly, recent studies have highlighted the growing interest in using LoRa technology for smart farming applications. Placidi et al. demonstrated in  the use A smart farm can be defined as a new type of automated farming system using IoT infrastructure. Our proposed system consists of four main parts: the end-node sensors, an IoT LoRaWan gateway, the control equipment, a cloud server hosting a web-based platform for control and monitoring, and a bot for a Telegram messaging application in mobile devices. In this section, we present a brief overview of LoRa and LoRaWan, and then we describe the proposed smart farm system.LoRa and LoRaWAN are global de facto standards of Low-Power Wide Area Networks (LPWANs), with LoRa being the physical layer and LoRaWAN the Media Access Control (MAC) layer. LoRaWAN is an open specification developed by the LoRa Alliance . LoRa teNetwork layer: an AES-128 secret key named network session key (NwkSKey) is shared between the end-device and the network server for authentication.Application layer: an AES-128 secret key named application session key (AppSKey) protects the payload transmission between end-devices and the application server.The network of LoRa uses encryption, integrity and authentication and it is secured with two security layers :Network In general, the architecture of a LoRa-based network consists of a hierarchical topology, formed by LoRa nodes, gateways network servers, and application servers. The devices can transmit the data to the gateways which belong to the same LoRaWan network. However, all gateways within the range of a LoRa node can receive messages and the duplications (when existing) will be filtrated in the network server responsible of processing the incoming packets.The monitoring network (Farm): a wireless network of different LoRa sensors distributed over the farm to collect to the required information such as moisture and airflow sensors.The control network (Warehouse): The reaped vegetables and fruits are stored in the warehouse. Controlling the climatic conditions of this place is a must, thus it is equipped with different environmental sensors . The warehouse contains all the control equipment such as air conditioner and irrigation water pump. These devices are controlled with a PLC.The proposed smart farm system consists of two main networks as described in The LoRa sensors scattered in the farm send the sensed information  to the LoRa gateway. The gateway is connected to the internet and it is responsible for forwarding the sensor data to the cloud server to be further analyzed and stored in the database. The gateway is also responsible of receiving the control commands from the server and forward them to the PLC located in the warehouse. These commands can be manually instructed by the user through the web-based platform or automatically executed if the sensed received data require an action . The automatic commands can be set and configured by the user in the web platform by defining the target device and the threshold values that trigger the actuating device. For better communication coverage, the LoRa gateway should be placed in a high altitude and all the LoRa end-nodes have to be distributed properly.In IoT, a gateway acts as a bridge between devices and the cloud or server. The gateway is responsible for collecting data from sensors and devices, processing it, and sending it to the cloud or server for storage and analysis. Here are some of the gateway protocols used in IoT such as MQTT, CoAP, HTTP and DDS . In the  Siemens  to a regnode-red-node-arduino” package in order to read the LoRa payloads exchanged between the node-red-contrib-s7” package is used to read/write commands to the Siemens S7 PLC via Modbus-TCP . A part of the Arduino code and the NodeRed scheme of the smart farm are represented in The Siemens Simatic Water pump and associated valves: controls the amount of water needed for irrigation.Air conditioner and fans for ventilation and air quality, maintaining an adequate level of temperature and humidity to preserve food products.Intrusion alarms.Lighting: on/off programming for presence simulation and night activity.LoRa Temperature sensor.LoRa Humidity sensor.The PLC in the warehouse controls the following elements:The Siemens S7 PLC is controlled using the https://telegram.me/BotFather accessed on 2 February 2023). There are steps that must be completed in /newbot. A bot token was used to communicate with our system via Telegram once the bot was created.In this system, a bot was used to integrate Telegram instant messaging using Telegram Bot API in both the node-red-contrib-telegrambot”. This integration aims to receive direct control commands from users, for example, turning on the lights. The Telegram node was programmed with a JSON code to define several commands and their actions. Thus, the user can send his command via telegram, the The first integration of the Telegram bot with the Telegram Bot API—PHP SDK , wh, wh28], d is the distance between the receiver and the transmitter.The ratio between the received power Equation ), where For a non-free space environment, a path loss exponent Equation  becomes Equation .(4)PrPtUsing the first-order fit , PL can The vailable . Typicalvailable . There mEquation . When prIn the previous test, measurement points with RPP below 50% are ignored. The Received Signal Strength Indicator (RSSI), the signal-to-noise ratio (SNR) and the percentage of the received packets (RPP) corresponding to each measurement point are represented in The RSSI and SNR variation as a function of the distance are illustrated in Using the experimentally collected data of As a result, the PL model parameters are typically only valid in specific environments, frequency ranges, and antenna configurations. Thus, for similar condition to our experimental test, the proposed PL estimation model can be applied in the design of a LoRa mesh network in a farm to improve the energy efficiency and reliability over existing LoRa systems. LoRa is designed to operate at very low power levels, which is one of the reasons why it is able to achieve long-range communication. However, this also means that the signal is more susceptible to interference and noise as it propagates through the environment, which can cause a sudden and significant increase in PL. The LoRa signal may encounter a variety of environmental factors that can cause a sudden increase in path loss, such as obstacles  and interference from other wireless devices. These factors can cause the signal to be absorbed, scattered, or reflected in different directions, resulting in a significant increase in PL.The transmission range of LoRa in agriculture is an important factor to consider when implementing smart farming solutions. The range of LoRa depends on various factors, such as the frequency used, transmission power, terrain, and obstacles present. In general, LoRa has been found to have a transmission range of several kilometers in rural areas and up to several hundred meters in urban environments. This range is suitable for most smart farming applications, which typically cover a large area . HoweverRecent studies have demonstrated the potential of LoRa for smart farming ,32. ThesIn conclusion, LoRa is a promising technology for smart farming applications. It offers long-range, low-power communication that can enable the collection of data from various sensors and devices over a wide area, making it an ideal solution for precision agriculture.The presented smart farm system requires a tailored application to customize the control and analysis of data and provide more flexibility in its management. To meet this need, we have developed a personalized web application called ‘Mi granja (My Farm)’, using the Laravel framework  for backIn Laravel, the Model-View-Controller (MVC) architecture is used to separate the application logic from the presentation layer. The work process of the MVC architecture. Routing is the process of accepting a request and directing it to the appropriate controller is described in The LoRa gateway communicates with the TTN server, which will forward the received payloads directly to our cloud server to be stored in the database, so that it can be visualized and monitored from the user interface. The user commands also take the same path: they are transmitted to the TTN server and then forwarded to the LoRa gateway, which will finally communicate with the LoRa end-node. The web application allows different users to create their accounts, each user can register several networks corresponding to his associated farms (multiples farms can be managed from the same web interface), and each network  contains a number of connected devices. In order to register a device in the network, the user should register this device in the TTN server first, and then register it in the dashboard page of the web application using its identifier, because all the data go through the TTN LoRaWan network before receiving/sending it to our cloud server.The home page and dashboard of the web application is illustrated in Scalable design, light weight and fast response.Strong security thanks to the MVC design and authentication system.Multi-device experience: users can access to the web application from various devices.Instant messaging: users receive notifications via email and Telegram.Different charts and graphs for Data visualisation.Organized data storage and easy searching.Export data for advanced analyses as Excel and CSV files.The web application is hosted in a cloud server and its main features are:This paper outlines the development of a comprehensive IoT system for a smart farm. The system aims to improve food product storage by monitoring and regulating humidity and temperature levels. It also enables remote monitoring and control of various devices through a web-based application, automating tasks such as irrigation and temperature adjustments.The developed web application is designed to simplify the process of creating and managing IoT networks. It provides users with the ability to add devices and access to data analysis and downloading features. The use of the MVC design pattern makes the application easy to modify and update, ensuring scalability and the ability to add new features as needed. The responsive design of the app makes it accessible on different screen sizes, ensuring a seamless user experience on any device. The low server resource consumption allows the app to support a large number of concurrent users, making it ideal for managing multiple sensor networks. The introduction of new additions to the traditional LoRa IoT network infrastructure, such as the LoRa link for exchanging data with automation PLCs commonly found in farms and the Telegram link for direct messaging with users via popular messaging apps, further enhances the functionality of the system.The general illustration of the presented system is described in To conclude, the proposed infrastructure represents a smart solution for farmers to integrate the IoT in their already existed farming systems which mainly are relying on a regular PLC. Currently, we are working on the development of some Artificial Intelligence (AI) data analysis tools that can be added to the presented systems for better farm management.


F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an  In recent years, there has been a surge in global electricity consumption, emphasizing the significance of voltage source converter-based high-voltage direct current transmission (VSG-HVDC) systems, especially those based on offshore platforms that have garnered increasing attention . The conIn the field of fault diagnosis for various power devices, including the main circulation pump, two primary methods are available. The first approach entails regular disassembly and inspection of the main circulation pump by operation and maintenance personnel. However, this method is economically impractical due to the substantial allocation of manpower and time it necessitates as well as the mandatory equipment shutdown . The secCurrently, there are a few mature technical solutions to address the challenges posed by the unmanned operation and maintenance of the main circulation pump in the cooling system of the VSG-HVDC system ,11,12,13As the field of artificial intelligence (AI) continues to expand, an increasing number of algorithms and models are being utilized for the fault diagnosis of various devices. For instance, a multi-layer perceptron (MLP) model was proposed in  for the The most critical aspect of ensemble learning is how to determine the final output of the entire model based on the results of each base learner , namely (i)This study has conducted a comprehensive validation of different fault diagnosis models for the main circulation pump in the specific application scenario. The findings reveal that while LSTM performs the best among individual models, the ensemble comprising MLP, CNN, and LSTM surpasses both the individual models and other ensembles in terms of diagnostic performance. This highlights the strength of utilizing a combined approach for improved fault diagnosis outcomes.(ii)The contribution of this article also lies in the development of a proposed model that effectively harnesses the strengths of different AI models, thereby overcoming the limitations associated with existing fault diagnosis algorithms when applied to complex operating conditions and limited fault data scenarios in main circulation pumps. Specifically, a novel training approach called the coach-members method is introduced for ensemble models, enhancing the compatibility of diverse base learners across various working conditions and resulting in an ensemble fault diagnosis model that achieves superior performance. This innovative approach demonstrates the capability to overcome challenges associated with fault diagnosis in demanding operational environments.To address the limitations of current fault diagnosis models for the main circulation pump in the cooling system of a VSG-HVDC converter valve, this study proposes a novel and cost-effective approach that integrates a set of base learners with a weighting model based on deep reinforcement learning (DRL). This approach aims to achieve more accurate fault diagnosis and thus improve the safety and stability of the VSG-HVDC transmission system under unmanned conditions. Each base learner is optimized for specific operating conditions to achieve the best fault diagnosis performance, while the DRL-based weighting model synthesizes their outputs and assigns different weights to obtain the final fault diagnosis results. The contributions and innovations of this study can be summarized in three aspects:The remaining part of this study is organized as follows. In The main circulation pump plays a crucial role in the internal water cooling system, as it is the only rotating equipment that operates continuously. Its normal operation ensures sufficient pressure and maintains a constant flow of the cooling water . The hea(i)Bearing lubrication failure: usually caused by insufficient lubrication or the use of low-quality lubricants, leading to increased friction and heat generation in the bearings. This can result in premature failure of the bearings and potential damage to the shaft.(ii)Shaft center deviation: usually caused by improper assembly or installation, resulting in misalignment between the shaft and bearings. The consequences of shaft center deviation include increased vibration, wear and tear on the bearings, and potential damage to the impeller.(iii)Water leakage: due to wear and tear on the pump housing or impeller or inadequate sealing of the inlet or outlet. The consequences of water leakage include reduced coolant flow, increased pressure in the cooling system, and potential damage to other components in the system.The cooling system inside each pole of the converter valve usually has two main circulation pumps, with one serving as the primary pump and the other serving as a backup. The main circulation pump is made up of several components, including a motor, impeller, shaft, bearings, and pump housing . The motTherefore, it is crucial to monitor the main circulation pump for common faults to prevent potential damage to the system. Each main circulation pump is equipped with four vibration sensors that monitor horizontal and vertical vibration on the pump and motor sides, and the collected vibration signals can be used to diagnose the faults of the main circulation pump. However, the performance of different methods to process these data for fault diagnosis varies, and existing commonly used methods often fall short in accurately diagnosing main circulation pump faults. Hence, it is of paramount importance to enhance existing methods and develop a new and effective fault diagnosis model.q is a hyperparameter which represents the length of the data sequence containing enough information and is to be determined in In order to diagnose the fault of the main circulation pump, sufficient time series data should be used as input to the fault diagnosis model, as shown in Equation (1):The main circulation pump of the VSG-HVDC system has three common faults in addition to its normal operating mode, namely: bearing lubrication failure, shaft center deviation, and water leakage. The normal working mode is marked as 0, while the remaining three faults are marked as 1, 2 and 3, respectively. Many methods can be used for fault diagnosis of the main circulation pump in the VSG-HVDC system. By inputting the collected data However, due to the complex operating conditions of the main circulation pump, it is difficult for a single model to accurately fit the relationship between operating data and faults. Therefore, we consider using multiple popular fault diagnosis models (referred to as base learners) simultaneously to jointly diagnose faults. The fault diagnosis result of one of the base learners is:n element. The diagram of coordination of diagnosis results of different base learners is present in After all the base learners have output the results of fault diagnosis, a model is required to coordinate these outputs. This model that plays a coordinating role needs to comprehensively consider the current operating conditions and the conclusions given by each model in order to output a reliable diagnostic result, as shown in Equation (6):F1 score, are often used to evaluate the performance of fault diagnosis models [(i)Accuracy Precision ((iii)Recall rate ((iv)F1 score MLP: a feedforward neural network model that consists of multiple layers of interconnected nodes and can be trained using backpropagation. It is known for its ability to learn complex non-linear relationships between inputs and outputs, making it suitable for capturing patterns in fault data. The advantages of MLP in fault diagnosis include its flexibility in handling different types of data, ability to learn complex patterns, and potential for accurate classification and prediction. However, MLP may have limitations in handling noisy data, overfitting, and sensitivity to hyperparameter tuning.(ii)CNN: a type of deep neural network that is designed for image or data in the form of an array. It uses convolutional layers to automatically learn spatial hierarchies of features from data, making it highly suitable for fault diagnosis tasks where the fault data are presented as an array. CNN has been shown to achieve state-of-the-art performance in many fault diagnosis tasks, making it a popular choice in the field.(iii)LSTM: a type of recurrent neural network (RNN) that is designed to overcome the vanishing gradient problem in traditional RNNs, making it more effective in capturing long-term dependencies in sequential data. LSTM introduces specialized memory cells with gating mechanisms that can control the flow of information, allowing for the effective handling of sequences with varying time scales. LSTM has gained popularity in fault diagnosis tasks that involve time series data due to its ability to capture complex temporal dependencies, handle variable-length sequences, and mitigate the vanishing gradient problem.Despite the advantages of each individual model, no single model is perfect and can perform well in all operational conditions of the VSG-HVDC system’s main circulation pump, since each model has its own limitations and may struggle with certain types of faults, noisy data, or complex data distributions. Therefore, a practical approach is to ensemble these models to leverage their strengths and mitigate their weaknesses.The ensemble approach in fault diagnosis has several advantages. First, it can reduce the risk of misclassification by leveraging the strengths of different models. Second, the ensemble approach can enhance the generalization performance of the models, as they can compensate for each other’s limitations. Finally, the ensemble approach can provide a more reliable diagnosis result by considering the consensus of multiple models. In case one model produces an incorrect diagnosis due to its limitations or noise in the data, the ensemble can still make a more accurate decision based on the majority vote or weighted average of the models’ predictions.To integrate the diagnostic results of multiple base learners, it is necessary to determine the degree to which their outputs should be weighted, which is a critical aspect of ensemble learning-based fault diagnosis. Various methods have been proposed to process the results of multiple base learners, including the average weighting method  and the The basis for determining DRL is employed to assign the weights for all base learners, so the action variable The aim of utilizing the DRL-based weighting model is to obtain the optimal weight combination at each time point, thereby minimizing the average error (or cumulative error) of the fault diagnosis results of the ensemble model. This objective can be transformed into a task of maximizing cumulative rewards within the DRL framework. Accordingly, the agent’s reward function It is important to note that the start and end times of faults in the main circulation pump operating data can be difficult for operation and maintenance personnel to define accurately. In reality, faults may last for a certain period of time. Therefore, when comparing the fault diagnosis results of the ensemble learning model at a particular timestamp to the actual results, it is necessary to consider the practical results that match the output of the ensemble learning model within a range of The proposed ensemble fault diagnosis model is a highly parameterized model, with hyperparameters that require careful selection and a strict training process. This model includes (i)Normalize the sampled vibration signal data by mapping it into The specific steps of obtaining the optimal ensemble fault diagnosis model are shown as follows:(ii)Divide the sample dataset into a training set, validation set and test set by the ratio of 3:1:1.(iii)For each base learner, obtain candidate models with different combinations of hyperparameters using manual experience and grid search methods.(iv)Perform the first-stage rough training for all candidate base learners on the training set.(v)Evaluate the trained candidate base learners on the validation set. From each type of candidate base learner, select the optimal one producing the lowest error, respectively, so as to form the combination of (vi)Similarly, obtain candidate DRL-based weighting models with different combinations of hyperparameters. Obtain the same number of candidate ensemble fault diagnosis models by coordinating each candidate DRL-based weighting model with the roughly trained base learner combination, respectively.(vii)Perform the second-stage fine training for all candidate ensemble fault diagnosis models on the training set.(viii)Evaluate the trained candidate ensemble fault diagnosis models on the validation set and select the optimal one producing the lowest error.(ix)Test the optimal ensemble fault diagnosis model on the test set. Denormalize and analyze the output with the performance metrics, as shown in (6) to (9).(i)When members join the team, they should already have a good foundation in terms of their abilities and skills;(ii)The coach is able to optimize the team’s cooperation strategies in real time based on the situation on the field and the actual capabilities of each member;(iii)Members are able to optimize their collaboration with each other in real time based on the strategies provided by the coach, allowing for efficient communication and synergy among team members.Stage (vii) employs the coach-members method proposed in this study, which is inspired by the concept of training football teams. To win a game, players not only need to meet their own level standards but also require an experienced coach to develop appropriate strategies for them based on the situation on the field and the actual condition of the players. The coach needs to arrange the optimal roles and participation levels of each member in the game, and each member needs to improve their cooperation with other players under the coach’s guidance, thereby increasing the probability of winning. If a player has strong personal abilities but receives poor guidance from the coach, or if the coach has high coordination skills but each player’s personal level or ability to cooperate with others is poor, the team’s probability of winning will be very low. Therefore, to improve the team’s probability of winning, the following three conditions need to be met simultaneously:(i)Before being incorporated as a base learner in the ensemble fault diagnosis model, each fault diagnosis model must have its hyperparameters and parameters fine-tuned to perform the fault diagnosis task independently and with satisfactory performance.(ii)The DRL-based weighting model should continuously optimize its parameters based on diagnostic errors. This allows it to determine the weight of each base learner more appropriately based on the operating status of the main circulation pump and the output of each base learner. As a result, each base learner can leverage its strengths and avoid weaknesses in diagnostic tasks under different working conditions.(iii)All base learners should continuously and simultaneously optimize their respective parameters based on diagnostic errors, so that when the DRL-based weighting model provides specific weight combinations, they can improve their collaboration with other base learners and enhance the accuracy of fault diagnosis.The methods mentioned above to improve the winning probability can be summarized as the coach-members method. Similarly, to enhance the performance of the proposed ensemble fault diagnosis model in the task of detecting faults in the main circulation pump, the model needs to meet the following three conditions:The first condition of having each base learner handle the fault diagnosis task independently with good performance is satisfied after the first-stage rough training. However, the second and third conditions require implementation through the second stage of fine training. During each batch of the training process, the weight values are given by the weighting model, the predicted value of It is important to note that in the second stage of fine training, all base learners must be involved. Although the base learners that have undergone the first stage of rough training have a certain level of accuracy in fault identification, this accuracy represents the average level across all operating conditions. A good ensemble fault diagnosis model should not solely rely on the overall performance of each base learner, as this may result in common shortcomings in fault diagnosis tasks for all base learners under certain operating conditions. Instead, a good ensemble fault diagnosis model should require all base learners to coordinate and cooperate, so that some base learners have high accuracy in each working condition, and the outputs of these high-accuracy base learners will have a greater influence on the final output, thus improving the overall performance of fault diagnosis.In this section, we demonstrated the excellent performance of the proposed model in the fault diagnosis task of the main circulation pump of the converter valve in the VSG-HVDC system through three progressive cases. Case 1 examined the performance of five commonly used fault diagnosis models and obtained their hyperparameter configuration and initial network parameters. In Case 2, we found the optimal combination of base learners that maximized the improvement in performance metrics. Building upon the optimal base learner combination found in Case 2, Case 3 verifies the superior performance of the DRL-based weighting model proposed in this study compared to other methods.The experiment used data collected from the main circulation pump data collection system of the No.1 converter valve at Chuxiong converter station in China, from 00:00 a.m. on 1 January 2018 to 20:00 p.m. on 31 December 2020. The main parameters of the main circulation pump include its model, CPKN C200-500, a flow rate of 107 L/s, lift of 65 m, speed of 1450 rpm, and rated power of 110 kW. Four vibration signals were collected, namely the pump side horizontal vibration signal, motor side horizontal vibration signal, pump side vertical vibration signal, and motor side vertical vibration signal, with a sensor sampling frequency of 2 kHz.(i)During routine maintenance, the converter valve was shut down nine times from 2018 to 2020, resulting in the main circulation pump’s vibration signal data being divided into roughly 10 sections by the nine breakpoints.(ii)Each data point is labeled by the operation and maintenance personnel of the converter station with a certain type of fault. To reduce the information extraction burden of the model, independent data sequences should only contain data points with the same fault type.(iii)The selection of the length of each independent data sequence, i.e., g et al.  proposedBefore using the collected data in this experiment, some preprocessing is required. First, the three-year dataset must be divided into several independent and sufficiently long sequences of data to be processed by the model. The data partitioning follows three principles:Based on the above partitioning principles and a 3:1:1 ratio, the dataset was divided into a training set, a validation set, and a test set. The training set contained 240 data sequences for each of the four fault modes, while the validation and test sets had 80 data sequences for each of the four fault modes, respectively.This case is used to evaluate whether some algorithms are suitable to be used as the base learners of the ensemble fault diagnosis model and also to obtain the optimal hyperparameter settings of these base learners. In addition to MLP, CNN, and LSTM introduced in After the first-stage rough training described in Based on the results in In this case, we selected different numbers of models as base learners to create a combination of base learners, and then, we employed the DRL-based weighting model using the coach members method to assign weights to the diagnostic results of each base learner. From Case 1, where there were a total of five tested models, we selected two, three, four, and five models as base learners to create a combination of base learners, resulting in 10, 10, 5, and 1 ensemble fault diagnosis models, respectively, for a total of 26. The index of these combinations of base learners and the base learners included within them is shown in F1 score. The results are shown in i combination of base learners in The trained ensemble fault diagnosis models were tested using a test set, and their performance in fault diagnosis was measured using accuracy, precision, recall, and To validate the effectiveness of the DRL-based weighting model proposed in F1 by 2.18% and 5.27%, respectively. In contrast, if the coach members method was not utilized during training, the model did not perform as well as the ISA-based weighting method. However, when the coach-members method was employed for model training, the model outperformed all other weight determination methods, demonstrating the effectiveness of the proposed DRL-based weighting model.From This study presents a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in VSG-HVDC transmission systems. The model integrates three base learners, namely MLP, CNN, and LSTM, with a DRL-based weighting model that determines the weights of all base learners in the final diagnosis result. This ensemble fault diagnosis model accurately identifies the fault type of the main circulation pump, which is critical for ensuring operational safety and stability in converter stations and contributes to the unmanned operation, maintenance, and monitoring of offshore VSG-HVDC systems.F1 score. Notably, the four performance metrics reach as high as 95.00%, 95.00%, 86.36% and 90.48%, respectively, which are 4.06%, 8.75%, 7.05% and 7.85% higher than those achieved by the widely used LSTM model, respectively. This highlights that the proposed model effectively leverages the strengths of different models and overcomes the limitations of the existing fault diagnosis algorithms in complex operating conditions with limited fault data. Furthermore, it outperforms the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This indicates that the coach members method enhances the compatibility of different base learners in various working conditions, further enhancing the performance of the ensemble model.The study conducts extensive validation work on the application scenario of fault diagnosis for the main circulation pump, leading to the conclusion that LSTM performs the best among individual models. However, when the ensemble fault diagnosis model is applied, the combination of MLP, CNN, and LSTM outperforms individual models. Furthermore, experimental results demonstrate that the proposed model surpasses traditional AI models in terms of accuracy, precision, recall, and


To improve their spatial resolution and detection capabilities, future ground-based optical telescopes will have a size of 30 m, and the aperture of space telescopes will be increased to 10 m. Such large optical systems necessitate the development of large integrated testing equipment. In this study, spectrum and system alignment measurements and wavefront quality checking were performed using the sub-aperture detection method and a fiber-connected Photonics Scanning Pentaprism (PSP). First, the system was aligned using an optical truss, ensuring that the optical axis was properly positioned. Second, using a sub-aperture light beam though the entrance pupil, light spots were formed on the focal plane and transmitted to the spectrometer via fibers to obtain the corresponding spectral components. Then, by taking measurements at different system positions, a full-aperture spectrum response could be reached. Lastly, by photon-integrated interference on the focal plane, intensity interference fringes could be projected at the entrance pupil of the system. And the wavefront quality of the system could be verified by observing the fringe deformation. The measurement accuracy of the optical axis of the system is better than 2 mrad. The spectral measurement accuracy was better than 5%, and the wavefront measurement accuracy surpassed 0.1 wavelengths (1 wavelength = 633 nm). This study effectively enhanced the detection and in situ calibration capabilities of large telescope systems, ensuring that the performance requirements can be met in the design of future telescopes. Future ground-based telescopes are expected to be 30–40 m in size. Meanwhile, the apertures of space telescopes are being increased from 6 to 10 m. In addition to providing a better understanding of the universe (including the “cosmic dark ages” and “first light”), large telescopes armed by adaptive optics facilitate significantly detailed observations of the structures of the universe. This is very significant for investigating black holes, dark matter, dark energy, exoplanets, and other important objects in astronomy. Telescopes must be tested during fabrication, as well as during the assessment of resembling. The following two operations are used for this testing: (1) the auto-collimation of planar mirrors and (2) planar wavefront emission by collimators. However, the costs and technical risks of building the corresponding large-aperture plane mirrors or collimators rapidly increase with the size of the aperture.It is currently common to use sub-aperture stitching for such assessments. However, the use of small-aperture plane mirrors or collimators for stitching requires highly complex actuators . To ensuThe large optical test and integration site (LOTIS) system produced by Lockheed Martin is a representative example of the equipment used for in situ measurements. It has an aperture of 6.5 m and operating wavelength of 0.4–5 μm, and can achieve a root-mean-square (RMS) error of less than 85 nm for a wavefront, at a ±0.75 mrad field of view (FoV) . To achiScanning pentaprisms are insensitive to tilt errors and have a good response to low-spatial-frequency signals. They are cost-effective and have good coverage and a simple structure. They are currently used for the testing of large planar and parabolic mirrors. The University of Arizona performed low-order tests of large-aperture plane mirrors with an accuracy of 18 nm. The LOTIS system wavefront cutting measurement equipment consists of nine pentaprism mirrors for optical pupil alignment, offering a measurement accuracy higher than 225 nm under atmospheric conditions. In addition to its other measurement equipment, LOTIS is equipped with visual and jitter measurement equipment for the mirror, which is used to ensure the normal operation of the measurement equipment .Scanning pentaprisms can also be used to assess the parallelism of multi-axis optical systems. However, testing methods for the wavelength accuracy  should be further explored. Wen Zhongkai et al. achieved a calibration accuracy of 0.998″ using bulk optical elements , Xu DanhThe traditional large-aperture telescope testing equipment uses volume optical elements. Different devices are used for different parameters, which are greatly affected by the environment, and it is difficult to meet the needs of in situ detection. Using photon scanning pentaprism can effectively reduce the volume and weight of the system. It can reduce the requirements of environmental conditions for testing and realize in-situ detection. By using the optical fiber interconnected system, the system stiffness can be used for reference transmission. It not only reduces the volume and weight of the measurement system, but also uses a common reference to transmit the metrology value, which reduces the accuracy loss caused by the non-common optical path difference and transmission link of the traditional method, effectively improves the in-situ measurement accuracy of the optical system, shortens the length of the traceability chain, and increases the efficiency and accuracy of the detection.This study is organized using the following structure: In this study, an improved Photonics Scanning Pentaprism (PSP) is proposed to perform waveguide-based photon collection and sensing, using the fiber-connected architecture. This addresses the significant effects of atmospheric and environmental vibrations on the optical path of conventional free-space scanning pentaprisms, which can make it impossible to test wavefront errors. Because the optical path of the PSP no longer had structural and spatial limitations, it was possible to arrange multiple short-range scanning mechanisms in a one-dimensional upward direction without introducing long rails. Moreover, the surface quality and stiffness requirements of the back-end mechanism could be reduced in response.As a result, the testing and setup of large-aperture telescopic optical systems could be achieved at a low cost, reducing the dependence on large-aperture collimators. Furthermore, the separate small-aperture system was less affected by the atmosphere, which improved the testing accuracy. The evolution of the perceptual uncertainty of the parameters of the optical path delay and the influences of the errors on the final control performance must be clearly understood to achieve high-precision optical path control and error suppression in the frequency/spatial domain under multi-physics fields.What is more, spectral measurements/calibration can also be performed and systems can be aligned using PSP. Stitching detection of the spectral response of the system can be achieved by taking measurements at different positions of the entrance pupil. By combining PSP with integrated interference, intensity interference fringes can be formed at the pupil. The light starts at the focal plane, passes through the system, and the fringe is obtained by a fiber-connected pentaprism system located at the pupil plane. Next, the wavefront slope is extracted. Finally, the wavefront information is reconstructed using the slope at that location.From Using the basic principles of Fourier optics, the complex optical field is analytically expressed through the light wave interference process for a single wavelength ,14. TherThe intensity can be obtained by squaring the modulus of the complex optical field as follows:After obtaining the expression for a single wavelength, the intensity of the interference between different wavelengths, etector) ,16,17:WuFirst, a two-dimensional interference fringe detection simulation model was constructed, where the characteristic frequency components in the two directions were obtained via a frequency domain analysis. The fringes obtained through the interference of volume optical elements and integrated photon interference are shown in Reconstructed wavefront were computed based on slope sampling and reconstruction in the frequency domain. A slight degradation was observed only at the edges of the samples. The system wavefront restoration results for small aberration mode are shown in Here, the rotational symmetry of the system was used for testing. After obtaining the data in one position, the angle of the spectral grating was rotated to obtain circumferential sampling. By cross-comparison with data from the initial position, we ensured that the curvature radius and relative alignment of the optical element satisfied the requirements ,21,22. VUsing optical trusses, the optical axis alignment accuracy could be maintained at 2 mrad, and the change in the system pointing angle during pentaprism scanning had to be less than this value. For high-precision measurements, rotation measurements could reduce the shaking caused by torque imbalances and achieve high detection accuracies. The scene diagram of the system’s optical axis alignment and the correspondence principle diagram are shown in In this paper, the method of direct camera detection and pentaprism pupil fringe detection is used to verify the system’s fringe acquisition. The results of the system’s direct acquisition and pentaprism scanning acquisition are similar, so the pentaprism polarizer or the camera can be used to collect stripes according to the actual situation. Based on the principle of shear interference , the spaThe spectral reception capability of the system was verified using a sweep frequency laser to match the sub-spectral bands and multi-mode fiber confocal system for light acquisition. High-flux spectral detection was achieved using integrated photonic devices. Five optical waveguides were integrated to obtain the spectral responses under different spectral inputs. The system used a broadband light source to output broadband light. The output band was selected using a wavelength division multiplexer and the light of the selected band was sent to the system’s focal plane using a coupler. During operation, the center position of the projection was verified using the waveguide of the center position and lens set; moreover, the system projection range was verified using the edge waveguide. After calibrating the transmittance of each waveguide based on the central wavelength (1560 m), the uniformity of the system’s light intensity was better than 5%. We made sure the coupling efficiency was high at each position on the pupil surface, ensuring that the laser power used did not induce nonlinear effects. The verification of the high-flux system spectral testing is shown in Considering the increasing complexity of telescopic systems, the mechanical and optical reference transfer between multiple subsystems influences the registration accuracy and fusion effects of multi-modal joint measurements. Therefore, traditional methods are no longer suitable for the current calibration requirements of optical systems with large apertures offering a “large span, multiple parameters, and a common reference,” and research into a new system for the in-situ measurement and detection of large-aperture telescopes is urgently needed.The optical axis could be precisely measured using PSP. Using the high-quality collimated beam incident on the pupil surface, we performed calibrations by combining the ideal target center positions of different terminals. Meanwhile, when components such as the secondary mirrors were tilted, the position of the light spot formed by the system changed. The tilt of the system’s optical axis could effectively be identified based on these changes.Using optical axis and spectral response measurements, the optical lever effect  can be applied to measure the slope of the wavefront through the focal plane to obtain the optical surface quality of the system and relative position of the optical components. The polar and Cartesian coordinate system scanning modes can be selected when applying the actual system. Aiming for a highly efficient test process, rotation sampling is applied to maximize the accuracy and minimize the time; the overall planning of the sampling locations, scanning paths, and dwell times are optimally designed by balancing the spatial resolution, detection efficiency, and detection accuracy.PSP could be used to perform local sampling of the pupil based on the mapping relationship between the focal and pupil planes. Flexible sampling and reconstruction of the system parameters could also be achieved. We could transform phase measurements into energy measurements using spatial light intensity projection and the photon pentaprism. We could also construct an efficient in-situ detection system offering high flexibility, a small size, and minimal external influences on the baseline configuration of the photon pentaprism.Telescopes with high spatial resolutions and detection capability are expected to offer high sensitivities; high spatial, temporal, and spectral resolutions; and large fields of view (FoV). In response to the calibration requirements for in-situ detection of multiple parameters  in large aperture optics systems, utilizing the characteristics of fiber-interconnections such as small volume, light weight, and high degree of freedom in light transmission, high-precision in-situ calibration of multiple parameters is ultimately achieved. The measurement accuracy of the optical axis of the system is better than 2 mrad. The spectral measurement accuracy is better than 5%, and the wavefront measurement accuracy surpassed 0.1 wavelengths (1 wavelength = 633 nm). The Photonics Scanning Pentaprism (PSP) System is investigated here, and it can ensure the performance of the system under in-site environment and at long-term.


Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms. Sea target tracking usually deals with uncertainty problems, which mainly originate from dynamic uncertainty caused by the non-collaborative nature of the target and measurement uncertainty caused by measurement noise. To solve the two key problems of target dynamic uncertainty and target measurement uncertainty, a large number of theories and techniques have been generated for the study of target tracking.In the 1960s, Kalman et al. . HoweverThe above methods are more effective in solving tracking problems for a single model, but the accuracy decreases when estimating the state of a target with multiple motion patterns. However, in practical scenarios, targets inevitably have certain maneuvering characteristics, and their state models tend to change over time when the targets are maneuvering. To solve the tracking problem of maneuvering targets , an InteThe sensors carried by the aerial moving platform will inevitably generate measurement errors due to their own performance indicators, systematic regimes, zero-value correction residuals, interference, and noise. Such errors include random errors and systematic errors. Random error can be reduced by filtering techniques, but systematic error is an inherent error that is difficult to eliminate by filtering methods ,15,16,17Scholars have proposed a variety of spatial registration methods for systematic spatial registration, such as the real-time quality control method , the leaThe above algorithms only consider the sensor observation systematic error, which can solve the error alignment problem of fixed platforms well. Aerial moving platforms such as UAVs, unlike fixed platforms, have an attitude angle error, and this error couples to sensor observation error. The effect of this error is not only nonlinear, but also related to the position of the target. It exhibits time-varying sensor measurement systematic error, causing difficulties in estimation. Ref.  divides For the systematic spatial registration problem of dynamic platform sensors, there are generally two systematic error estimation models. The first one is a fully expanded three-dimensional model. It forms a state vector including the systematic error of the sensor and the attitude angle systematic error to estimate. Cui et al.  propose The second model is a decoupled, fully expanded dimensional model that equates the attitude angle systematic error to the sensor systematic error. Ref.  establisTherefore, in order to obtain the expected result of the aerial moving platform for tracking the target on the sea, the following two problems need to be solved: firstly, the attitude angle systematic error and random error of the aerial moving platform and the sensor systematic error and the random error should be reduced; secondly, a suitable tracking algorithm should be used for fast and accurate processing of the obtained data.In order to solve the above problems, we propose a new tracking algorithm for sea targets based on the sensor spatial registration of an aerial moving platform. Firstly, the observation error of the aerial moving platform for sea target detection is modeled and analyzed, and the influence of sensor error and attitude angle error on the observation data is derived. Then, we use two aerial moving platforms to detect sea targets and complete the reduction of sensor systematic error and attitude angle systematic error of aerial moving platforms. Finally, the IMM algorithm and Kalman filtering algorithm are combined to complete the tracking of the sea target. Through simulation experiments, on-lake experiments, and comparative analysis, the feasibility and effectiveness of the method are verified.The organization of the rest of the paper is as follows: L represents longitude, B represents latitude, and First, we describe the detection model of the aerial moving platform to the sea target. At a certain moment, the aerial moving platform acquires high-precision geographic coordinates Let the polar coordinates of Since When processing and analyzing the data measured by multiple aerial moving platforms, it is necessary to transform the target position information measured by different platforms into a unified coordinate systematic, called position alignment. In this paper, the sea target position information is standardized to a local geodetic reference system under the fusion center.According to the sequence from unstable carrier coordinate systematic to stable carrier coordinate systematic to earth coordinate systematic and finally to the local georeferencing systematic under the fusion center, the detection information of the aerial moving platform on the sea target is transformed into the local geodetic reference system under the fusion center based on the high-precision geodetic coordinates of the air movable platform The coordinates in the local geodetic reference system under the fusion center transformed by the aerial moving platform to the sea target measurement data areIn summary, the flowchart of the transformation of the detection information from the aerial moving platform to the local geodetic reference system under the fusion center is shown in Observation errors refer to the differences between the true values of measurements. These errors can occur due to a variety of factors, including calibration drift, electronic noise, or environmental disturbances. Specifically, the observation error of the aerial moving platform to the sea target mainly comes from two parts: sensor measurement error Since the data types of sensor error and attitude angle error are different, they cannot be directly fused by adding or subtracting them in a simple way. Typically, sensor error and attitude angle error are treated as separate factors due to their different data types. However, this approach can greatly increase computation and model complexity while also potentially impacting real-time tracking and tracking accuracy. Considering that both sensor error and attitude angle error finally act on the measurement data, the attitude error of the aerial moving platform can be converted into a part of the sensor error by analyzing the effect of attitude angle error on the sensor observation data.The attitude angle of the air platform In general, the attitude angle deviation of the aerial moving platform is small, and the sea target position is transformed from the carrier stable coordinate systematic to the carrier unstable coordinate systematic according to the attitude angleBy considering the true value of the attitude angle as a function of its measurement and error, Equation (14) can be approximated by a first-order Taylor expansion at the measurement of the attitude angle. The derivation is given in According to Equation (14), the distance measurement of the target by the aerial moving platform will not change during the transformation of the sea target from the stable carrier coordinate systematic to the unstable carrier coordinate systematic. The distance measurement is not affected by the attitude angle, so the sensor distance measurement model isAccording to Equation (15), the measured elevation of the sea target in the unstable carrier coordinate systematic considering only the attitude angle error can be obtained:Considering that In summary, the elevation measurement model is obtainedIn the same way, the sensor azimuth measurement model is obtainedThe effect of attitude angle error on the measured data of platform sensors has been obtained, and the connection between sensor error and attitude angle error has been established. The effect of systematic error on the measured values in the measurement equation is obtained from Equations (16), (19) and (20) to construct a systematic error consistency matrix The basic framework of the new tracking algorithm for sea targets proposed in this paper is based on the Kalman filtering of IMM. The difference is that a new pseudo-measurement equation is constructed from the measurements of the same sea target by two different aerial moving platforms.First, we synthesize Equations (5), (7) and (21) to rewrite the measurement equation of the aerial moving platform and divide the measured value into three parts: true position of the sea target, systematic error, and random errorEquation (24) can be writtenIn practical operations, the true value of the target measurement data The pseudo-measurement equation is constructed based on the uniqueness of the location of the sea target. From Equation (22), the true position of the sea target in the local geographic reference systematic under the fusion center isTherefore, the measurement equation of the aerial moving platform is rewritten asThe measurement equations of the aerial moving platforms A and B on the sea target are obtained separately:It is easy to obtain according to the consistency of the target location,Subtracting Equation (28) from Equation (29)From Equation (31), we can obtain the expressions of the sensor systematic error and attitude angle systematic error about the aerial moving platform measurementEquation (28) and Equation (29) can be added to obtainSubstitute Equation (32) into Equation (34) to eliminate systematic errorsSubstituting into Equation (35) givesLetIn summary, Equation (37) can be written asThe pseudo-measurement equation to reduce the impact of systematic errors in both sensor measurements and platform attitude angles is constructed, and the random sensor error and attitude angle random error is further reduced by combining IMM and Kalman filtering techniques. Now we can complete the fast-tracking of the sea target and the specific process is shown in To first verify the feasibility of the above algorithm, simulation experiments are conducted and compared with the existing algorithm. Linear Pseudo Linear Kalman Filter (PLKF) and nonlinear Square Root Cubature Kalman Filter (SRCKF) are used to track the target position, respectively, where EC represents the error estimation method in the literature , thus coThe fusion center is located at coordinates In this paper, two models are used in the IMM algorithm: Constant Velocity model (CV) and Constant Turning model (CT). The state transfer probability matrix of IMM is set to In order to fully verify the feasibility, robustness, and superiority of the algorithm compared with other algorithms, two groups of different simulation experimental scenarios are set up, mainly changing the error settings, in which the simulation duration is all 400 s, and the target motion is uniform linear motion from 0 to 80 s, uniform right-turn motion from 80 to 160 s, uniform linear motion from 160 to 240 s, uniform linear motion from 240 to 320 s, and 320~400 s. The error settings for different scenarios are as follows.Simulation scenario one: the error settings are the same as in To verify that the newly proposed algorithm is robust and applicable under different systematic error variations, scenario two is designed.Simulation scenario two: Compared with simulation scenario one, the error of the sensor observation system of the aerial motion platform A changes abruptly at 50–70 s and 240–260 s, and its angular deviation becomes eight times the original one.In order to further analyze the causes of IMM-PLKF and IMM-PLKF tracking errors, we carried out simulation experiments by adding random sensor error, sensor systematic error, and attitude error, respectively, on the basis of scenario 1 target motion. The tracking results are shown in As can be seen from To further evaluate the robustness of the new algorithm against error effects, we conducted experiments by gradually increasing the error mutation multiplier from 1 to 8. The results are as follows: From It is worth noting that the IMM-PLKF and IMM-SRCKF exhibit slower changes as the mutation multiplier increases since the mutation time is shorter. As a result, these methods demonstrate smaller changes in total tracking error compared to the IMM-ECPLKF and IMM-ECSRCKF. In contrast, the new algorithm has the shortest single-step time among all methods, making it computationally efficient. This is primarily due to the fact that the new algorithm does not require individual error estimation or filtering of individual platforms. As a result, it greatly improves computational efficiency.To further verify the effectiveness of the algorithm, we conducted an on-lake experiment in a water area in Ezhou City, Hubei Province.This experimental system consists of an information processor (a three-screen tablet computer), YAR28 (N) radar, Ku02 radar, combined navigation equipment, RTK, etc. Several major experimental pieces of equipment are briefly introduced below. Due to the difficulty of carrying the existing radar sensors on the UAV, two radars combined with inertial navigation equipment with errors were used to simulate the aerial moving platforms for observation.The YAR28(N) radar is a continuous wave systematic sea surveillance network radar with a strong ability to detect small targets, good suppression of sea clutter, and superior target tracking performance. The appearance of this radar is shown in Ku02 radar is a pulse systematic radar that can effectively track low and small slow targets. The appearance of this radar is shown in The information processor uses a three-screen tablet computer, and the target uses a small boat, as shown in In the experiment, two small boats were used as targets to make maneuvering movements in the designated waters. The two radars are placed at a fixed location on the shore to simulate two aerial moving platforms for cooperative observation of the target. In order to obtain a better target state fusion estimation effect, a certain angle is formed between the two radars and the target so that the two radars can complete the observation of the target from different angles. The experimental situation is shown in The specific experimental procedure is that the boat navigates in the designated waters and makes maneuvering movements. Then, the two radars are turned on simultaneously, and the two radars search and steadily track the target in real-time to obtain the target’s distance, bearing, and pitch angle information. The inertial guidance equipment outputs the inertial guidance information of the radar with errors, and the RTK equipment outputs the high-precision geodetic coordinates of the radar position. The target observation information obtained by the two radars, as well as the inertial guidance information and the radar geodetic coordinates information, is transmitted to the information processor to finally obtain the state estimation of the target. Experimental procedure is shown in The algorithm initializes the target tracking with the target’s observations. In order to verify the effectiveness of the proposed algorithm, data collected during on-lake experiments were utilized. Scenarios three and four were executed to demonstrate the algorithm’s ability to track targets along different boat trajectories. The actual tracking results for scenarios three and four are illustrated in When the data processing is performed in the experiment on the lake, the amount of data that can be practically applied after data alignment and wild value elimination is very small. The endpoint and trajectory position estimation errors are calculated for each algorithm. The position estimation errors of scenarios three and four are shown in Based on The results of the experiments on the lake further verify the feasibility of the new algorithm and show its superior performance in engineering applications.Our study proposes a novel tracking algorithm for sea targets based on the spatial registration of the aerial moving platform sensors. In this study, we design the systematic error consistency matrix to reduce the systematic error and establish the pseudo-measurement equation through the observation of targets by two aerial moving platforms. The novel algorithm aims to achieve more accurate tracking of sea targets. The simulation and on-lake experiments show that the new method has better spatial registration performance than the previous filtering algorithm. When the systematic error is time-varying, the new algorithm can implement stable tracking of sea targets compared to the previous filtering algorithm, which means it has a certain application value in practical scenarios.