Smart Instruments Research Articles

Calculations without math: “Smart instruments” and the transposition of complex shapes in the wooden boat workshop

Calculations without math: “Smart instruments” and the transposition of complex shapes in the wooden boat workshop

Analysis of Measurement Accuracy and Reliability of Intelligent Instrumentation in Process Control

Abstract Intelligent instrumentation plays a crucial role in industrial applications, with its measurement accuracy and reliability directly influencing operational efficiency. This article presents the integration of the HART communication protocol to enhance data transmission within smart instruments, focusing on internal structure and process control optimization. A HART smart instrument multiplexer system is established, utilizing an analog PID control algorithm for data regulation and optimal control techniques for automatic parameter adjustment. Additionally, the three-parameter Weibull distribution is employed to assess the reliability of smart meters, deriving distribution characteristics and estimating parameters through failure rate analysis. The performance of the designed HART smart meter multiplexer system is thoroughly evaluated, including control performance, reliability, channel function, and measurement accuracy. Notably, the system demonstrates a minimal overshoot in analog PID control at 0.911%, with a steady-state error range of -5°C to 4°C. Furthermore, the implementation of this system significantly reduces the risk of hazardous failures by 41.91%. In conditions of 350°C temperature and 1.2MPa pressure, the system accurately measures superheated steam flow with a relative error of only 0.05%. By optimizing the settings of smart instruments, this system not only enhances measurement accuracy in industrial production but also improves reliability, supporting diverse applications in the chemical industry.

Applications of Artificial Intelligence in Cataract Surgery: A Review.

Cataract surgery is one of the most performed procedures worldwide, and cataracts are rising in prevalence in our aging population. With the increasing utilization of artificial intelligence (AI) in the medical field, we aimed to understand the extent of present AI applications in ophthalmic microsurgery, specifically cataract surgery. We conducted a literature search on PubMed and Google Scholar using keywords related to the application of AI in cataract surgery and included relevant articles published since 2010 in our review. The literature search yielded information on AI mechanisms such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) as they are being incorporated into pre-operative, intraoperative, and post-operative stages of cataract surgery. AI is currently integrated in the pre-operative stage of cataract surgery to calculate intraocular lens (IOL) power and diagnose cataracts with slit-lamp microscopy and retinal imaging. During the intraoperative stage, AI has been applied to risk calculation, tracking surgical workflow, multimodal imaging data analysis, and instrument location via the use of "smart instruments". AI is also involved in predicting post-operative complications, such as posterior capsular opacification and intraocular lens dislocation, and organizing follow-up patient care. Challenges such as limited imaging dataset availability, unstandardized deep learning analysis metrics, and lack of generalizability to novel datasets currently present obstacles to the enhanced application of AI in cataract surgery. Upon addressing these barriers in upcoming research, AI stands to improve cataract screening accessibility, junior physician training, and identification of surgical complications through future applications of AI in cataract surgery.

Characterization and discrimination of the flavor profiles of Chinese indigenous sheep breeds via electronic sensory, smart instruments and chemometrics

This study was conducted to analyze the flavor profile of six commercially available Chinese indigenous breeds of sheep meat and to distinguish them based on their flavor characteristics. Various techniques such as gas chromatography-mass spectrometry (GC-MS), electronic nose (E-nose), high performance liquid chromatography (HPLC) and electronic tongue (E-tongue) were employed to analyze both volatile and non-volatile compounds in the meat samples. The results showed that, a total of 28 volatile compounds in sheep meat were detected, with 1-octen-3-ol, 1-pentanol, hexanal, octanal, and nonanal identified as the major contributors to its aroma. Tan Sheep and Oula Sheep had significantly higher concentrations of 1-pentanol, nonanal, 1-octen-3-ol, 1-octanol, and hexanoic acid than other breeds. Sweet amino acids were the most abundant free amino acids, comprising 72–85% of the total FAAs, and inosinic acid was the most abundant ATP-related compound. Small-Tailed Han sheep had significantly higher levels of SFAAs, total FAAs, and AMP, while Tan sheep had significantly higher levels of UFAAs, and Oula Sheep had significantly higher levels of GMP. The response values of differential electronic sensors W5S, W1W, NMS, PKS, and SCS were found to be useful in discriminating between breeds of sheep meat. The fusion electronic sensory signals were used to train support vector machine and random forest models, both achieving 100% accuracy in discriminating between sheep breeds.

Performance of Smart Materials-Based Instrumentation for Force Measurements in Biomedical Applications: A Methodological Review

The introduction of smart materials will become increasingly relevant as biomedical technologies progress. Smart materials sense and respond to external stimuli (e.g., chemical, electrical, mechanical, or magnetic signals) or environmental circumstances (e.g., temperature, illuminance, acidity, or humidity), and provide versatile platforms for studying various biological processes because of the numerous analogies between smart materials and biological systems. Several applications based on this class of materials are being developed using different sensing principles and fabrication technologies. In the biomedical field, force sensors are used to characterize tissues and cells, as feedback to develop smart surgical instruments in order to carry out minimally invasive surgery. In this regard, the present work provides an overview of the recent scientific literature regarding the developments in force measurement methods for biomedical applications involving smart materials. In particular, performance evaluation of the main methods proposed in the literature is reviewed on the basis of their results and applications, focusing on their metrological characteristics, such as measuring range, linearity, and measurement accuracy. Classification of smart materials-based force measurement methods is proposed according to their potential applications, highlighting advantages and disadvantages.

Current and Future Advances in Surgical Therapy for Pituitary Adenoma.

The vital physiological role of the pituitary gland, alongside its proximal critical neurovascular structures means pituitary adenomas cause significant morbidity or mortality. Whilst enormous advancements have been made in the surgical care of pituitary adenomas, treatment failure and recurrence remain challenges. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (e.g. endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, the future armamentarium of pituitary surgeons, including advanced optical devices, smart instruments and surgical robotics, will augment the surgeon's abilities. Intraoperative support to team members will benefit from a surgical data science approach, utilising machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, early detection of individuals at risk of complications and prediction of treatment failure through neural networks of multimodal datasets will support earlier intervention, safer hospital discharge, guide follow-up and adjuvant treatment decisions. Whilst advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of technological translation, ensuring systematic assessment of risk and benefit. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

Smart scientific instruments based on smartphones: a brief review

<span>Smartphone has gone beyond a communication hub to be a measurement device itself, thanks to various built-in sensors. This article reviewed achievements in transforming ubiquitous smartphones into cost-effective scientific instruments for educational laboratories, environmental studies, point-of-care diagnostics, home-based health monitoring, and rehabilitation. Magnetic fields were precisely measured by built-in magnetometers, leading to demonstrations for engineering and medical applications. The smartphone-based joint-angle measurement was a viable alternative to traditional goniometers. Characterizations of optical signals captured by cameras led to portable spectrophotometers and colorimeters for both educational and practical uses. Interestingly, smartphones became a platform for high-resolution microscopes and fluorescence microscopes were developed with add-on components. These smart instruments become even more attractive options in the pandemic period with limited facility and laboratory access.</span>

Robotics and cybersurgery in ophthalmology: a current perspective.

Ophthalmology is one of the most enriched fields, allowing the domain of artificial intelligence to be part of its point of interest in scientific research. The requirement of specialized microscopes and visualization systems presents a challenge to adapting robotics in ocular surgery. Cyber-surgery has been used in other surgical specialties aided by Da Vinci robotic system. This study focuses on the current perspective of using robotics and cyber-surgery in ophthalmology and highlights factors limiting their progression. A review of literature was performed with the aid of Google Scholar, Pubmed, CINAHL, MEDLINE (N.H.S. Evidence), Cochrane, AMed, EMBASE, PsychINFO, SCOPUS, and Web of Science. Keywords: Cybersurgery, Telesurgery, ophthalmology robotics, Da Vinci robotic system, artificial intelligence in ophthalmology, training on robotic surgery, ethics of the use of robots in medicine, legal aspects, and economics of cybersurgery and robotics. 150 abstracts were reviewed for inclusion, and 68 articles focusing on ophthalmology were included for full-text review. Da Vinci Surgical System has been used to perform a pterygium repair in humans and was successful in ex vivo corneal, strabismus, amniotic membrane, and cataract surgery. Gamma Knife enabled effective treatment of uveal melanoma. Robotics used in ophthalmology were: Da Vinci Surgical System, Intraocular Robotic Interventional Surgical System (IRISS), Johns Hopkins Steady-Hand Eye Robot and smart instruments, and Preceyes' B.V. Cybersurgery is an alternative to overcome distance and the shortage of surgeons. However, cost, availability, legislation, and ethics are factors limiting the progression of these fields. Robotic and cybersurgery in ophthalmology are still in their niche. Cost-effective studies are needed to overcome the delay. Technologies, such as 5G and Tactile Internet, are required to help reduce resource scheduling problems in cybersurgery. In addition, prototype development and the integration of artificial intelligence applications could further enhance the safety and precision of ocular surgery.

Identification of the flavor profiles of Chinese pancakes from various areas using smart instruments combined with E-noses and E-tongues

In order to reveal the flavor characteristics of Chinese pancakes, the aroma and taste compounds of seven traditional Chinese pancakes were identified. The results showed that electronic nose (E-nose) analysis with PCA could successfully distinguish the aroma profiles of seven Chinese pancakes; the principal components PC1 and PC2 represented 75.74% and 23.2% of the total variance (98.94%) respectively. Meanwhile, the discrimination index of taste profiles of seven Chinese pancakes based on electronic tongue (E-tongue) analysis with LDA was 99.32%; the discriminant factors DF1 and DF2 represented 94.99% and 4.33% of the total variance respectively. Furthermore, GC-MS results demonstrated that thirty-three flavor compounds were identified in seven Chinese pancakes, including aldehydes, alcohols, alkanes, acids, and aromatics. Among the flavor components, aldehydes with ROAVs higher than 1 contributed most significantly to the overall aroma, such as (E,E)-2,4-nonadienal present the largest contribution in Qingzhou pancake, Jinan and Yishui pancake; hexanal present the largest contribution in Shenxian and Gaomi pancake; nonanal and benzeneacetaldehyde present the largest contribution in Linqu and Tai’an pancake, respectively. The umami and sweet taste amino acids were the most abundant in all the Chinese pancake samples, and Qingzhou pancake had relatively high amino acid content. The content of glucose was higher than maltose in Gaomi, Shenxian, and Tai’an pancakes, whereas the content of maltose was higher than glucose in Linqu, Qingzhou, Jinan, and Yishui pancakes. These results indicated that the aroma and taste profiles of Chinese pancakes differed significantly in terms of their flavor compound composition. The presented results could be beneficial for providing a comprehensive method for flavor profile identification of traditional whole-grain-based staples such as Chinese pancakes. Keywords: Chinese pancake, flavor profile, E-nose, E-tongue, smart instrument, identification DOI: 10.25165/j.ijabe.20231601.6817 Citation: Liu W, Fan Y, Liu Q N, Xu F, Zhang L, Hu H H. Identification of flavor profiles of Chinese pancakes from various areas using smart instruments combined with E-noses and E-tongues. Int J Agric & Biol Eng, 2023; 16(1): 283–290.

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust and aphid with a few specific bands. Through a comprehensive spectral analysis, only three bands at 570, 680 and 750 nm were selected. A novel vegetation index namely Ratio Triangular Vegetation Index (RTVI) was developed for detecting anomalous areas on leaves. Then, the Support Vector Machine (SVM) method was applied to construct the discrimination model based on the spectral ratio analysis. The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy (OA) of 83%. With three bands from the hyperspectral imaging data, the three wheat diseases and pests were successfully detected and discriminated. A stepwise strategy including background removal, damage lesions recognition and stresses discrimination was proposed. The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

Remote datalogging of solar UV irradiation using open-source ESP32 platform and MQTT protocol

Recent data loggers are computer peripheral devices or stand-alone smart instruments which can collect local databases and are able to transfer data to remote servers. Therefore, an appropriate communication protocol is required to facilitate the integration to existing computer infrastructure. This study proposes the utilization of MQTT as a communication protocol between scientific instrumentation: UV sensors, open-source data processing controllers and SoC computers. Data acquisition was done in real-time, stored in InfluxDB database and graphically presented by Grafana visualization tool.

Advanced Safety Programmable Logic Controller and Operational Programmable Logic Controller for Data Acquisition Control System of SADHANA Sodium Loop

The SAfety Decay Heat Analysis in NAtrium (SADHANA) sodium loop is designed with two PLC controllers. All safety control interlocks are monitored by a Safety programmable logic controller (SPLC), and temperature control, including averaging ON/OFF heaters, is managed by an Operational PLC (OPLC).The decay heat exchange system of the SADHANA sodium loop is evaluated using these combined safety and operational PLCs. In the case of reactor loss of offsite and onsite power, decay heat generated in the reactor core after shutdown is removed by four Safety Grade Decay Heat Removal (SGDHR) systems [1,2,3,4,5]. Each exchanger (AHX) is located outside the reactor containment building, and a sodium loop circulation is created by natural convection [6,7,8,9,10]. The purpose of construction of SADHANA loop, with simulated models of DHX and AHX to study the performance of the SGDHR system, has been planned [11,12,13,14,15]. The SADHANA sodium loop has been commissioned, operating at isothermal temperatures of 300°C and hot temperatures of 500°C/& 550°C. The smart instrumentation and control system comprising Loop Operational Data Acquisition and Control System (LODACS) and Experimental Data Acquisition System (EDAS) are used in the SADHANA sodium loop. During the tests, LODACS personal computer and Instrumentation panel monitor loop parameters such as sodium level, leak, and pressure. In SADHANA loop DHX and AHX temperature decay heat changes monitoring by EDAS. The temperature of the pool in Test vessel 4 (TV-4) is controlled by immersion type sodium heaters via PID controllers. Currently, the loop consists of 64 surface heaters, 30 immersion type sodium heaters, and 205 thermocouples (84 Nos. of operational thermocouples and 121 Nos. of experimental thermocouples). According to the experimental thermocouples are being monitored by a separate fast data acquisition (scan rate of 10 seconds) by in-house developed software. This technical paper also describes the implementation of PLCs in different data acquisition control systems.

Soft Landing Parameter Measurements for Candidate Navigation Trajectories Using Deep Learning and AI-Enabled Planetary Descent

Smart instruments, sensors, and AI technologies are playing an important role in many fields such as medical science, Earth science, astronomy physics, and space study. This article attempts to study the role of sensors, instruments, and AI (artificial intelligence) based smart technologies in lunar missions during navigation of trajectories. Lunar landing missions usually divide the power descent phase into three to four sub-phases. Each sub-phase has its own set of initial and final constraints for the desired system state. The landing systems depend on human competencies for making the most crucial landing decisions. Trajectory planning and designing are very significant in lunar missions, and it requires inputs with precision. The manual systems may be prone to errors. In contrast, AI and smart sensor-based measurements give an accurate idea about the trajectory paths and make appropriate decisions where manual systems may turn into disasters. The manual systems are either pre-fed or have manual controls to guide the trajectory. For autonomous landing problems, trajectory design is a very crucial task. The automated trajectories play a vital role in the measurement and prediction of landing state parameters of the space rocket. Nowadays, sensors, intelligent instruments, and the latest technologies go hand in hand to devise measurement methods for accurate calculations and make appropriate decisions during landing space rockets at the designated destination. Space missions are very expensive and require huge efforts to design smart systems for navigation trajectories. This paper attempts to design all possible candidates of reference navigation trajectories for autonomous lunar descent by employing 3D non-linear system dynamics with randomly chosen initial state conditions. The generated candidates do not rely on multiple hops and thus exhibit an ability to serve autonomous missions. This research work makes use of smart sensors and AI federated techniques for smartly training the system to serve the ultimate purpose. The trajectories are simulated in an automated simulating environment to perform exhaustive analyses. The results accurately approximate the trajectories analogous to their numerical counterparts and converge to their measured final state estimates. The generation rate of feasible trajectories measures the accuracy of the algorithm. The algorithm’s accuracy is near 0.87 for 100 sec flight time, which is reasonable.

Smart-Readiness Assessment of a Complex Residential Building in Ukraine

Abstract Smart building concepts are a relevant topic for the Ukrainian building sector. Although the current policy is mostly dedicated to the improvement of the current energy-saving situation in the building stock, the concept discussed could provide valuable inputs in the realization of the existing strategy. For an assessment of the expected positive impact of the implementation of smart indicators on building operation and renovation processes, as well as the predictions of scenarios of the range of the application of smart instruments, the research presented proposes a thorough analysis of the building stock in Ukraine and, in a real pilot case, the assessment of smart-readiness indicators (SRI) for a complex multi-family building. The case scenarios used, which are derived on the basis of SRI and a processed questionnaire among the habitants, are considered as the reference foundation for a practical application and disseminating the concept for similar cases.

Unsupervised neural network-enabled spatial-temporal analytics for data authenticity under environmental smart reporting system

Environment, social and governance (ESG) disclosures required on listed companies have aroused considerable interest in both academia and industry for sustainable development and investing. However, the authenticity and credibility of the ESG report exposed to the public remain in doubt due to black box-like reporting processes with massive human involvements. In this study, a framework of environmental smart reporting system (BI-ESRS) based on the blockchain and Internet of Things (IoT) technologies is developed to automate the acquisition of environment-related data and make the reporting reliable and traceable. In addition, we evaluate the authenticity of the data collected from IoT devices, considering human-made counterfeits on measuring instruments for greenwashing. It is anticipated to stimulate companies to submit high-quality data without fake. Specifically, an unsupervised neural network-enabled spatial-temporal analytics (UN-STA) method is devised to achieve anomalies detection and index the data with an authenticity rate. An artificial neural network, self-organizing map (SOM), is applied to construct the prediction model. The received signal strength indicator (RSSI) of Bluetooth low energy (BLE), the vibration amplitude of smart instruments and data uploading interval constitute the input vector for competitive learning. Finally, an experimental simulation is carried out to demonstrate the implementation of the proposed system and method, and their effectiveness has also been testified. Moreover, the sensitivity of the SOM model over the three factors has been analyzed by applying the control variate technique. This work is expected to serve as a reference for practitioners to satisfy similar requirements in the industry and inspire new ideas for scholars.

Rethinking The Electric Guitar as an Augmented Instrument

The research analyzes the main electronic and digital ensembles for electric guitar in the last decades. Through this, the sense of augmentation of the instru- ment is deepened, with special emphasis on its integration with the computer. A first section of the research is the interpretation of a series of case studies using Ableton Live software with MAX/MSP, together with a MIDI controller that homogenizes and allows performing the case studies selected for the the- sis: Electric Counterpoint (Steve Reich 1987), La cite des saules (Dufourt 1997), Trash TV Trance (Romitelli 2002) and Not I (Prins 2007). A second section is the development of an augmentation proposal for e-guitar that consists of a separation in the hands-feet interface to be able to control effects, volumetric balances and samples and loops. Finally, a reflection is articulated around the augmentation in the electric guitar and its relationship with the concepts of post digital, Smart instruments, modular flexibility and obsolescence.

Tool-tissue Forces in Surgery: A Systematic Review

Abstract Background Excessive tool-tissue interaction forces often result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. This review sought to explore the tool-tissue interaction forces exerted by instruments during surgery across different specialities, tissues, manoeuvres and experience levels. Materials & methods A PRISMA-guided systematic review was carried out using Embase, Medline and Web of Science databases. Results Of 462 articles screened, 45 studies discussing surgical tool-tissue forces were included. The studies were categorized into 9 different specialities with the mean of average forces lowest for ophthalmology (0.04N) and highest for orthopaedic surgery (210N). Nervous tissue required the least amount of force to manipulate (mean of average: 0.4N), whilst connective tissue (including bone) required the most (mean of average: 45.8). For manoeuvres, drilling recorded the highest forces (mean of average: 14N), whilst sharp dissection recorded the lowest (mean of average: 0.03N). When comparing differences in the mean of average forces between groups, novices exerted 22.7% more force than experts, and presence of a feedback mechanism (e.g. audio) reduced exerted forces by 47.9%. Conclusions The measurement of tool-tissue forces is a novel but rapidly expanding field. The range of forces applied varies according to surgical speciality, tissue, manoeuvre, operator experience and feedback provided. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring forces during surgery may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.

Smart sensor implant technology in total knee arthroplasty

Innovations in computer technology and implant design have paved the way for the development of smart instruments and intelligent implants in trauma and orthopaedics to improve patient-related functional outcomes. Sensor technology uses embedded devices that detect physical, chemical and biological signals and provide a way for these signals to be measured and recorded. Sensor technology applications have been introduced in various fields of medicine in the diagnosis, treatment and monitoring of diseases. Intelligent ‘Smart’ implants are devices that can provide diagnostic capabilities along with therapeutic benefits. In trauma and orthopaedics, applications of sensors is increasing because of the advances in microchip technologies for implant devices and research designs. It offers real-time monitoring from the signals transmitted by the embedded sensors and thus provides early management solutions. Smart orthopaedic implants have applications in total knee arthroplasty, hip arthroplasty, spine surgery, fracture healing, early detection of infection and implant loosening. Here we have explored the role of Smart sensor implant technology in total knee arthroplasty. Smart sensor assisted can be used intraoperatively to provide objective assessment of ligament and soft tissue balancing whilst maintaining the sagittal and coronal alignment to achieve desired kinematic targets following total knee arthroplasty. It can also provide post-implantation data to monitor implant performance in natural conditions and patient's clinical recovery during rehabilitation. The use of Smart Sensor implant technology in total knee arthroplasty appears to provide superior patient satisfaction rates and improved functional outcomes.

Tri-doped co-annealed zinc oxide semi-conductor synthesis and characterization: photodegradation of dyes and gas sensing applications

Despite the fact that much of the research has been performed on ZnO-based nanoparticles, still a lot of work is unexplored. The synthesis and characterization of the ZnO nanorods have been co-annealed using a simple combustion method and used for gas sensor and photocatalytic degradations applications. Herein pure and In, Sn and Sb tri-dopants were used, i.e. 0.5 at.wt% 1.0 at.wt% and 1.5 at.wt%, while their effects co-annealed on glass substrate at different temperatures at 500 °C and 1100° have been studied. These samples were coated onto the chosen substrate using spin coating technique. Crystallite scale was measured to the range of 30–50 nm. At such temperatures, the grain size measured for the samples was in range of 50–70 nm. This showed that the prepared nanorods are well crystalline and have strong optical properties to handle. Studies of X-ray diffraction showed the influential point (101). These coated samples designed for nitrogen gas sensing have been tested for the development of smart and functional instruments. Furthermore, it was observed that the samples prepared at higher temperatures exhibit better recovery and better reaction time. Valance ion process explains the gas sensors fast reaction and long recovery time. Thus prepared ZnO nanoparticles have photocatalytic degradation (99.86%) only in 55 min. We observed optimum exposure at an operating temperature of 105 °C. It is notable that morphology of susceptible layer nanoparticles is preserved based on different tri-doping concentrations. The concentration of T2-ZnO nanoparticles for photodegradation of the DR-31 dye and NO2 gas sensing applications were 1.0 at.wt%

Automatic reading recognition system of pointer meters using WSNs with on-sensor image processing

Even though smart instruments are popular in modern industrial processes, pointer meters without computer interfaces are still widely employed in many application areas. Automatic reading recognition of pointer meters based on computer vision provides a promising solution to input the meter readings to the existing monitoring system or IoT. However, most of the current reading recognition approaches for pointer meters are based on inconvenient wired systems or expensive inspection robots. Compared with a wired system, wireless sensor networks (WSNs) are low-cost, easy to install and relocate. To leverage these advantages of WSNs and address the limitation of its radio bandwidth, this paper proposes a novel automated meter reading system for pointer meter using WSNs with on-sensor image processing, in which meter image capturing, image preprocessing, and reading recognition are completed on WSNs end node, and then only the recognition result is transmitted through WSNs. The feasibility of the proposed method is evaluated by a set of experiments on the prototype. Experimental results indicate that the presented approach can recognize the meter reading with an error of 0.3% and significantly reduce the payload transmission data in the WSNs.