Raspberry Pi Zero Research Articles

Digital Analysis with the Help of an Integrated UAV System for the Surveillance of Fruit and Wine Areas

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for surveillance and monitoring the health and growth of crops from orchards with vines and fruit trees. Using low-cost sensors that accurately measure ultraviolet solar radiation was an important objective. The device, which needed to be attached to the commercial DJI Mini 4 Pro drone, had to be small, portable, and have low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2 W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions were drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the plantation state analysis results with proposed models in the geographic information system (GIS) environment are also presented. The visualization of maps indicating variations in vegetation conditions led to identifying problems such as hydric stress.

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.

ILAM: Imaging Locomotor Activity Monitor for circadian phenotyping of large‐bodied flying insects

Abstract Historically, most insect chronoecological research has used direct observations, cameras or infrared beam‐based monitors to quantify movement across timed intervals. Although some alternative DIY systems are cheaper than the current standard locomotor activity monitor, these options remain complicated to build and/or computationally intensive. We developed the imaging Locomotor Activity Monitor (iLAM), an affordable (~$75 USD/unit) system for activity quantification. The iLAM utilizes a Raspberry Pi Zero W computer and night‐vision camera inside a flight cage to photograph a population of insects at user‐defined intervals. Open‐source, modular R‐scripts process the images and output a file containing the number, size, coordinate location and timestamp of all movements (blobs) identified between consecutive images. Output can be analysed directly or converted into the standard TriKinetics DAM format. We demonstrated the flexibility and power of the iLAM system by comparing diel and circadian activity of different insect species (fireflies: Photinus marginellus, P. greeni, P. obscurellus), ecotypes (moths: Ostrinia nubilalis) and sexes (moths: O. nubilalis). Data captured by only six iLAMs ($450) identified that peak activity of O. nubilalis females (AZT: 19.2 h) occurs significantly earlier than males (22.0 h). Additionally, male moths from a univoltine population exhibited a significantly shorter endogenous period length (AZT: 21.3 h) than males from a bivoltine genetic background (22.7 h). The iLAM will serve as a valuable tool for researchers seeking to measure locomotor activity across diverse species, sexes and populations in constant and changing environments.

Brainwaves in the Cloud: Cognitive Workload Monitoring Using Deep Gated Neural Network and Industrial Internet of Things

Monitoring and classifying cognitive workload in real time is vital for optimizing human–machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility.

Decontamination Effect of Hypochlorous Acid Dry Mist on Selected Bacteria, Viruses, Spores, and Fungi as Well as on Components of Electronic Systems.

This publication presents the effect of hypochlorous acid dry mist as a disinfectant on selected bacteria, viruses, spores, and fungi as well as on portable Microlife OXY 300 finger pulse oximeters and electronic systems of Raspberry Pi Zero microcomputers. The impact of hypochlorous acid on microbiological agents was assessed at concentrations of 300, 500, and 2000 ppm of HClO according to PN-EN 17272 (Variant I). Studies of the impact of hypochlorous acid fog on electronic components were carried out in an aerosol chamber at concentrations of 500 ppm and 2000 ppm according to two models consisting of 30 (Variant II) and 90 fogging cycles (Variant III). Each cycle included the process of generating a dry mist of hypochlorous acid (25 mL/m3), decontamination of the test elements, as well as cleaning the chamber of the disinfectant agent. The exposure of the materials examined on hypochlorous acid dry mist in all variants resulted in a decrease in the number of viruses, bacteria, spores, and fungi tested. In addition, the research showed that in the variants of hypochlorous acid fogging cycles analyzed, no changes in performance parameters and no penetration of dry fog of hypochlorous acid into the interior of the tested medical devices and electronic systems were observed.

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications

Monitoring and analysis of physical activity and health conditions based on smart wearable devices

The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.

Optimization and scale up of production of the PSMA imaging agent [18F]AlF-P16-093 on a custom automated radiosynthesis platform

BackgroundRecent advancements in positron emission tomograph (PET) using prostate specific membrane antigen (PSMA)-targeted radiopharmaceuticals have changed the standard of care for prostate cancer patients by providing more accurate information during staging of primary and recurrent disease. [68Ga]Ga-P16-093 is a new PSMA-PET radiopharmaceutical that demonstrated superior imaging performance in recent head-to-head studies with [68Ga]Ga-PSMA-11. To improve the availability of this new PSMA PET imaging agent, [18F]AlF-P16-093 was developed. The 18F-analog [18F]AlF-P16-093 has been synthesized manually at low activity levels using [18F]AlF2+ and validated in pre-clinical models. This work reports the optimization of the production of > 15 GBq of [18F]AlF-P16-093 using a custom automated synthesis platform.ResultsThe sensitivity of the radiochemical yield of [18F]AlF-P16-093 to reaction parameters of time, temperature and reagent amounts was investigated using a custom automated system. The automated system is a low-cost, cassette-based system designed for 1-pot syntheses with flow-controlled solid phase extraction (SPE) workup and is based on the Raspberry Pi Zero 2 microcomputer/Python3 ecosystem. The optimized none-decay-corrected yield was 52 ± 4% (N = 3; 17.5 ± 2.2 GBq) with a molar activity of 109 ± 14 GBq/µmole and a radiochemical purity of 98.6 ± 0.6%. Run time was 30 min. A two-step sequence was used: SPE-purified [18F]F− was reacted with 80 nmoles of freeze-dried AlCl3·6H2O at 65 °C for 5 min followed by reaction with 160 nmoles of P16-093 ligand at 40 °C for 4 min in a 1:1 mixture of ethanol:0.5 M pH 4.5 NaOAc buffer. The mixture was purified by SPE (> 97% recovery). The final product formulation (5 mM pH 7 phosphate buffer with saline) exhibited a rate of decline in radiochemical purity of ~ 1.4%/h which was slowed to ~ 0.4%/h when stored at 4 °C.ConclusionThe optimized method using a custom automated system enabled the efficient (> 50% none-decay-corrected yield) production of [18F]AlF-P16-093 with high radiochemical purity (> 95%). The method and automation system are simple and robust, facilitating further clinical studies with [18F]AlF-P16-093.

Monitoring Baby Incubator Central through Internet of Things (IoT) based on Raspberry Pi Zero W with Personal Computer View

kids born before 37 weeks of pregnancy or weighing less than 2500 grams are considered premature, whereas kids born between 38 and 40 weeks of pregnancy and between 2500 to 4000 grams are considered full-term. Given that their organ systems are still developing within the womb, premature infants find it difficult to adjust to life outside the womb. As a result, special consideration must be made. They include modifying the environment's temperature, humidity, and oxygen needs to reflect those of the mother's womb. These conditions might be replaced with a baby incubator. This tool's creation is intended to make it easier for midwives and other healthcare professionals to keep an eye on many baby incubators. The Internet of Things (IoT) system is used by this instrument to transfer data. Using three ESP32 modules that have been put together to create modules that can collect data and have that data analyzed by a server (central monitoring) Raspberry Pi Zero W. Data will be sent via Internet of Things (IoT) technology, and the website will display the data. Two tests were conducted at 32 degrees Celsius, one at 34 degrees Celsius, and one at 36 degrees Celsius for a total of five tests. This technique was developed using a form of pre-experimental, after-only study. In this configuration, researchers may only see the module reading results; incubator analyzer data are not shown. Error value 3 in monitoring at 32 degrees Celsius has a maximum error of -0.04 percent. The largest error value occurs when the temperature is set to 34 degrees Celsius, when the monitoring error value is -0.016%. Monitoring inaccuracy is at its highest, 0.01%, when the temperature is 36 degrees Celsius. The monitoring 3 error value is most at 32 degrees Celsius (-0.025 percent), followed by 34 degrees Celsius (0.031 percent), and finally 36 degrees Celsius (0.049 percent), as shown by data on noise measurements. The findings demonstrate that each measurement performed by the module still contains mistakes. Medical staff should find it easier to concurrently monitor many infant incubators thanks to this discovery.

Object classification and visualization with edge artificial intelligence for a customized camera trap platform

The camera traps have revolutionized the image and video capture in ecology and are often used to monitor and record animal presence. With miniaturization of low power electronic devices, better battery technologies, and software advancements, it has become possible to use the edge devices, such as Raspberry Pi as camera traps that can not only capture images and videos, but can also enable sophisticated image processing, and off-site communications. These developments can help to provide near real-time insights and reduce the manual processing of images. The on-board image classification and visualization is facilitated by the advancements in the Deep Neural Networks (DNN), transfer learning approaches, and software libraries.This paper provides an investigation of image classification with transfer learning approaches using pre-trained DNN models, and visualizations with Explainable Artificial Intelligence (XAI) techniques on Raspberry Pi Zero (RPi-Z) edge device. The MobileNetV2 model was used for image classification on the Florida-Part1 dataset obtaining the results for precision, recall, and F1-score as 0.95, 0.96, and 0.95 respectively. We also compared the model performance of MobileNetV2, EfficientNetV2B0, and MobileViT models for classification on the Extinction dataset with the best results for precision, recall, and F1-score as 0.97, 0.96, and 0.96 respectively, obtained with the EfficientNetV2B0 model. Two XAI techniques, Gradient-weighted-Class Activation Mapping (Grad-CAM) and Occlusion Sensitivity were used for visualization through heatmaps, to highlight the relative importance of the image areas contributing to the DNN model's prediction, that can also help to understand the model's performance and bias. The results provide practical use case scenarios for utilizing the transfer learning approaches, model optimization and deployment to edge devices, and model visualizations in ecological research.

Artificial Intelligent-Based Wake Word Detection at Edge Device

Deep Neural Network based wake word (such as Hi Alexa or Hey Siri) systems allow increasingly accurate speech communication between humans and machines. However, this setup requires high processing power or cloud services which may not be accessible by edge devices. Currently, the accuracy of machine learning methods for cloudless edge devices in voice activation hovers below 90%. This paper explores wake word implementation on edge devices using a 2-Dimensional Convolutional Neural Network (CNN) with improved and balanced accuracy and latency. The proposed CNN model is created, trained and quantized using TensorFlow on a PC and exported to a Raspberry Pi Zero 2 W. The quantization method reduces the model size by 20% and spectral gating is adopted to lower wake word inaccuracy detection in moderately noisy environment. The proposed system achieved more than 90% wake word detection accuracy across 30 to 50 dB background noise with an average of 1.03 second of response time for the intended user. The result shows low-powered edge device still offers competitive performance for detecting wake word without cloud services.

Neural Network-Based Solar Irradiance Forecast for Edge Computing Devices

Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, which is the key factor for controlling the variations in the PV power production. First, it introduces the Xception long short-term memory (XceptionLSTM) cell tailored for recurrent neural networks (RNN). Second, it presents the novel irradiance forecasting model that consists of a sequence-to-sequence image regression NNs in the form of a spatio-temporal encoder–decoder including Xception layers in the spatial encoder, the novel XceptionLSTM in the temporal encoder and decoder and a multilayer perceptron in the spatial decoder. The proposed model achieves a forecast skill of 16.57% for a horizon of 5 min when compared to the persistence model. Moreover, the proposed model is designed for execution on edge computing devices and the real-time application of the inference on the Raspberry Pi 4 Model B 8 GB and the Raspberry Pi Zero 2W validates the results.

TelePi: an affordable telepathology microscope camera system anyone can build and use

Telepathology facilitates histological diagnoses through sharing expertise between pathologists. However, the associated costs are high and frequently prohibitive, especially in low-resource settings, where telepathology would paradoxically be of paramount importance due to a paucity of pathologists.We have constructed a telepathology system (TelePi) with a budget of < €120 using the small, single-board computer Raspberry Pi Zero and its High-Quality Camera Module in conjunction with a standard microscope and open-source software. The system requires no maintenance costs or service contracts, has a small footprint, can be moved and shared across several microscopes, and is independent from other computer operating systems. TelePi uses a responsive and high-resolution web-based live stream which allows remote consultation between two or more locations. TelePi can serve as a telepathology system for remote diagnostics of frozen sections. Additionally, it can be used as a standard microscope camera for teaching of medical students and for basic research. The quality of the TelePi system compared favorable to a commercially available telepathology system that exceed its cost by more than 125-fold. Additionally, still images are of publication quality equal to that of a whole slide scanner that costs 800 times more.In summary, TelePi is an affordable, versatile, and inexpensive camera system that potentially enables telepathology in low-resource settings without sacrificing image quality.

Frappe: fast fiducial detection on low cost hardware

Square fiducial markers are widely used in robotics to easily obtain pose and other information about the world from camera images. Processing the images to extract the markers is usually performed centrally with standard libraries but the code is typically aimed at PC-level hardware. Platforms with constrained processing power have difficulty handling multiple camera streams at real-time refresh rates. We introduce the Frappe (Fiducial Recognition Accelerated with Parallel Processing Elements) algorithm for detecting and decoding the popular ArUco tags. Designed to be implemented on the low cost hardware of the Raspberry Pi Zero, we show tag detection and decoding on images of 640 × 480 resolution exceeding 60 Hz, five times faster than the standard ArUco library, while maintaining similar detection performance and using much less energy. Using Frappe, we demonstrate improved real-world performance on a visual navigation task with our DOTS robot.

Bio-inspired algorithm for decisioning wireless access point installation

&lt;p class="p1"&gt;&lt;span lang="EN-US"&gt;This paper presents the bio-inspired algorithms for decisioning wireless access point (AP) installation. In order to achieve the desired coverage capability of APs, the bio-inspired algorithms are applied for robust competition and optimization. The main objective is to determine the optimal number of APs with the high coverage capability in the concerning area using the genetic and ant colony optimization algorithms. Received signal strength indicator (RSSI) and line-of-sight (LoS) gradient approach are the most important parameters for AP installation depending on the AP signal strength. Practical experiments are tested on the embedded system using Xilinx Kria KR260 and Raspberry Pi Zero 2W boards at the tested room size about 16 m wide and 40 m long inside the building. Xilinx Kria KR260 board is used to calculate the number of AP installation and localization compared to Xcode. Then, Raspberry Pi Zero 2W board is the representation of wireless AP for measuring the signal in the testing area. Experiment results show that maximum received signals strength is equal to -35 dBm at 6 m and there are six APs installation with high coverage area and maximum received signal strength at the area of 16×40 m&lt;sup&gt;2&lt;/sup&gt;.&lt;/span&gt;&lt;/p&gt;

Smart Card-based Access Control System using Isolated Many-to-Many Authentication Scheme for Electric Vehicle Charging Stations

In recent years, the Internet of Things (IoT) trend has been adopted very quickly. The rapid growth of IoT has increased the need for physical access control systems (ACS) for IoT devices, especially for IoT devices containing confidential data or other potential security risks. This research focused on many-to-many ACS, a type of ACS in which many resource-owners and resource-users are involved in the same system. This type of system is advantageous in that the user can conveniently access resources from different resource-owners using the same system. However, such a system may create a situation where parties involved in the system have their data leaked because of the large number of parties involved in the system. Therefore, ‘isolation’ of the parties involved is needed. This research simulated the use of smart cards to access electric vehicle (EV) charging stations that implement an isolated many-to-many authentication scheme. Two ESP8266 MCUs, one RC522 RFID reader, and an LED represented an EV charging station. Each institute used a Raspberry Pi Zero W as the web and database server. This research also used VPN and HTTPS protocols to isolate each institute’s assets. Every component of the system was successfully implemented and tested functionally.

Monitoring Baby Incubator Central through Internet of Things (IoT) based on Raspberry Pi Zero W with Computer Monitoring)

Premature infants, with a gestational age of 37 weeks or less and weighing less than 2500 grams, face challenges in adapting to the outside world due to the underdeveloped state of their organ systems. Special care is essential to support their growth and development, including maintaining optimal temperature, humidity, and oxygen levels similar to those experienced in the mother's womb. Baby incubators play a crucial role in providing these conditions. This study aims to develop a convenient monitoring tool for midwives and healthcare workers responsible for multiple baby incubators. The tool operates independently and is compatible with various brands of baby incubators. Leveraging an IoT system, it utilizes ESP32 and Raspberry Pi Zero W modules for data transmission. The tool incorporates a DS18B20 sensor for monitoring skin temperature and a DHT22 sensor for monitoring the temperature inside the incubator chamber. Using a pre-experimental design with an after-only research design, the tool's measurements are compared against an incubator analyzer. The study reveals that the developed module still exhibits some measurement errors. However, despite these limitations, the research is expected to greatly assist medical personnel in simultaneously monitoring multiple baby incubators.&#x0D; Keywords : Premature infants, Baby incubators, IoT, Temperature, ESP32, Raspberry Pi Zero W

Improving the Computational Efficiency of ROVIO

ROVIO is one of the state-of-the-art monocular visual inertial odometry algorithms. It uses an Iterative Extended Kalman Filter (IEKF) to align visual features and update the vehicle state simultaneously by including the feature locations in the state vector of the IEKF. This algorithm is single-core intensive, which allows the other cores to be used for other algorithms, such as object detection and path optimization. However, the computational cost of the algorithm grows rapidly with the maximum number of features to track. Each feature adds three new states (a 2D bearing vector and inverse depth), leading to bigger matrix multiplications that are computationally expensive. The main computational load of ROVIO is the iterative update step of the IEKF. In this work, we reduce the average computational cost of ROVIO by [Formula: see text] on an NVIDIA Jetson TX2, without affecting the accuracy of the algorithm. This computational gain is mainly achieved by exploiting the sparse matrices in ROVIO. Furthermore, we reduce the computational peaks by pre-selecting new features based on their already calculated FAST score. The combination of both modifications allows us to run ROVIO on the computationally restricted Raspberry Pi Zero 2[Formula: see text]W.

A REPLICABLE OPEN-SOURCE MULTI-CAMERA SYSTEM FOR LOW-COST 4D GLACIER MONITORING

Abstract. Image-based monitoring has emerged as a prevalent technique for sensing mountain environments. Monoscopic time-lapse cameras, which rely on digital image correlation to quantify glacier motion, have limitations due to the need for a Digital Elevation Model for deriving 3D flow velocity fields. Multi-camera systems overcome this limitation, as they allow for a 3D reconstruction of the scene. This paper presents a replicable low-cost stereoscopic system designed for 4D glacier monitoring. The system consists of independent and autonomous units, built from off-the-shelves components, such as a DSLR camera, an Arduino microcontroller, and a Raspberry Pi Zero, reducing costs compared to pre-built time-lapse cameras. The units are energetically self-sufficient and resistant to harsh alpine conditions. The system was successfully tested for more than a year to monitor the northwest terminus of the Belvedere Glacier (Italian Alps). Daily stereo-pairs acquired were processed with Structure-from-Motion to derive 3D point clouds of the glacier terminus and estimate glacier retreat and ice volume loss. By combining the information about ice volume loss with ablation estimates and ice flow velocity information, e.g., derived from monoscopic-camera time series, a multi-camera system enables a comprehensive understanding of sub-seasonal glacier dynamics.

Possibilities of Using Bluetooth Low Energy Beacon Technology to Locate Objects Internally: A Case Study

The developments that are occurring in relation to Industry 4.0 are making it possible to automate a huge number of production activities. Automation includes the possibility of automatically identifying individual elements of a system. One of the options for doing this involves the use of Bluetooth Low Energy technology. The system’s advantages lie in its wide availability, economic simplicity, ability to design individual system elements, and overall system architecture. The system applied in the case study presented in this article consisted of beacons from Accent Systems and identification gateways based on the Raspberry Pi Zero W device. During several hours of testing, the functionality and reliability of all system components was demonstrated. The measurements showed that the system was able to determine the distance from a gate in line of sight with 94% accuracy. With regards to indirect visibility, when a metal crate was used to shield the beacon from the gateway, the system was able to determine the exact distance only 22% of the time. However, the variance between the actual and measured values was found to be small, therefore proving sufficient for most use cases. The major advantage of Bluetooth Low Energy beacons, and Bluetooth technology in general, is its massive ubiquity in the market. Since the Bluetooth module is part of every smartphone, this system can be made available to a wide range of users.