Active Sensor Research Articles

Commissioning of Pixel Sensor Telescope for Monolithic Active Pixel Sensor Characterization

The Monolithic Active Pixel Sensors (MAPS) have been promisingly demonstrated as a core part of tracking detectors used in many experiments. In order to obtain decent particle tracks generated after high-energy particle collision, it is especially important that the detector must consist of high-performance MAPS. A pixel sensor telescope is a tool employed to investigate the properties of MAPS using high-energy particle test beams. At the Synchrotron Light Research Institute (SLRI), the pixel sensor telescope assembled with five layers of 50-µm thick pixel sensors has been commissioned and tested both in the laboratory with a radioactive source and by a high-energy electron test beam produced at the SLRI Beam Test Facility. With sensor signal threshold ranging from 9.73 to 10.15 DAC and noises between 0.62 and 0.87 DAC, each plane of the pixel sensor telescope can detect particles decaying from the radioactive source. Moreover, the test beam profiles have been measured to be 4.00 - 4.56 mm and 1.15 - 2.29 mm in horizontal and vertical axes, respectively. Correlations between each plane have been observed and confirm the proper operation of the pixel sensor telescope. Consequently, the pixel sensor telescope can be used to characterize the pixel sensor prototype.

Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements

In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with semi-passive elements that can receive the signal in the active sensing mode. Ultimately, our goal is to minimize the channel estimation error using the received signal at the base station and additional information acquired from a small number of active sensors at the IRS. Unlike recent works on channel estimation with semi-passive elements that require both uplink and downlink training signals to estimate the UE-IRS and IRS-BS links, we only use uplink training signals to estimate all the links. To compute the minimum mean squared error (MMSE) estimates of all the links, we propose a novel variational inference-sparse Bayesian learning (VI-SBL) channel estimator that performs approximate posterior inference on the channel using VI with the mean-field approximation under the SBL framework. The simulation results show that VI-SBL outperforms the state-of-the-art baselines for IRS with passive reflecting elements in terms of the channel estimation accuracy and training overhead. Furthermore, VI-SBL with semi-passive elements is shown to be more spectral- and energy-efficient than the baselines with passive reflecting elements.

Vision-based autonomous mapping and exploration on robot tracked vehicle

Vision based mapping is an emerging technology with decades of research advancements. The most famous mapping method available is silmuntaneous localization and mapping (SLAM) which provide an accurate map projected in a simulation. Unfortunately, SLAM requires an active sensor in order to acquire the data from its environment opposite the vision-based mapping which requires a passive sensor to collect data. This project aims to develop an autonomous mapping and exploration algorithm, design a controller for the robot-tracked vehicle and analyze the accuracy of the algorithm. The problem in autonomous mapping is precision, limitation of computational power and complex computation. So, the algorithm will be based on the visual odometer algorithm through a single-visual sensor. The robot tracker has also been designed and implemented on Raspberry Pi 3. The accuracy of two object with different height was calculated to ensure the validation of the algorithm being able to project the real object in 3D projection. The result for the task is shown in figures as to present the capability of the algorithm in projecting the map in 3D projection. The algorithm works as expected but still requires improvements to increase the precision of the map projection.

Green synthesis of elephant manure-derived carbon dots and multifunctional applications: Metal sensing, antioxidant, and rice plant promotion

Carbon dots, the carbon nanomaterials, have been greatly developed for several applications including fluorescent sensors for metal ion detection, antioxidant activity, and plant growth promotion due to their excellent photophysical properties, non-toxicity, and high quantum yield. Therefore, this research focused on the synthesis of multifunctional carbon dots from elephant manure via green hydrothermal synthesis owing to its chemical composition of cellulose, hemicellulose, and lignin. The as-synthesized carbon dots exhibited blue fluorescence emission under UV light and the excitation wavelength-dependent fluorescence enhancement. Carbon dots could be further used as a “turn-off” fluorescence sensor for Hg2+ detection as a part of environmental monitoring. Furthermore, the as-synthesized carbon dots containing high phenolic content showed the antioxidant activity and promoted rice growth with an increase in the characteristic aroma compound (2-acetyl-1-pyrroline), confirmed by GC–MS analysis.

Sensors Work in Agriculture: Where Are We? What Are the Prospects?

The increased food requirement puts intense pressure on the agriculture community to grow more from the same resources resulting in people leaving the farming business. This happened not exclusively due to the industrial pressure to produce more but to the lack of technology adoption among growers. The use of the sensor in agriculture is not new, but its adoption among agriculture producers is a challenge for industry and scientists. This study aimed to determine sensors used in agricultural fields with challenges and prospects. The study found that sensors have successfully been used at the industry level with highly skilled labor; however, their adoption is challenging in rural agriculture systems due to the lack of a support system. The study found that the sensors used in predicting crop parameters, yield, quality, insect attacks, leaf damage, and several plants are crucial parameters to study. Sensors, particularly ground-based active optical sensors, have performed well while developing algorithms where soil parameters, environmental factors, and sensors have successfully predicted crop yield and quality.

Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy

Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy.

AUV Collision Avoidance Planning Method Based on Deep Deterministic Policy Gradient

Collision avoidance planning has always been a hot and important issue in the field of unmanned aircraft research. In this article, we describe an online collision avoidance planning algorithm for autonomous underwater vehicle (AUV) autonomous navigation, which relies on its own active sonar sensor to detect obstacles. The improved particle swarm optimization (I-PSO) algorithm is used to complete the path planning of the AUV under the known environment, and we use it as a benchmark to improve the fitness function and inertia weight of the algorithm. Traditional path-planning algorithms rely on accurate environment maps, where re-adapting the generated path can be highly demanding in terms of computational cost. We propose a deep reinforcement learning (DRL) algorithm based on collision avoidance tasks. The algorithm discussed in this paper takes into account the relative position of the target point and the rate of heading change from the previous timestep. Its reward function considers the target point, running time and turning angle at the same time. Compared with the LSTM structure, the Gated Recurrent Unit (GRU) network has fewer parameters, which helps to save training time. A series of simulation results show that the proposed deep deterministic policy gradient (DDPG) algorithm can obtain excellent results in simple and complex environments.

Leveraging Accelerometer Data for Lameness Detection in Dairy Cows: A Longitudinal Study of Six Farms in Germany

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015–2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock.

CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations

Abstract. CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).

Smart Home Automated Control System using Android Application Based on Arduino

Smart Home System (SHS) is a dwelling incorporating a communications network that connects the electrical appliances and services allowing them to be remotely controlled, monitored or accessed. SHS includes different approaches to achieve multiple objectives range from enhancing comfort in daily life to enabling a more independent life for elderly and handicapped people. In this paper, the main four fields for SHS which are, home automation and remote monitoring, environmental monitoring, including humidity, temperature, fault tracking and management and finally the health monitoring has been considered. The system design is based on the Microcontroller MIKRO C software; multiple passive and active sensors and also a wireless internet services which is used in different monitoring and control processes. This paper presents the hardware implementation of a multiple- form control system for house automation and combines both hardware and software technologies. The system results show that it can be classified as a comfortable, secure, private, economic and safe system in addition to its great flexibility and reliability.

Beam test of a 180 nm CMOS Pixel Sensor for the CEPC vertex detector

The proposed Circular Electron Positron Collider (CEPC) imposes new challenges for the vertex detector in terms of pixel size and material budget. A Monolithic Active Pixel Sensor (MAPS) prototype called TaichuPix, based on a column drain readout architecture, has been developed to address the need for high spatial resolution. In order to evaluate the performance of the TaichuPix-3 chips, a beam test was carried out at DESY II TB21 in December 2022. Meanwhile, the Data Acquisition (DAQ) for a multi-plane configuration was tested during the beam test. This work presents the characterization of the TaichuPix-3 chips with two different processes, including cluster size, spatial resolution, and detection efficiency. The analysis results indicate the spatial resolution better than 5µm and the detection efficiency exceeding 99.5% for TaichuPix-3 chips with the two different processes.

An energy-efficient irregular hexagonal tessellation-based approach for connected k-coverage in planar wireless sensor networks

In the design of planar wireless sensor networks (PWSNs), the preliminary tasks are to achieve both coverage and connectivity, which are essential for the correct operation of this type of network. A PWSN is said to be in perfect operational condition only when it is capable of guaranteeing both coverage of the field and connectivity among all the active sensor nodes. In order to achieve both coverage and connectivity in PWSNs, we intend to solve the problem of connected k-coverage in PWSNs, where each point in a field of interest is covered (or sensed) by at least k sensor nodes (k > 1) simultaneously and all the participating sensor nodes in the k-coverage process are connected to each other. In our study, we found an irregular hexagon, denoted by IrHx(rs/n), as the best polygon for tessellating a field of interest and allowing to deploy sensor nodes, where n > 1 is a natural number and rs is the radius of the sensing range of the sensor nodes. First, we construct our proposed irregular hexagon, IrHx(rs/n), which forms a tile, using the regular hexagon. Second, we compute the minimum sensor density required to k-cover a planar field of interest using our proposed IrHx(rs/n) tile. Third, we establish a relationship between the sensing and communication radii of the sensor nodes to ensure network connectivity in k-covered PWSNs. Finally, we substantiate our theory with various simulation results.

Activity budget and movement patterns of Brown Swiss and Alpine Grey lactating cows during summer grazing in alpine pastures

We used GPS tracking to monitor the grazing patterns of Brown Swiss and Alpine grey lactating cows on an alpine summer pasture (2038 m a.s.l.; SD = 146) in the Dolomites. The pasture (171 ha) was managed with a continuous grazing system (0.52 LU/ha) with morning and evening milking in the barn, guided grazing during the ‘day’, and free grazing at ‘night’. GPS positions were collected from 8 Brown Swiss multiparous and 9 Alpine Grey (4 primiparous and 5 multiparous) cows every two minutes. We inferred behaviours (grazing, resting, walking) from movement metrics, activity sensors and direct behavioural observations. After excluding milking periods, the cows grazed for 8 h/d, rested 10–11 h/d, and walked for 1.5/d. Grazing extended into late evening after milking, and resting prevailed throughout the ‘night’ until the morning milking. When grazing and resting, cows mainly used grasslands as the preferred habitat, but forest and sparse shrub were also used remarkably without consistent negative or positive selection. The pasture use was highly heterogeneous, with higher animal loads close to the barn, especially at night, and in areas with gentler slopes. Alpine Grey primiparous cows were less limited by slope and distance from the barn in their movement but were more selective in habitat use than multiparous cows. Differences between multiparous cows of the two breeds were less marked. Further studies should help understand the internal and external drivers of cattle grazing patterns to devise management practices combining animals’ productivity and welfare with the conservation of the grassland ecosystem services.

From Smart City to Smart Society: A quality-of-life ontological model for problem detection from user-generated content

Social-media platforms have become a global phenomenon of communication, where users publish content in text, images, video, audio or a combination of them to convey opinions, report facts that are happening or show current situations of interest. Smart-city applications can benefit from social media and digital participatory platforms when citizens become active social sensors of the problems that occur in their communities. Indeed, systems that analyse and interpret user-generated content can extract actionable information from the digital world to improve citizens’ quality of life. This article aims to model the knowledge required for automatic problem detection to reproduce citizens’ awareness of problems from the analysis of text-based user-generated content items. Therefore, this research focuses on two primary goals. On the one hand, we present the underpinnings of the ontological model that categorises the types of problems affecting citizens’ quality of life in society. In this regard, this study contributes significantly to developing an ontology based on the social-sensing paradigm to support the advance of smart societies. On the other hand, we describe the architecture of the text-processing module that relies on such an ontology to perform problem detection, which involves the tasks of topic categorisation and keyword recognition.

AI-enabled crack-length estimation from acoustic emission signal signatures

Abstract This article addresses the classification of fatigue crack length using artificial intelligence (AI) applied to acoustic emission (AE) signals. The AE signals were collected during fatigue of two specimen types. One specimen type had a 1-mm hole for crack initiation. The other specimen type had a 150-micron wide slit of various lengths. Fatigue testing was performed under stress-intensity-factor control to moderate crack advancement. The slit specimen produced AE signals only from crack advancement at the slit tips whereas the 1-mm hole specimens produced AE signals both from crack tip advancement and crack rubbing or clapping. The AE signals were captured with a piezoelectric wafer active sensor (PWAS) array connected to MISTRAS instrumentation and AEwin software. The collected AE signals were preprocessed using time-of-flight filtering and denoising. Choi Williams transform converted time-domain AE-signals into spectrograms. To apply machine learning, the spectrogram images were used as input data for the training, validation, and testing of a GoogLeNet convolutional neural network (CNN). The CNN was trained to sort the AE signals into crack-length classes. CNN performance enhancements, including synthetic data generation and class balancing were developed. A three-class example with crack lengths of (i) 10-12 mm; (ii) 12-14 mm; and (iii) 14-16 mm is provided. Our AI approach was able to classify the AE signals into these three classes with 91% accuracy thus proving that the AE signals contain sufficient information for crack estimation using an AI-enabled approach.

2 Satellite-Based Decision Support Tools to Assist Grazing Cattle Production

Abstract Grasslands cover approximately 40.5% of the surface of the earth and 80% of agriculturally productive land. After forests, grasslands are the primary carbon sink source and the most used feed source for livestock production. Regularly monitoring grasslands assures efficient management and sustainability of pasture-based production systems. Conventional ground-based methods to monitor grassland production and management rely on field measurements, which are time-consuming and usually restricted to small-scale assessment. Using satellite information allows for large-scale monitoring of grasslands and capturing the spatial variability of the land surface with high temporal resolution. Various methods for grassland monitoring based on satellite data can be applied, such as classifications, correlations/regression analyses, and time series analyses. Depending on the purpose of the application, these methods are sometimes combined to derive grassland management and production information. The ability of satellite-based data to quantify vegetation characteristics depends on the type of sensor and instrumentation features, such as spectral, radiometric, spatial, and temporal resolution, polarization, and angularity. The models to estimate grassland biomass based on remote sensing have been chiefly focused on optical systems. The spectral reflectance of raw bands and vegetation indices were used as proxies to investigate spatial and temporal patterns of grassland production. Optical (multispectral or hyperspectral) sensors are passive and require sunlight, so they depend highly on the weather (cloud) and light conditions. Thus, there has been increasing interest in active sensors, such as Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LIDAR) sensors, which are not constrained due to clouds but are more complex. Using satellite data in combination with field measurements has commonly yielded regression models (e.g., linear, power, logarithmic, multiple linear) for estimating grassland biomass or biophysical characteristics (e.g., chlorophyll, leaf area index) of different types of grasslands. The exponential evolution of digital computers has pushed forward machine learning-based regression methods to estimate biomass. Random forest, support vector machines, and artificial neural networks are the most used algorithms. The possibility of accurate mapping and monitoring of biomass and nutritional attributes of grasslands based on satellite provides essential insights into the decision support system for pasture management. A better understanding of the nitrogen status of pastures, forage biomass, and its nutritive value is instrumental in livestock and forage management. Timely prediction of these variables can help improve decision-making by grazing land managers on, for instance, the adjustment of stocking rate or adequate supplementation to match the needs of animals toward more sustainable production. Future use of satellite-based grazing models in tandem with ruminant nutrition models will enable to development of decision-support tools to assist with many aspects of livestock production in diverse environmental conditions and accounting for temporal variability. (FAPESP #2020/14367-7)

Approaches, potential, and challenges in the use of remote sensing to study mangrove and other tropical wetland forests

Tropical wetland forests are fragile ecosystems facing critical risks due to global warming and other anthropogenic threats. Hence, gathering accurate and reliable information on them is urgent. Although remote sensing has demonstrated great potential in studying terrestrial ecosystems, remote sensing-based wetland forest research is still in an early stage of development. Mapping wetland forests, particularly mangrove forests, was an initial goal of this approach and is a task that still faces methodological challenges. Initially based on aerial photography only, wetland forest mapping through remote sensing underwent explosive diversification after the launching of artificial satellites in the 1970s. Later, precision in wetland forest mapping increased with the combination of hyperspectral, multispectral, and high and very high resolution imagery. Accurate delimitation of wetland forest extent is also necessary to assess their temporal dynamics (losses, gains, and horizontal displacement). Despite the prevalence of mapping studies, current remote sensing-based research on wetland forests addresses new questions and novel aims, such as describing and predicting wetland forest attributes through mathematical modeling. Although this approach has made substantial progress in recent decades, modeling and predicting wetland forest attributes remain insufficiently explored fields of research. Combining active and passive sensors is a promising alternative to provide a more accurate picture of these communities’ attributes. In particular, LiDAR and radar-based technologies may help overcome difficulties encountered in older studies. In the future, we will witness conceptual and methodological progress that will enable us to surmount the remaining challenges.

Satellite–Derived Bathymetry in Shallow Waters: Evaluation of Gokturk-1 Satellite and a Novel Approach

For more than 50 years, marine and remote sensing researchers have investigated the methods of bathymetry extraction by means of active (altimetry) and passive (optics) satellite sensors. These methods, in general, are referred to as satellite-derived bathymetry (SDB). With the advances in sensor capabilities and computational power and recognition by the International Hydrographic Organization (IHO), SDB has been more popular than ever in the last 10 years. Despite a significant increase in the number of studies on the topic, the performance of the method is still variable, mainly due to environmental factors, the quality of the deliverables by sensors, the use of different algorithms, and the changeability in parameterization. In this study, we investigated the capability of Gokturk-1 satellite in SDB for the very first time at Horseshoe Island, Antarctica, using the random forest- and extreme gradient boosting machine learning-based regressors. All the images are atmospherically corrected by ATCOR, and only the top-performing algorithms are utilized. The bathymetry predictions made by employing Gokturk-1 imagery showed admissible results in accordance with the IHO standards. Furthermore, pixel brightness values calculated from Sentinel-2 MSI and tasseled cap transformation are introduced to the algorithms while being applied to Sentinel-2, Landsat-8, and Gokturk-1 multispectral images at the second stage. The results indicated that the bathymetric inversion performance of the Gokturk-1 satellite is in line with the Landsat-8 and Sentienl-2 satellites with a better spatial resolution. More importantly, the addition of a brightness value parameter significantly improves root mean square error, mean average error, coefficient of determination metrics, and, consequently, the performance of the bathymetry extraction.

A review of recent advance of ship detection in single-channel SAR images

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD).

Design of Nupix-A2, a Monolithic Active Pixel Sensor for heavy-ion physics

The High Intensity heavy-ion Accelerator Facility (HIAF) is being constructed to generate intense beams of primary and radioactive ion for a wide range of research fields. Hence, a Monolithic Active Pixel Sensor (MAPS) named Nupix-A2 has been developed in a 130-nm High Resistivity CMOS process. The Nupix-A2 can simultaneously measure the particle hit' energy, arrival time, and position. It consists of a 128 × 128 pixel array, a digital-to-analog converter array, and a digital control module. The Nupix-A2 can measure energy deposition from 300 e- to over 50 ke- and time duration from 13 μs to 140 μs. This sensor also offers full readout mode and fast readout mode. In full-readout mode, all pixels measure arrival time and energy, suitable for real-time beam monitoring and image reconstruction. In fast-readout mode, only particle hit's positions are detected and read out. This paper presents the design of the Nupix-A2.