Autonomous Monitoring Research Articles

Monitoring noise levels and delphinid presence through autonomous acoustic in Rio de Janeiro coastal area

In order to investigate the soundscape in a Rio de Janeiro coast site, passive acoustic monitoring was conducted during two non-consecutive weeks in the summer of 2015/2016 by deploying one SM2M + device. The equipment recorded at a 66% duty cycle with sample rate of 96 kHz and 36 dB gain. Third octave levels (TOLs) were calculated for all recordings through PAMGuide software. To search for delphinid presence, a band limited energy detector was employed using Raven 1.5 in a 512 Hann window, 50% overlap. TOLs varied across frequencies and day hours. Light hours were noisier than dark hours in 25 frequency bands (MW, p<0.01), with highest mean level being measured at 794 Hz at 06am (105.4 ± 5.4 dB re 1μPa), and lowest mean level measured at 39.8 kHz at 01am (80.3 ± 3.7 dB re 1μPa). A total of 281 delphinid sound emissions were detected in nine occasions, seven during week 1 and two during week 2. Seven detection events occurred during dark hours. Although more than one species of delphinid is known to occur in Rio de Janeiro coast, these results indicate a frequent nocturnal use of the area, which was only possible to observe through autonomous monitoring.

Machine learning for behavior classification of passively tracked vessels

Situational awareness is a constant challenge for autonomous underwater vehicles (AUVs) due to limited communication to the surface, navigation drift, and a need to operate in busy areas around other vessels. One piece of information that would be valuable to both keep AUVs safe and inform autonomous monitoring missions is the location and behavior of nearby surface vessels. Passive acoustic data collected on a hydrophone array and processed on an on-board computer can provide bearing and time-to-intercept (TTI). This information can be used to classify overall boat behavior and inform AUV response: for example, investigating an area a boat has circled or avoiding an approaching vessel for vehicle safety reasons. Simulation studies were used to characterize trajectories for simple vehicle behaviors based on the bearing and TTI data produced by an existing passive tracking system. A classifier based on K-nearest-neighbor with dynamic time warping as a distance metric was used to classify simulation data. The simulation-based classifier was applied to classify experimental tracking data on boats completing different types of behaviors using bearing/TTI from dock-based and AUV-based hydrophone arrays.

Uma revisão bibliométrica sobre a identificação de cargas similares em Smart Grid

Diante da necessidade de uso consciente dos recursos energéticos para o crescimento sustentável, o conceito Smart Grid tem servido de referência para o uso eficiente de tais recursos, sobretudo o elétrico. Entretanto, a aplicação de tal conceito nas instalações elétricas requer o desenvolvimento de dispositivos que permitam o monitoramento autônomo de aparelhos eletroeletrônicos em tais instalações. Este artigo tem como objetivo realizar uma revisão bibliométrica sobre os métodos não-intrusivo de monitoramento de cargas, a fim de mostrar o estado da arte dessa temática, isto é, os principais avanços e entraves para o desenvolvimento dessa tecnologia. É realizada uma análise quantitativa e qualitativa dos trabalhos referentes ao tema buscados via base de dados Web of Science. Como resultado de tal investigação, observa-se a relevância dos estudos nessa área, que está em pleno crescimento. Cita-se ainda o fato de o tema estar sendo investigado tanto em países desenvolvidos, como Canadá e Estados Unidos, quanto em países em desenvolvimento, como Malásia e Índia, o que mostra que existe uma preocupação mundial em se avançar em tal problema que independe da situação econômica da nação.

Autonomous monitoring framework for resource-constrained environments

ABSTRACTThe availability of low-cost imaging devices for embedded applications has enabled development of wireless monitoring systems capable of acquiring and transmitting both image and video data. Remote deployment of such systems is often constrained by limited power resources, thus a system must operate autonomously, balancing operational needs against available resources. This paper describes a framework for the design and implementation of an autonomous embedded remote monitoring system employing information-driven sensing to conserve energy and extend the system deployment lifetime.The results from two case studies show improvements over a conventional system and other similar systems through the use of intelligent algorithms for reliable event detection and enhanced system operational lifetime by efficient utilisation of limited resources. The results are applicable to low-power battery-operated field devices offering better resource utilisation in disaster management systems, intelligent transportation and remote monitoring.

In-Channel 3D Models of Riverine Environments for Hydromorphological Characterization

Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by the Water Framework Directive is characterising the hydromorphology. Recent advances in autonomous operation and the spatial coverage of monitoring systems enables more rapid 3D models of the river environment to be produced. This study presents a Structure from Motion (SfM) semi-autonomous based framework for the estimation of key reach hydromorphological measures such as water surface area, wetted water width, bank height, bank slope and bank-full width, using in-channel stereo-imagery. The framework relies on a stereo-camera that could be positioned on an autonomous boat. The proposed approach is demonstrated along three 40 m long reaches with differing hydromorphological characteristics. Results indicated that optimal stereo-camera settings need to be selected based on the river appearance. Results also indicated that the characteristics of the reach have an impact on the estimation of the hydromorphological measures; densely vegetated banks, presence of debris and sinuosity along the reach increased the overall error in hydromorphological measure estimation. The results obtained highlight a potential way forward towards the autonomous monitoring of freshwater ecosystems.

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Activity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed.

A multi-modal energy harvesting device for low-frequency vibrations

This paper presents an innovative design of a low-frequency multi-modal system vibration-based energy harvester (VEH) for powering wireless autonomous monitoring systems wind turbines of 30 kW. The main objective is to design an energy harvesting device capable to operate in a very low-frequency bandwidth (3 to 10 Hz) increasing as much as possible the operational bandwidth by enhancing the amplitude of the second mode of vibration. The electrical power performance is evaluated for four different energy harvesting designs, which are mainly composed of multi-beams cantilevers with tip masses. For the harvesting system with two multiple-beams trident, a rigid beam is selected to join them. This versatile geometric configuration offers the possibility to modify the vibration characteristics of the harvester in several alternative ways, not only by increasing the tip mass which may be not favorable from a structural viewpoint. The resonant frequencies values, the time voltage signals and the electric power are obtained through a finite element beam formulation early proposed by the authors, capable to modeling three dimensional systems. The numerical results are validated through experimental tests. Regarding the output power, the most promising design with two multiple-beams trident with a tip mass delivers 36.48 μW and 96.04 μW in the proposed range of operation (first two resonance frequencies 4.76 and 7.91 Hz, respectively) excited by 0.1 g of base acceleration. This clearly indicates that the device is a very good candidate for the proposed application of autonomous wireless monitoring, since the output power is larger than the minimum of 20 μW required.

Visual support system for remote control by adaptive ROI selection of monitoring robot

A visual assistance system has become attractive as a technique to improve the efficiency and stability of remote control. While an operator controls a working robot, another autonomous monitoring robot evaluates a suitable viewpoint to observe the work site, and dynamically moves to the optimal viewpoint for monitoring. Choosing the observation region (ROI: region of interest) is equivalent to deciding the action of the following autonomous monitoring system. Therefore, we focus on ROI detection in our visual support system. We propose an ROI selection method to identify the most suitable observation point and interobject relations. The monitoring robot detects a gestalt of the scene in order to identify the relations between objects. Such an adaptive ROI in real time improves the efficiency of the remote control. The experimental results indicate the effectiveness of the proposed system in terms of execution time and number of errors.

Materials and Wearable Devices for Autonomous Monitoring of Physiological Markers.

Wearable devices are gaining considerable attention owing to the ease with which they can collect crucial information in real-time, both continuously and noninvasively, regarding a wearer's health. A concise summary is given of the three main elements that enable autonomous detection and monitoring of the likelihood or the existence of a health-risk state in continuous and real-time modes, with an emphasis on emerging materials and fabrication techniques in the relevant fields. The first element is the sensing technology used in the noninvasive detection of physiological markers relevant to the state of health. The second element is self-powered devices for longer periods of use by drawing energy from bodily movement and temperature. The third element is the self-healing properties of the materials used in the wearable devices to extended usage if they become scratched or cut. Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further improvement of skin-based wearable devices are also presented and discussed.

Towards Realizing Cybermanufacturing Kiosks: Quality Assurance Challenges and Opportunities

This paper presents the cyberphysical system (CPS) challenges associated with realizing manufacturing-as-a-service paradigm, capable of creating custom functional, freeform structures. While earlier efforts have addressed the product design, resource allocation, and asset management challenges in custom manufacturing, those pertaining to quality and performance assurance have not received much attention thus far. For low volume custom manufacturing, the intelligence comprising all the steps involved in the production process and its continuous monitoring should reside in the real-time service, rather than requiring an upfront costly design process for each product. In this paper, we first outline the technical challenges associated with autonomous monitoring and control of tool motion and process parameters of a laser kirigami (origami with cuts) process to create custom freeform components, with planar facets, from peel-off sensors embedded on sheet precursors. In the sequel, we present an implementation of image-based monitoring, particularly for the estimation of the dihedral angle between the facets being bent during the laser kirigami process. As the sheet is formed, the sensors embedded on the sheets serve as a swarm of mobile sensors whose instantaneous location and orientation are discerned using optimally placed cameras to estimate the process state. This allows the tuning of process parameters, specifically the laser intensity and transverse feed rate to mitigate quality issues. The results from the current experimental investigations of the laser kirigami process suggest that the process state can be estimated to error rates of less than 2% at 10 frames per second, which is orders of magnitude faster than that achievable using the present stereo digital image correlation technologies.

Dynamic priority-based packet handling protocol for healthcare wireless body area network system

The vision of wireless body area network (WBAN) is to facilitate, improve, and have an immense impact on the healthcare system in terms of identifying the risk level or severity factor of a patient in various emergencies. Modern and technical advances in WBAN revolutionise this area for autonomous monitoring of vital signals for a longer duration as well as from a remote place. However, handling of heterogeneous packets in a fast changing healthcare scenario has continued to be an opportunity for exploration. We present a novel concept of dynamic priority-based packet handling (DPPH) which promises to add exciting capabilities to the world of WBANs. DPPH uses the principles of accurate identification and classification of heterogeneous packets to effectively determine patient's critical condition and alerts the medical person if required. In this paper, we have focused on dynamic prioritisation-based queuing, scheduling, alerting and resource sharing policies for performance enhancement. The proposed approach is validated through a comparison with existing approaches. The effectiveness of the proposed protocol is evaluated under various scenarios using a network simulator 'NS-2.35' and its performance is judged on the basis of packet delivery ratio, loss ratio, end-to-end delay, and throughput.

Dynamic priority-based packet handling protocol for healthcare wireless body area network system

The vision of wireless body area network (WBAN) is to facilitate, improve, and have an immense impact on the healthcare system in terms of identifying the risk level or severity factor of a patient in various emergencies. Modern and technical advances in WBAN revolutionise this area for autonomous monitoring of vital signals for a longer duration as well as from a remote place. However, handling of heterogeneous packets in a fast changing healthcare scenario has continued to be an opportunity for exploration. We present a novel concept of dynamic priority-based packet handling (DPPH) which promises to add exciting capabilities to the world of WBANs. DPPH uses the principles of accurate identification and classification of heterogeneous packets to effectively determine patient's critical condition and alerts the medical person if required. In this paper, we have focused on dynamic prioritisation-based queuing, scheduling, alerting and resource sharing policies for performance enhancement. The proposed approach is validated through a comparison with existing approaches. The effectiveness of the proposed protocol is evaluated under various scenarios using a network simulator 'NS-2.35' and its performance is judged on the basis of packet delivery ratio, loss ratio, end-to-end delay, and throughput.

Seen or heard? A comparison of visual and acoustic autonomous monitoring methods for investigating temporal variation in occurrence of southern right whales

Passive acoustic detectors are widely used for monitoring distribution of cetaceans. Autonomous visual methods are less frequently employed; they are limited to detections during daylight and good weather, but offer potential advantages due to certainty of species’ identification and longevity of deployment. To compare performance of acoustic and visual methods, temporal changes in distribution of southern right whales, Eubalaena australis, were monitored in the remote sub-Antarctic Auckland Islands (50°31′S, 166°16′E). A time-lapse camera was deployed for 2 years from August 2010, taking an image every 70 min. The presence of whales was scored for each image taken during daylight (n = 8295). A passive acoustic recorder was deployed in August 2011, recording for 3.75 min every hour until battery life expired after 10 months. Each recording (n = 6978) was aurally reviewed. Both methods revealed a similar seasonal distribution; maximum detection rates were in the austral winter and no whales were detected in January or February. However, at the peak of right whale occurrence, the proportion of samples with whales detected was much higher for the acoustic recorder (93.9% in August) than the time-lapse camera (14.8%). A generalised additive model fitted to the visual data revealed significant effects of sea state and visibility. Acoustic detection rates were higher, probably because detection range is greater, and less affected by weather. The solar-powered time-lapse camera system, however, functioned effectively for much longer. We discuss the relative merits of visual and acoustic detectors and attempt to draw conclusions about their efficacy for different focal species and monitoring locations.

An out-of-plane rotational energy harvesting system for low frequency environments

We present a novel design of a rotational power scavenging system as an alternative to cantilever beams attached to a hub. The device is meant to provide energy to wireless autonomous monitoring systems in low frequency environments such as wind turbines of 30kW with rotational speeds of between 50 and 150rpm. These characteristics define the bandwidth of the rotational energy harvesting system (REH) and its physical dimensions. A versatile geometric configuration with two elastic beams and two heavy masses joined by a spring is proposed. A piezoelectric sheet is mounted on the primary beam while the REH is placed on a rotating hub with the gravitational force acting as a periodic source. This kind of double-beam system offers the possibility to modify the vibration characteristics of the harvester for achieving high power density. An analytical framework using the Lagrangian formulation is derived to describe the motion of the system and the voltage output as a function of rotation speed. Several sets of experiments were performed to characterize the system and to validate the assumed hypothesis. In the experimental setup, a wireless data acquisition system based on Arduino technology was implemented to avoid slip-ring mechanisms. The results show very good agreement between the theoretical and experimental tests. Moreover, the output power of a simple harvesting circuit, which serves as an energy storage device, yields values ranging 26–105μW over the whole frequency range. This allows us to use the proposed device for the designed purpose, taking into account the power requirements of commercially available wireless transmitter systems.

A case study of an autonomous wireless sensor network system for environmental data collection

This article presents a case study of the design of an autonomous wireless sensor network for environmental monitoring to provide data for understanding the relationships among food, energy, and water systems in suburban areas in Long Island New York. The study focuses on the existing water quality data for the two counties on Long Island, Nassau and Suffolk Counties, to understand the impact of energy and food production on the coastal waters of Long Island Sound and Peconic Bay. The analysis of existing data from the US Environmental Protection Agency database reveals the challenges in using such data to understand the relationships between water quality, weather events and identified point sources of pollutants. Maintaining on-going data collection programs is expensive, time-consuming and personnel intensive which is why this project examines new ways to obtain and utilize environmental data. The researchers propose the design of an autonomous wireless sensor network environmental monitoring system with an optimal sensor placement strategy to identify targeted locations for water quality monitoring. The goal is to provide environmental data that can facilitate a better understanding of the interactions of farming activities and energy production resulting in undesirable impacts on water quality. The proposed design can be used to provide real-time data to help stakeholders improve efficiency in resource management and advance early detection of potential hazardous events. © 2017 American Institute of Chemical Engineers Environ Prog, 2017

An autonomous medical monitoring system: Validation on arrhythmia detection.

In this paper, we present a generic platform for autonomous medical monitoring and diagnostics. We validated the platform in the context of arrhythmia detection with publicly available databases. The big advantage of this platform is its capacity to deal with various types of physiological signals. Many pre-processing steps are performed to bring the input information into a uniform state that will be explored by a machine learning algorithm. Since this block plays a crucial role in the entire processing pipeline, three different methods were evaluated for detection and classification of anomalies. The results presented in this work are validated on cardiac beats, where the highest accuracy was obtained on the classification of normal beats (94%). On the other hand, atrial fibrillation and premature ventricular contraction beats were classified with an accuracy of 78%.

Optimizing stations location for urban noise continuous intelligent monitoring

In most of urban noise monitoring systems, the optimization of number and locations of autonomous monitoring stations is a Non-deterministic Polynomial Complete (NPC) problem. It is also important for the implementation of intelligent measurement networks. This paper investigates an optimization method to achieve minimum stations for urban noise intelligent monitoring. First a mathematical model for monitoring stations selection has been developed. Next, a novel hybrid Immune PSO K-means (IPKM) clustering algorithm is proposed to solve the mathematical model. The IPKM algorithm can overcome the shortcomings (e.g. slow convergence speed) of the Particle Swarm Optimization (PSO) algorithm, and help K-means clustering algorithm escaping from local optima. Finally, the methodology has been applied to QingDao urban noise intelligent monitoring networks. For comparison, the K-means algorithm and IPKM algorithm are applied to the noise grid survey datasets of 1998–2014years. The final optimized results illustrate the proposed method could perform relevant monitoring tasks with fewer monitoring stations. In addition, the importance of the proposed method is that it would be applicable for noise monitoring and noise control management problems.

Reconfigurable swarm robots for structural health monitoring: a brief review

Autonomous monitoring of infrastructure systems offers a promising alternative to manual inspection techniques which are mostly tedious, expensive and prone to error. Robot-based autonomous monitoring systems not only provide higher precision, but they also allow frequent inspection of infrastructure systems at a much lower cost. Recent advancements in robotic systems have led to the development of reconfigurable swarm robots (RSR) that can change their shape and functionality dynamically, without any external intervention. RSR have the advantages of being modular, on-site reconfigurable, multifunctional, incrementally assemble-able, reusable, fault-tolerant, and even repairable on the orbit. Newly-developed reconfigurable robots are expected to bring a radical change in the prevailing structural health monitoring techniques, thus augmenting the efficiency, accuracy and affordability of inspection operations. This paper presents a holistic review of the previous studies and state-of-the-art technologies in the field of RSR, and argues that RSR offer great potential advantages from the perspective of monitoring and assessment of civil and mechanical systems. A roadmap for future research has also been outlined based on the limitations of the current methods and anticipated needs of future inspection systems.

Coincident aboveground and belowground autonomous monitoring to quantify covariability in permafrost, soil, and vegetation properties in Arctic tundra

AbstractCoincident monitoring of the spatiotemporal distribution of and interactions between land, soil, and permafrost properties is important for advancing our understanding of ecosystem dynamics. In this study, a novel monitoring strategy was developed to quantify complex Arctic ecosystem responses to the seasonal freeze‐thaw‐growing season conditions. The strategy exploited autonomous measurements obtained through electrical resistivity tomography to monitor soil properties, pole‐mounted optical cameras to monitor vegetation dynamics, point probes to measure soil temperature, and periodic manual measurements of thaw layer thickness, snow thickness, and soil dielectric permittivity. The spatially and temporally dense monitoring data sets revealed several insights about tundra system behavior at a site located near Barrow, AK. In the active layer, the soil electrical conductivity (a proxy for soil water content) indicated an increasing positive correlation with the green chromatic coordinate (a proxy for vegetation vigor) over the growing season, with the strongest correlation (R = 0.89) near the typical peak of the growing season. Soil conductivity and green chromatic coordinate also showed significant positive correlations with thaw depth, which is influenced by soil and surface properties. In the permafrost, soil electrical conductivity revealed annual variations in solute concentration and unfrozen water content, even at temperatures well below 0°C in saline permafrost. These conditions may contribute to an acceleration of long‐term thaw in Coastal permafrost regions. Demonstration of this first aboveground and belowground geophysical monitoring approach within an Arctic ecosystem illustrates its significant potential to remotely “visualize” permafrost, soil, and vegetation ecosystem codynamics in high resolution over field relevant scales.

Perceptions of seniors with heart failure regarding autonomous zero-effort monitoring of physiological parameters in the smart-home environment

BackgroundTechnological advances are leading to the ability to autonomously monitor patient's health status in their own homes, to enable aging-in-place. ObjectivesTo understand the perceptions of seniors with heart failure (HF) regarding smart-home systems to monitor their physiological parameters. MethodsIn this qualitative study, HF outpatients were invited to a smart-home lab, where they completed a sequence of activities, during which the capacity of 5 autonomous sensing modalities was compared to gold standard measures. Afterwards, a semi-structured interview was undertaken. These were transcribed and analyzed using an interpretive-descriptive approach. ResultsFive themes emerged from the 26 interviews: (1) perceptions of technology, (2) perceived benefits of autonomous health monitoring, (3) disadvantages of autonomous monitoring, (4) lack of perceived need for continuous health monitoring, and (5) preferences for autonomous monitoring. ConclusionsPatient perception towards autonomous monitoring devices was positive, lending credence to zero-effort technology as a viable and promising approach.