scholarly journals Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1036
Author(s):  
Simon Arvidsson ◽  
Marcus Gullstrand ◽  
Beril Sirmacek ◽  
Maria Riveiro
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Sensor Data ◽  
Low Resolution ◽  
Real Time Processing ◽  
Privacy Concerns ◽  
Sensor Field ◽  
Heat Sensor ◽  
Occupancy Prediction ◽  
The Impact

Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

scholarly journals Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5497 ◽  
Author(s):  
Beril Sirmacek ◽  
Maria Riveiro
Keyword(s):  
Computer Vision ◽  
Low Cost ◽  
Elderly Care ◽  
Sensor Data ◽  
Low Resolution ◽  
Feature Classification ◽  
Advantages And Disadvantages ◽  
The People ◽  
Heat Sensor ◽  
Occupancy Prediction

Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects, which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors; however, they have not addressed nor compensated for such heat artifacts. Therefore, in this paper, we presented a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We used a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We proposed two novel workflows to predict the occupancy; one that is based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we used several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyzed noise resources that affect the heat sensor data. The experiments show that the feature classification based method gives high accuracy when the data are clean from noise artifacts. However, when there are noise artifacts, the computer vision based method can compensate for those artifacts providing robust results. Because the computer vision based method requires an empty room recording, the feature classification based method should be chosen either when there is no expectancy of seeing noise artifacts in the data or when there is no empty recording available. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

scholarly journals Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices

2020 ◽  
Author(s):  
Beril Sirmacek ◽  
Maria Riveiro
Keyword(s):  
Low Cost ◽  
Elderly Care ◽  
Sensor Data ◽  
Low Resolution ◽  
Multiple Sensors ◽  
Advantages And Disadvantages ◽  
The People ◽  
Energy Consuming ◽  
Heat Sensor ◽  
Occupancy Prediction

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

scholarly journals BIM and IoT Sensors Integration: A Framework for Consumption and Indoor Conditions Data Monitoring of Existing Buildings

2021 ◽  
Vol 13 (8) ◽  
pp. 4496
Author(s):  
Giuseppe Desogus ◽  
Emanuela Quaquero ◽  
Giulia Rubiu ◽  
Gianluca Gatto ◽  
Cristian Perra
Keyword(s):  
Energy Consumption ◽  
Low Cost ◽  
Application Programming Interface ◽  
Visual Programming ◽  
Information Modeling ◽  
Sensor Data ◽  
Time Data ◽  
Existing Building ◽  
Building Maintenance ◽  
Indoor Conditions

The low accessibility to the information regarding buildings current performances causes deep difficulties in planning appropriate interventions. Internet of Things (IoT) sensors make available a high quantity of data on energy consumptions and indoor conditions of an existing building that can drive the choice of energy retrofit interventions. Moreover, the current developments in the topic of the digital twin are leading the diffusion of Building Information Modeling (BIM) methods and tools that can provide valid support to manage all data and information for the retrofit process. This paper shows the aim and the findings of research focused on testing the integrated use of BIM methodology and IoT systems. A common data platform for the visualization of building indoor conditions (e.g., temperature, luminance etc.) and of energy consumption parameters was carried out. This platform, tested on a case study located in Italy, is developed with the integration of low-cost IoT sensors and the Revit model. To obtain a dynamic and automated exchange of data between the sensors and the BIM model, the Revit software was integrated with the Dynamo visual programming platform and with a specific Application Programming Interface (API). It is an easy and straightforward tool that can provide building managers with real-time data and information about the energy consumption and the indoor conditions of buildings, but also allows for viewing of the historical sensor data table and creating graphical historical sensor data. Furthermore, the BIM model allows the management of other useful information about the building, such as dimensional data, functions, characteristics of the components of the building, maintenance status etc., which are essential for a much more conscious, effective and accurate management of the building and for defining the most suitable retrofit scenarios.

scholarly journals Measure particulate matter by yourself: data-quality monitoring in a citizen science project

2019 ◽  
Vol 8 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Aboubakr Benabbas ◽  
Martin Geißelbrecht ◽  
Gabriel Martin Nikol ◽  
Lukas Mahr ◽  
Daniel Nähr ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Data ◽  
And Control ◽  
Future Work ◽  
The Impact

Abstract. The concern about air quality in urban areas and the impact of particulate matter (PM) on public health is turning into a big debate. A good solution to sensitize people to this issue is to involve them in the process of air quality monitoring. This paper presents contributions in the field of PM measurements using low-cost sensors. We show how a low-cost PM sensor can be extended to transfer data not only over Wi-Fi but also over the LoRa protocol. Then, we identify some of the correlations existing in the data through data analysis. Afterwards, we show how semantic technologies can help model and control sensor data quality in an increasing PM sensor network. We finally wrap up with a conclusion and plans for future work.

scholarly journals Low-Cost Eye Tracking Calibration: A Knowledge-Based Study

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5109
Author(s):  
Gonzalo Garde ◽  
Andoni Larumbe-Bergera ◽  
Benoît Bossavit ◽  
Sonia Porta ◽  
Rafael Cabeza ◽  
...  
Keyword(s):  
Eye Tracking ◽  
High Performance ◽  
Low Cost ◽  
Image Features ◽  
Gaze Estimation ◽  
Low Resolution ◽  
Knowledge Based ◽  
Fully Connected ◽  
The Impact ◽  
Selection Of

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7∘, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.

scholarly journals PICUS: A Pocket-Sized System for Simple and Fast Non-Destructive Evaluation of the Detachments in Ancient Artifacts

2021 ◽  
Vol 11 (8) ◽  
pp. 3382
Author(s):  
Giosuè Caliano ◽  
Francesca Mariani ◽  
Paola Calicchia
Keyword(s):  
Cross Correlation ◽  
Light Emitting Diode ◽  
Low Cost ◽  
Force Sensor ◽  
Sensor Data ◽  
Ir Camera ◽  
Light Emitting ◽  
Experimental Apparatus ◽  
The Impact ◽  
Non Destructive

An innovative, robust method has been developed, based on the use of a simple, compact, expressly designed device, named PICUS (the ‘woodpecker’ in ancient Latin), and inspired by the auscultation method carried out by the experts in the field of conservation of cultural heritage. This method entails gently knocking the surface, controlling and measuring the impact time of the stroke’s force, recording the generated sound, comparing the acquired sound with a reference sound by calculating the cross-correlation function, and its maximum, as a measure of the detachment. In a nutshell, it performs an analysis similar to that carried out by a professional who performs a routine examination on the detachments by hand. The experimental apparatus consists of a probe made of an electromechanical percussion element that gently taps the surface producing a sound, a force sensor purposely developed to measure the impact force, and a microphone, all connected with an Arduino-like low cost board, to record and elaborate the sounds and the force sensor signal. The probe XY position on the scene is recognized using an infra-red (IR) system with a low-cost IR camera and an IR light-emitting diode (IR-LED) positioned on the probe. The “tapper” and the microphone replace the hand and the ear of a conservator carrying out a detachment investigation, while the comparison with a reference is the typical mind process of a professional restorer. The result is the fusion of the microphone data and the force sensor data.

scholarly journals Passive Strategies to Improve the Comfort Conditions in a Geodesic Dome

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 663
Author(s):  
Frank Florez ◽  
Pedro Fernández de Córdoba ◽  
John Taborda ◽  
Juan Carlos Castro-Palacio ◽  
José Luis Higón-Calvet ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Energy Consumption ◽  
Low Cost ◽  
Internal Temperature ◽  
Lumped Parameter ◽  
Principal Factors ◽  
Passive Strategies ◽  
The Impact ◽  
The Mathematical Model ◽  
Geodesic Dome

Non-conventional thermal zones are low-cost and ecology friendly alternatives to the housing needs of populations in various situations, such as surviving natural disasters or addressing homelessness. However, it is necessary to guarantee thermal comfort for occupants, while aiming to minimize energy consumption and wastage in refrigeration systems. To reduce the cooling requirements in non-conventional thermal zones it is necessary to model the structure and analyze the principal factors contributing to internal temperature. In this paper, a geodesic dome is modellingusing the lumped parameter technique. This structure is composed of a wooden skeleton and wooden floor, with a canvas surface as its exterior. The mathematical model was tuned using experimental data, and its parameters were classified using Monte Carlo sensitivity analysis. The mathematical model was used to evaluate the impact on internal temperature and occupants’ comfort when two strategies are considered. The results obtained indicatee internal temperature reductions down to a range of 7% to 11%; this result is reflected directly in the energy used to refrigerate the thermal zone, contributing to the objective of providing houses with lower energy consumption.

scholarly journals Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring

2021 ◽  
Vol 13 (8) ◽  
pp. 219
Author(s):  
Francesco Barchi ◽  
Luca Zanatta ◽  
Emanuele Parisi ◽  
Alessio Burrello ◽  
Davide Brunelli ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Damage Detection ◽  
Execution Time ◽  
Sensor Node ◽  
Data Transfer ◽  
Low Cost ◽  
Access Time ◽  
Virtual Sensors ◽  
The Impact ◽  
Spi Interface

In this work, we present an innovative approach for damage detection of infrastructures on-edge devices, exploiting a brain-inspired algorithm. The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their theoretical energy efficiency and compactness, to recognise damage conditions by processing data from low-cost accelerometers (MEMS) directly on the sensor node. We focus on designing an efficient coding of MEMS data to optimise SNN execution on a low-power microcontroller. We characterised and profiled LSNN performance and energy consumption on a hardware prototype sensor node equipped with an STM32 embedded microcontroller and a digital MEMS accelerometer. We used a hardware-in-the-loop environment with virtual sensors generating data on an SPI interface connected to the physical microcontroller to evaluate the system with a data stream from a real viaduct. We exploited this environment also to study the impact of different on-sensor encoding techniques, mimicking a bio-inspired sensor able to generate events instead of accelerations. Obtained results show that the proposed optimised embedded LSNN (eLSNN), when using a spike-based input encoding technique, achieves 54% lower execution time with respect to a naive LSNN algorithm implementation present in the state-of-the-art. The optimised eLSNN requires around 47 kCycles, which is comparable with the data transfer cost from the SPI interface. However, the spike-based encoding technique requires considerably larger input vectors to get the same classification accuracy, resulting in a longer pre-processing and sensor access time. Overall the event-based encoding techniques leads to a longer execution time (1.49×) but similar energy consumption. Moving this coding on the sensor can remove this limitation leading to an overall more energy-efficient monitoring system.

Computer simulation study about the dependence of amorphous silicon photonic waveguides efficiency on the material quality

2020 ◽  
Vol 90 (3) ◽  
pp. 30502
Author(s):  
Alessandro Fantoni ◽  
João Costa ◽  
Paulo Lourenço ◽  
Manuela Vieira
Keyword(s):  
Amorphous Silicon ◽  
High Throughput Screening ◽  
Simulation Analysis ◽  
Low Cost ◽  
Deposition Process ◽  
Large Area ◽  
Sidewall Roughness ◽  
Sensing Applications ◽  
Fabrication Defects ◽  
The Impact

Amorphous silicon PECVD photonic integrated devices are promising candidates for low cost sensing applications. This manuscript reports a simulation analysis about the impact on the overall efficiency caused by the lithography imperfections in the deposition process. The tolerance to the fabrication defects of a photonic sensor based on surface plasmonic resonance is analysed. The simulations are performed with FDTD and BPM algorithms. The device is a plasmonic interferometer composed by an a-Si:H waveguide covered by a thin gold layer. The sensing analysis is performed by equally splitting the input light into two arms, allowing the sensor to be calibrated by its reference arm. Two different 1 × 2 power splitter configurations are presented: a directional coupler and a multimode interference splitter. The waveguide sidewall roughness is considered as the major negative effect caused by deposition imperfections. The simulation results show that plasmonic effects can be excited in the interferometric waveguide structure, allowing a sensing device with enough sensitivity to support the functioning of a bio sensor for high throughput screening. In addition, the good tolerance to the waveguide wall roughness, points out the PECVD deposition technique as reliable method for the overall sensor system to be produced in a low-cost system. The large area deposition of photonics structures, allowed by the PECVD method, can be explored to design a multiplexed system for analysis of multiple biomarkers to further increase the tolerance to fabrication defects.

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