A TRIZ Approach for Designing a Smart Lighting and Control System for Classrooms Based on Counter Application with Dual PIR Sensors.

Electrical energy is often wasted through human negligence when people do not switch off electrical appliances such as lighting after leaving a place. Such a scenario often happens in a classroom when the last person leaves the class and forgets to switch off the electrical appliances. Such wastage may not be able to be afforded by schools that are limited financially. Therefore, this research proposed a simple and cost-effective system that can analyze whether there is or is not a human presence in the classroom by applying a counter to count the total number of people entering and leaving the classroom based on the sensing signals of a set of dual PIR sensors only and then correlating this to automatically turn on or off the electrical appliances mentioned. The total number of people identified in the classroom is also displayed on an LCD screen. A TRIZ approach is used to support the ideation of the system. The system can switch on several electrical output loads simultaneously when the presence of people is detected and switch them off when there are no people in the classroom. The proposed system can be expanded to be used in homes, offices, and buildings to prevent the high cost of electricity consumption caused by the negligence of people. This enables smarter control of electricity consumption.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Obstacle Detection in Infrared Navigation for Blind People and Mobile Robots.

The paper is a continuation of the authors' work intended for infrared navigation for blind people and mobile robots. This concerns the detection of obstacles in the person's or mobile robot's trajectory, in particular, the detection of corners. The temperature distribution of a building's internal wall near a corner has been investigated. Due to geometry, more heat will be transferred by conduction so that inside the building, the temperature on the wall will be decreasing towards a corner. The problem will be investigated theoretically and numerically, and the results are confirmed by experimental measurements. The purpose of this research is to help blind people by equipping them with a small infrared camera that warns them when they are approaching a corner inside a building. The same aim is addressed to mobile robots.

Positioning System of Infrared Sensors Based on ZnO Thin Film.

Infrared sensors incorporating suspended zinc oxide (ZnO) pyroelectric films and thermally insulated silicon substrates are fabricated using conventional MEMS-based thin-film deposition, photolithography, and etching techniques. The responsivity of the pyroelectric film is improved via annealing at 500 °C for 4 h. The voltage response of the fabricated sensors is evaluated experimentally for a substrate thickness of 1 µm over a sensing range of 30 cm. The results show that the voltage signal varies as an inverse exponential function of the distance. A positioning system based on three infrared sensors is implemented in LabVIEW. It is shown that the position estimates obtained using the proposed system are in excellent agreement with the actual locations. In general, the results presented in this study provide a useful source of reference for the further development of MEMS-based pyroelectric infrared sensors.

Thermal Imaging Detection System: A Case Study for Indoor Environments.

Currently, there is an increasing need for reliable mechanisms for automatically detecting and localizing people-from performing a people-flow analysis in museums and controlling smart homes to guarding hazardous areas like railway platforms. A method for detecting people using FLIR Lepton 3.5 thermal cameras and Raspberry Pi 3B+ computers was developed. The method creates a control and capture library for the Lepton 3.5 and a new person-detection technique that uses the state-of-the-art YOLO (You Only Look Once) real-time object detector based on deep neural networks. A thermal unit with an automated configuration using Ansible encapsulated in a custom 3D-printed enclosure was used. The unit has applications in simple thermal detection based on the modeling of complex scenes with polygonal boundaries and multiple thermal camera monitoring. An easily deployable person-detection and -localization system based on thermal imaging that supports multiple cameras and can serve as an input for other systems that take actions by knowing the positions of people in monitored environments was created. The thermal detection system was tested on a people-flow analysis performed in the Czech National Museum in Prague. The contribution of the presented method is the development of a small and simple detection system that is easily mountable with wide indoor as well as outdoor applications. The novelty of the system is in the utilization of the YOLO model for thermal data.

Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data.

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.

Fast and Uncooled Semiconducting Ca-Doped Y-Ba-Cu-O Thin Film-Based Thermal Sensors for Infrared.

YBa2Cu3O6+x (YBCO) cuprates are semiconductive when oxygen depleted (x < 0.5). They can be used for uncooled thermal detection in the near-infrared: (i) low temperature deposition on silicon substrates, leading to an amorphous phase (a-YBCO); (ii) pyroelectric properties exploited in thermal detectors offering both low noise and fast response above 1 MHz. However, a-YBCO films exhibit a small direct current (DC) electrical conductivity, with strong non-linearity of current-voltage plots. Calcium doping is well known for improving the transport properties of oxygen-rich YBCO films (x > 0.7). In this paper, we consider the performances of pyroelectric detectors made from calcium-doped (10 at. %) and undoped a-YBCO films. First, the surface microstructure, composition, and DC electrical properties of a-Y0.9Ca0.1Ba2Cu3O6+x films were investigated; then devices were tested at 850 nm wavelength and results were analyzed with an analytical model. A lower DC conductivity was measured for the calcium-doped material, which exhibited a slightly rougher surface, with copper-rich precipitates. The calcium-doped device exhibited a higher specific detectivity (D*=7.5×107 cm·Hz/W at 100 kHz) than the undoped device. Moreover, a shorter thermal time constant (<8 ns) was inferred as compared to the undoped device and commercially available pyroelectric sensors, thus paving the way to significant improvements for fast infrared imaging applications.

Inorganic-Organic Hybrid Phototransistor Array with Enhanced Photogating Effect for Dynamic Near-Infrared Light Sensing and Image Preprocessing.

Narrow-band-gap organic semiconductors have emerged as appealing near-infrared (NIR) sensing materials by virtue of their unique optoelectronic properties. However, their limited carrier mobility impedes the implementation of large-area, dynamic NIR sensor arrays. In this work, high-performance inorganic-organic hybrid phototransistor arrays are achieved for NIR sensing, by taking advantage of the high electron mobility of In2O3 and the strong NIR absorption of a BTPV-4F:PTB7-Th bulk heterojunction (BHJ) with an enhanced photogating effect. As a result, the hybrid phototransistors reach a high responsivity of 1393.0 A W-1, a high specific detectivity of 4.8 × 1012 jones, and a fast response of 0.72 ms to NIR light (900 nm). Meanwhile, an integrated 16 × 16 phototransistor array with a one-transistor-one-phototransistor (1T1PT) architecture is achieved. On the basis of the enhanced photogating effect, the phototransistor array can not only achieve real-time, dynamic NIR light mapping but also implement image preprocessing, which is promising for advanced NIR image sensors.

Mathematical Model and Synthetic Data Generation for Infra-Red Sensors.

A key challenge in further improving infrared (IR) sensor capabilities is the development of efficient data pre-processing algorithms. This paper addresses this challenge by providing a mathematical model and synthetic data generation framework for an uncooled IR sensor. The developed model is capable of generating synthetic data for the design of data pre-processing algorithms of uncooled IR sensors. The mathematical model accounts for the physical characteristics of the focal plane array, bolometer readout, optics and the environment. The framework permits the sensor simulation with a range of sensor configurations, pixel defectiveness, non-uniformity and noise parameters.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence.

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

Infrared Monitoring of Oxygenation Process Generated by Robotic Verticalization in Bedridden People.

Bedridden people, especially at home, suffer from diverse pathologies beyond the main one that brings them to a specific position. Long-term cares are suitable at home to avoid congestions within hospital facilities. There are different technologies available to improve such people's conditions in their daily life. The standing posture is the key solution to enhance people's wellness amid the psychological burden due to the almost impossibility to be completely healed. The paper proposes the use of a polyfunctional and robotic bed capable of displaying many positions namely vertical, tilting, anti-trendelenburg with necessary graduation. A three-year monitoring of a patient, using a polyfunctional and robotic bed, suffering from amyotrophic lateral sclerosis (ALS), has been investigated. Different physiological parameters have been measured and, particularly, the variation of temperature has been measured in presence of body position connected to the robotic bed rotation that provokes biomechanical effort. It is demonstrated that certain body positions correspond to major and minor physical effort, hence major and minor oxygenation. An infrared camera has been used. As a positive result, the variation of posture has been delaying the increase of the pathological signs, because of better conditions.

Light Absorption in Nanowire Photonic Crystal Slabs and the Physics of Exceptional Points: The Shape Shifter Modes.

Semiconductor nanowire arrays have been demonstrated as promising candidates for nanoscale optoelectronics applications due to their high detectivity as well as tunable photoresponse and bandgap over a wide spectral range. In the infrared (IR), where these attributes are more difficult to obtain, nanowires will play a major role in developing practical devices for detection, imaging and energy harvesting. Due to their geometry and periodic nature, vertical nanowire and nanopillar devices naturally lend themselves to waveguide and photonic crystal mode engineering leading to multifunctional materials and devices. In this paper, we computationally develop theoretical basis to enable better understanding of the fundamental electromagnetics, modes and couplings that govern these structures. Tuning the photonic response of a nanowire array is contingent on manipulating electromagnetic power flow through the lossy nanowires, which requires an intimate knowledge of the photonic crystal modes responsible for the power flow. Prior published work on establishing the fundamental physical modes involved has been based either on the modes of individual nanowires or numerically computed modes of 2D photonic crystals. We show that a unified description of the array key electromagnetic modes and their behavior is obtainable by taking into account modal interactions that are governed by the physics of exceptional points. Such models that describe the underlying physics of the photoresponse of nanowire arrays will facilitate the design and optimization of ensembles with requisite performance. Since nanowire arrays represent photonic crystal slabs, the essence of our results is applicable to arbitrary lossy photonic crystals in any frequency range.

Non-Layered Gold-Silicon and All-Silicon Frequency-Selective Metasurfaces for Potential Mid-Infrared Sensing Applications.

We report simulations on the spectral behavior of non-layered gold-silicon and all-silicon frequency-selective metasurfaces in an asymmetric element configuration in the mid-infrared spectral window of 5-5.8 µm. The non-layered layout is experimentally feasible due to recent technological advances such as nano-imprint and nano-stencil lithography, and the spectral window was chosen due to the multitude of applications in sensing and imaging. The architecture exhibits significant resonance in the window of interest as well as extended tunability by means of variation of cell element sizes and relative coordinates. The results indicate that the proposed metasurface architecture is a viable candidate for mid-infrared absorbers, sensors and imaging systems.

The Role of Noise in Specific Detectivity of InAs/GaSb Superlattice MWIR Bariodes.

In this paper, the results of the electrical, noise, and optical characterization of p-i-n and p-B-i-n diodes with AlSb and 4 ML AlSb/8 ML GaSb superlattice barriers in High-Operating Temperature conditions, are presented. Experimental and theoretical noise parameters were compared. Both dark current and noise analysis showed that the p-Bp_bulk-i-n bariode had the best performance. P-i-n photodiodes had the highest experimental value of specific detectivity (D*) of 6.16 × 109 Jones at 210 K and zero bias. At about -1 V reverse bias, the bariode with AlSb/GaSb electron barrier caught up to it and both devices achieved D* = (1-1.1) × 108 Jones. Further optimization of the superlattice-based electron barrier should result in the improvement of bariode performance at a smaller bias, at which better noise performance is more pronounced. It was shown that neglecting the low-frequency noise component can lead to a significant overestimation of detectivity. The simple method of incorporation of low-frequency noise contribution in the detectivity calculation, without time-consuming measurements, has been proposed.

Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis.

Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot.

Characterization of metal oxide gas sensors via optical techniques.

Metal oxide (MOX) sensors are increasingly gaining attention in analytical applications. Their fundamental operation principle is based on conversion reactions of selected molecular species at their semiconducting surface. However, the exact turnover of analyte gas in relation to the concentration has not been investigated in detail to date. In the present study, two optical sensing techniques-luminescence quenching for molecular oxygen and infrared spectroscopy for carbon dioxide and methane-have been coupled for characterizing the behavior of an example semiconducting MOX methane gas sensor integrated into a recently developed low-volume gas cell. Thereby, oxygen consumption during MOX operation as well as the generation of carbon dioxide from the methane conversion reaction could be quantitatively monitored. The latter was analyzed via a direct mid-infrared gas sensor system based on substrate-integrated hollow waveguide (iHWG) technology combined with a portable Fourier transform infrared spectrometer, which has been able to not only detect the amount of generated carbon dioxide but also the consumption of methane during MOX operation. Hence, a method based entirely on direct optical detection schemes was developed for characterizing the actual signal generating processes-here for the detection of methane-via MOX sensing devices via near real-time online analysis. Graphical Abstract.

Hands-Free User Interface for VR Headsets Based on In Situ Facial Gesture Sensing.

The typical configuration of virtual reality (VR) devices consists of a head-mounted display (HMD) and handheld controllers. As such, these units have limited utility in tasks that require hand-free operation, such as in surgical operations or assembly works in cyberspace. We propose a user interface for a VR headset based on a wearer's facial gestures for hands-free interaction, similar to a touch interface. By sensing and recognizing the expressions associated with the in situ intentional movements of a user's facial muscles, we define a set of commands that combine predefined facial gestures with head movements. This is achieved by utilizing six pairs of infrared (IR) photocouplers positioned at the foam interface of an HMD. We demonstrate the usability and report on the user experience as well as the performance of the proposed command set using an experimental VR game without any additional controllers. We obtained more than 99% of recognition accuracy for each facial gesture throughout the three steps of experimental tests. The proposed input interface is a cost-effective and efficient solution that facilitates hands-free user operation of a VR headset using built-in infrared photocouplers positioned in the foam interface. The proposed system recognizes facial gestures and incorporates a hands-free user interface to HMD, which is similar to the touch-screen experience of a smartphone.

A Thermopile Device with Sub-Wavelength Hole Arrays by CMOS-MEMS Technology.

A thermopile device with sub-wavelength hole array (SHA) is numerically and experimentally investigated. The infrared absorbance (IRA) effect of SHAs in active area of the thermopile device is clearly analyzed by the finite-difference time-domain (FDTD) method. The prototypes are manufactured by the 0.35 µm 2P4M complementary metal-oxide-semiconductor micro-electro-mechanical-systems (CMOS-MEMS) process in Taiwan semiconductor manufacturing company (TSMC). The measurement results of those prototypes are similar to their simulation results. Based on the simulation technology, more sub-wavelength hole structural effects for IRA of such thermopile device are discussed. It is found from simulation results that the results of SHAs arranged in a hexagonal shape are significantly better than the results of SHAs arranged in a square and the infrared absorption efficiencies (IAEs) of specific asymmetric rectangle and elliptical hole structure arrays are higher than the relatively symmetric square and circular hole structure arrays. The overall best results are respectively up to 3.532 and 3.573 times higher than that without sub-wavelength structure at the target temperature of 60 °C when the minimum structure line width limit of the process is ignored. Obviously, the IRA can be enhanced when the SHAs are considered in active area of the thermopile device and the structural optimization of the SHAs is absolutely necessary.

Enhanced Infrared Absorbance of the CMOS Compatible Thermopile by the Subwavelength Rectangular-Hole Arrays.

The enhanced infrared absorbance (IRA) of the complementary metal-oxide-semiconductor (CMOS) compatible thermopile with the subwavelength rectangular-hole arrays in active area is investigated. The finite-difference time-domain (FDTD) method considered and analyzed the matrix arrangement (MA) and staggered arrangement (SA) of subwavelength rectangular-hole arrays (SRHA). For the better cases of MA-SRHA and SA-SRHA, the geometric parameters are the same and the infrared absorption efficiency (IAE) of the SA type is better than that of the MA type by about 19.4% at target temperature of 60˚C. Three proposed thermopiles with SA-SRHA are manufactured based on the 0.35 µm 2P4M CMOS-MEMS process. The measurement results are similar to the simulation results. The IAE of the best simulation case of SA-SRHA is up to 3.3 times higher than that without structure at the target temperature of 60˚C. Obviously, the staggered rectangular-hole arrays with more appropriate geometric conditions obtained from FDTD simulation can excellently enhance the IRA of the CMOS compatible thermopile.

Cascade laser sensing concepts for advanced breath diagnostics.

With more than a thousand constituents at trace level concentrations, exhaled breath analysis (EBA) allows for non-invasive point-of-care (POC) disease diagnostics and metabolic status monitoring in or close to real-time. A number of biomarkers in breath may be used to not only identify diseases and disease progression but also to monitor therapeutic interventions. Although the relationship of selected breath components/biomarkers with certain disease pathologies is well established, diagnosing the exhaled breath composition remains an analytical and practical challenge due to the concentration levels of molecules of interest, i.e., low parts-per-billion (ppb) regime and below. Besides the analytical assessment of breath components via conventional methods such as gas chromatography coupled to mass spectrometry and related techniques, the application of cascade laser spectroscopy (CLS) is relatively new and exhibits several advantages when aiming for compact and user-friendly trace gas sensors with high molecular selectivity, the required sensitivity, and potentially reasonable instrumental costs. This trend article highlights the current status and potential of CLS in breath diagnostics with a focus on recent advancements in instrumentation and application along with future prospects and challenges. Graphical abstract Cascade laser technology in the mid-infrared spectral range enables sensitive and molecularly selective exhaled breath analysis with near real-time response, label-free detection, and point-of-care feasibility.