Search Results (16)

Accurate time period estimation (TPE) between sensor signals is essential for vehicle speed measurement in intelligent transportation systems (ITSs). In this context, we focus on time period estimation using signals acquired from a dual pyroelectric infrared (PIR) sensor setup. To estimate the time period between these signals, this paper analyzes and compares two correlation-based methods—conventional cross-correlation (CCF) and Hilbert transform-enhanced cross-correlation (CCFHT). An analytical framework is developed to quantify the bias and variance of each method under practical conditions, including sensor mismatch and noise. The PIR sensor signals are modeled based on their dynamic response characteristics, enabling theoretical analysis supported by simulations and field experiments. Results show that although both methods yield negligible bias under ideal conditions, CCFHT significantly reduces estimation variance in noisy or mismatched scenarios. These findings confirm the advantages of CCFHT for achieving robust and precise vehicle speed estimation using low-cost PIR sensor systems, and provide insights into practical deployment within ITSs. Full article

Uncooled infrared (IR) sensors, including bolometers, thermopiles, and pyroelectrics, have traditionally dominated the market. Nevertheless, a new innovative technology, dubbed the TMOS sensor, has emerged. It is based on CMOS-SOI-MEMS (complementary-metal-oxide-semiconductor silicon-on-insulator micro-electromechanical systems) fabrication. This pioneering technology utilizes a suspended, micro-machined, thermally insulated transistor to directly convert absorbed infrared radiation into an electrical signal. The miniaturization of IR sensors, including the TMOS, is crucial for seamless integration into wearable and mobile technologies. However, this presents a significant challenge: balancing size reduction with sensor sensitivity. Smaller sensor footprints can often lead to decreased signal capture and, consequently, diminished performance. Metamaterial advancements offer a promising solution to this challenge. These engineered materials exhibit unique electromagnetic properties that can potentially boost sensor sensitivity while enabling miniaturization. The strategic integration of metamaterials into sensor design offers a pathway towards compact, high-sensitivity IR systems with diverse applications. This study explores the impact of electro-optical metal-insulator-metal (MIM) metamaterial absorbers on the thermal and electro-optical characteristics of CMOS-SOI-MEMS sensors in the mid-IR region. We target the key thermal properties critical to IR sensor performance: thermal conductance (Gth), thermal capacitance (Cth), and thermal time constant (τth). This study shows how material selection, layer thickness, and metamaterial geometry fill-factor affect the sensor’s thermal performance. An analytical thermal model is employed alongside 3D finite element software for precise numerical simulations. Full article

Increasing concerns about air quality due to fossil fuel combustion, especially nitrogen oxides (NO_x) from marine and diesel engines, necessitate advanced monitoring systems due to the significant health and environmental impacts of nitrogen dioxide (NO₂). In this study, a gas detection system based on the principle of the non-dispersive infrared (NDIR) technique is proposed. Firstly, the pyroelectric detector was developed by employing an ultra-thin LiTaO₃ (LT) layer as the sensitive element, integrated with nanoscale carbon material prepared by wafer-level graphics technology as the infrared absorption layer. Then, the sensor was hermetically sealed using inert gas through energy storage welding technology, exhibiting a high detectivity (D*) value of 4.19 × 10⁸ cm·√Hz/W. Subsequently, a NO₂ gas sensor was engineered based on the NDIR principle employing a Micro Electro Mechanical System (MEMS) infrared (IR) emitter, featuring a light path chamber length of 1.5 m, along with integrated signal processing and software calibration algorithms. This gas sensor was capable of detecting NO₂ concentrations within the range of 0–500 ppm. Initial tests indicated that the gas sensor exhibited a full-scale relative error of less than 0.46%, a limit of 2.8 ppm, a linearity of −1.09%, a repeatability of 0.47% at a concentration of 500 ppm, and a stability of 2% at a concentration of 500 ppm. The developed gas sensor demonstrated significant potential for application in areas such as industrial monitoring and analytical instrumentation. Full article

Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff. Full article

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. Full article

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection. Full article

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. Full article

Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks. Full article

Pyroelectric infrared sensors are often based on lead-containing materials, which are harmful to the environment and subject to governmental restrictions. Ferroelectric Hf_1−xZr_xO₂ thin films offer an environmentally friendly alternative. Additionally, CMOS integration allows for integrated sensor circuits, enabling scalable and cost-effective applications. In this work, we demonstrate the deposition of pyroelectric thin films on area-enhanced structured substrates via thermal atomic layer deposition. Scanning electron microscopy indicates a conformal deposition of the pyroelectric film in the holes with a diameter of 500 nm and a depth of 8 μm. By using TiN electrodes and photolithography, capacitor structures are formed, which are contacted via the electrically conductive substrate. Ferroelectric hysteresis measurements indicate a sizable remanent polarization of up to 331 μC cm⁻², which corresponds to an area increase of up to 15 by the nanostructured substrate. For pyroelectric analysis, a sinusoidal temperature oscillation is applied to the sample. Simultaneously, the pyroelectric current is monitored. By assessing the phase of the measured current profile, the pyroelectric origin of the signal is confirmed. The devices show sizable pyroelectric coefficients of −475 μC m⁻² K⁻¹, which is larger than that of lead zirconate titanate (PZT). Based on the experimental evidence, we propose Hf_1−xZr_xO₂ as a promising material for future pyroelectric applications. Full article

Fundamentally different responses of a LiTaO ${}_3$ thin film detector are observed when it is subjected to short microwave pulses as the pulse intensity is altered over a wide range. We start from weak microwave pulses which lead to only trivial pyroelectric peak response. However, when the microwave pulses become intense, the normally expected pyroelectric signal seems to be suppressed and the sign of the voltage signal can even be completely changed. Analysis indicates that while the traditional pyroelectric model, which is a linear model and works fine for our data in the small regime, it does not work anymore in the large signal regime. Since the small-signal model is the key foundation of electromagnetic-wave sensors based on pyroelectric effects, such as pyroelectric infrared detecters, the observation in this work suggests that one should be cautious when using these devices in intense fields. In addition, the evolution of detector signal with respect to excitation strength suggests that the main polarisation process is changed in the large signal regime. This is of fundamental importance to the understanding on how crystalline solids interact with intense microwaves. Possible causes of the nonlinear behaviour is discussed. Full article

In augmented reality (AR), audio markers can be alternatives to image markers for rendering virtual objects when an AR device camera fails to identify the image marker due to lighting conditions and/or the distance between the marker and device. However, conventional audio markers simply broadcast a rendering queue to anonymous devices, making it difficult to provide specific virtual objects of interest to the user. To overcome this limitation without relying on camera-based sensing, we propose a user-aware audio marker system using low frequency ultrasonic signal processing. The proposed system detects users who stay within the marker using ultrasonic-based object detection, and then it uses ultrasonic communication based on windowed differential phase shift keying modulation in order to send a rendering queue only to those users near the marker. Since the proposed system uses commercial microphones and speakers, conventional telecommunication systems can be employed to deliver the audio markers. The performance of the proposed audio marker system is evaluated in terms of object detection accuracy and communication robustness. First, the object detection accuracy of the proposed system is compared with that of a pyroelectric infrared (PIR) sensor-based system in indoor environments, and it is shown that the proposed system achieves a lower equal error rate than the PIR sensor-based system. Next, the successful transmission rate of the proposed system is measured for various distances and azimuths under noisy conditions, and it is also shown that the proposed audio marker system can successfully operate up to approximately 4 m without any transmission errors, even with 70 dB_SPL ambient noise. Full article

Analysis of the multiple factors affecting human identification ability based on pyroelectric infrared technology is a complex problem. First, we examine various sensed pyroelectric waveforms of the human body thermal infrared signal and reveal a mechanism for affecting human identification. Then, we find that the mechanism is decided by the distance, human target, pyroelectric infrared (PIR) sensor, the body type, human moving velocity, signal modulation mask, and Fresnel lens. The mapping relationship between the sensed waveform and multiple influencing factors is established, and a group of mathematical models are deduced which fuse the macro factors and micro factors. Finally, the experimental results show the macro-factors indirectly affect the recognition ability of human based on the pyroelectric technology. At the same time, the correctness and effectiveness of the mathematical models is also verified, which make it easier to obtain more pyroelectric infrared information about the human body for discriminating human targets. Full article

Conventional wearable sensors are mainly used to detect the physiological and activity information of individuals who wear them, but fail to perceive the information of the surrounding environment. This paper presents a wearable thermal sensing system to detect and perceive the information of surrounding human subjects. The proposed system is developed based on a pyroelectric infrared sensor. Such a sensor system aims to provide surrounding information to blind people and people with weak visual capability to help them adapt to the environment and avoid collision. In order to achieve this goal, a low-cost, low-data-throughput binary sampling and analyzing scheme is proposed. We also developed a conditioning sensing circuit with a low-noise signal amplifier and programmable system on chip (PSoC) to adjust the amplification gain. Three statistical features in information space are extracted to recognize static humans and human scenarios in indoor environments. The results demonstrate that the proposed wearable thermal sensing system and binary statistical analysis method are efficient in static human detection and human scenario perception. Full article

In terahertz (THz) materials science, imaging by scanning prevails when low power THz sources are used. However, the application of array detectors operating with high power THz sources is increasingly reported. We compare the imaging properties of four different array detectors that are able to record THz radiation directly. Two micro-bolometer arrays are designed for infrared imaging in the 8–14 μm wavelength range, but are based on different absorber materials (i) vanadium oxide; (ii) amorphous silicon; (iii) a micro-bolometer array optimized for recording THz radiation based on silicon nitride; and (iv) a pyroelectric array detector for THz beam profile measurements. THz wavelengths of 96.5 μm, 118.8 μm, and 393.6 μm from a powerful far infrared laser were used to assess the technical performance in terms of signal to noise ratio, detector response and detectivity. The usefulness of the detectors for beam profiling and digital holography is assessed. Finally, the potential and limitation for real-time digital holography are discussed. Full article

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector’s false alarms. Full article