Visible Light Sensor Research Articles

Backscattered Visible Light Sensing of Retroreflective Foils Utilizing Random Forest Based Classification for Speed and Movement Direction Determination and Identification of an Indoor Moving Object

Making the Internet of Things “green” has become a major research focus in recent years. The anticipated massive increase in the numbers of sensor and communication devices makes this endeavor even more important, resulting in various solution approaches ranging from energy harvesting to energy efficient routing schemes. In this work, we propose a system that can perform some of the main tasks of the Internet of Things, namely identification and sensing of an indoor moving object, by the means of visible light sensing in combination with off-the-shelf retroreflective foils, without the necessity to place any actively powered components on the object itself. By utilizing the supervised machine learning approach of random forest, we show that these two tasks can be fulfilled with up to 99.96% accuracy. Based on our previous findings in this regard, we propose some advancements and improvements of the overall system, yielding better results in parallel with an increased complexity of the system. Furthermore, we expand the number of performable tasks toward additional movement direction determination. The achieved results demonstrate the applicability of visible light sensing and its potentials for a “green” Internet of Things.

Passive indoor visible light-based fall detection using neural networks

In this paper, a passive visible light sensing (VLS) fall detection system based on luminaires is proposed that uses neural networks to learn the state (i.e., upright or prone) of a target (e.g., a person). The proposed method measures the channel impulse response (CIR) between different source-receiver pairs in a passive scenario, where the user does not hold a device or sensor. The CIR measurements are collected in a realistically modeled room and neural networks are employed to learn the relationship between the CIR measurements and the states of the target at randomly selected positions in the room. The performance evaluation of the system shows that an accuracy of more than 97% is attainable by utilizing a large number of data samples and high brightness factor of the luminaires. The robustness of the proposed method is validated by using a tilted state which is labeled with same class as the upright state, however, the tilted state is not used to train the network. One of the key applications of fall detection is in healthcare domain for patient monitoring. The correct prediction of the prone state is particularly critical in such scenarios since emergency situations may arise from a fall.

Noticeably enhanced opto-electrical and photodetection performance of spray pyrolysis grown Mn:CdS nanostructured thin films for visible-light sensor applications

Mn:CdS nanostructured thin films with improved optoelectronic performance were grown on glass substrates via spray pyrolysis method with different Mn doping concentrations. The structural properties of the films were analyzed using X-ray diffraction (XRD) patterns which revealed that the grown films exhibit hexagonal wurtzite structure with a growth oriented along (002) plane. A systematic effect of Mn doping was observed on the structural and optical properties of the films. The grown films were found to be optically active and show high absorbance in the visible region. The optical band gap values of the films obtained from the absorbance spectra are in the range of 2.33-2.42 eV. A composition dependent analysis of Raman spectra of the thin films shows that the crystalline quality of as grown films is high. The near band, edge (NBE) and the defect related emission were recorded using photoluminescence spectroscopy with different excitation sources. Photodetector devices fabricated based on Mn:CdS thin films and their performance analysis show improved visible light photodetection with the incorporation of Mn in the CdS system. The film doped with 3.0 wt.% Mn concentrations demonstrates high photoresponse parameters such as photosensitivity (∼ 402), responsivity (8.37 A/W), detectivity (1.45 × 1012 Jones) and external quantum efficiency (EQE∼1954%) with 0.3 s and 0.6 s rise and decay times, respectively, in comparison with 1.0 and 5.0 wt.% Mn doped films. This study demonstrates that the 3.0 wt.% Mn doping concentration is the optimal for CdS based visible light photo-detector applications via cost effective method.

Mass Production of 2D Manifolds of Graphene Oxide by Shear Flow

AbstractSelf‐organizing 2D sheets into a 3D structure with a well‐defined arrangement and tuned bandgap has garnered significant interest. However, there exist challenges such as scalable and feasible synthetic methods and preservation of 2D properties while preventing re‐stacking. Herein, a facile method for the mass production of 2D manifolds of graphene oxide (GO), which are 3D microtubes, while maintaining their 2D properties by applying shear to roll up GO sheets, is reported. The GO mesotubes are formed by shear flow in aluminum–glass parallel plates. Redox reaction plays a vital role during the formation of GO mesotubes by the vorticity alignment during slow shear flow. A high yield of 94% is achieved at an initial pH of 2.2. The maximum d‐spacing of the GO sheet in the tube wall is 21 nm, indicating no re‐stacking of the graphitic structure. It is demonstrated that the GO mesotubes behaves like a soft gel, rendering them a candidate material for soft robotics, e‐skins, and a visible light sensor owing to their reduced optical bandgap.

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors.

Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in spatial resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r² = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using unmanned aerial vehicles, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.

2D XBiSe3(X = Ga, In, Tl) monolayers with high carrier mobility and enhanced visible-light absorption

The geometrical configurations of the XBiSe3 (X = Ga, In, Tl) monolayers are identified by employing the first-principles density functional theory calculations, and the stabilities are confirmed by phonon dispersion, formation energy, and ab initio molecular dynamics simulation, respectively. The bandgap and band edges, the density of states, the optical absorption, mobility, and effect of strain engineering are evaluated to understand the photoelectronic properties of the monolayers. The results show that the XBiSe3 monolayers have the indirect bandgaps of 1.14–1.69 (1.20–1.84) eV by HSE06(GW), leading to the enhanced optical absorption from the visible to near-ultraviolet region. The large mobility of the electron and hole are also observed, which is helpful for the separation and transfer of the photogenerated carrier pair. The band edges and bandgaps, as well as the optical absorptions, can effectively be tuned by strain engineering. It should be noted that the band edges of the InBiSe3 monolayer could satisfy the condition of redox potential for the hydrogen evolution reaction under the compressive strain heavier than −3%, implicating this monolayer can also be used for photocatalytic water splitting to produce hydrogen. Therefore, these monolayers have potential applications in photocatalytic materials or photoelectronic devices such as energy harvesters and visible-light sensors.

MoOx-Si heterojunction with wide-band-gap MoOx contact layer in the application of low-intensity visible-light sensing

The MoOx-Si heterojunction was demonstrated with MoOx as hole contact layer for visible-light sensor application. The α-MoO3 polycrystalline layer was fabricated with high band gap (~2.91 eV) and high transmittance (>75%). The microscale MoOx-Si heterojunction sensor displays great photocurrent under white-light illumination (~10−11 A at extremely low luminance of 40 lx). The rejection ratio is 103 at 1240 lx. Additionally, the MoOx-Si heterojunction sensor shows great repeatability and stability at remarkable low operation voltage (−0.5 V–0 V). The device displays fast recovery with short response time and decay time (both < 0.036 s). The performance indicates the MoOx-Si heterojunction could be a promising device for low-intensity visible-light sensing application.

Identification and Speed Estimation of a Moving Object in an Indoor Application Based on Visible Light Sensing of Retroreflective Foils.

Identification and sensing are two of the main tasks a wireless sensor node has to perform in an Internet of Things (IoT) environment. Placing active powered nodes on objects is the most usual approach for the fulfillment of these functions. With the expected massive increase of connected things, there are several issues on the horizon that hamper the further deployment of this approach in an energy efficient, sustainable way, like the usage of environmentally hazardous batteries or accumulators, as well as the required electrical energy for their operation. In this work, we propose a novel approach for performing the tasks of identification and sensing, applying visible light sensing (VLS) based on light emitting diode (LED) illumination and utilizing retroreflective foils mounted on a moving object. This low cost hardware is combined with a self-developed, low complex software algorithm with minimal training effort. Our results show that successful identification and sensing of the speed of a moving object can be achieved with a correct estimation rate of 99.92%. The used foils are commercially available and pose no threat to the environment and there is no need for active sensors on the moving object and no requirement of wireless radio frequency communication. All of this is achievable whilst undisturbed illumination is still provided.

The structural, electronic, elastic, dielectric, dynamical, thermal and optical properties of Janus ZrOS monolayer: A first-principles investigation

The geometrical and electronic structures, elastic constants, dielectric permittivities, phonon spectra, thermal properties and optical responses of Janus ZrOS monolayer have been investigated by first-principles calculations. The lattice constant a, Zr–O bond length dZr-O, Zr–S bond length dZr-S, band gap Eg, in-plane stiffness C and Poisson's ratio υ of the Janus ZrOS monolayer are 3.44 Å, 2.15 Å, 2.55 Å, 1.88 eV, 226.35 N/m and 0.26, respectively. A p-d hybridized ionic bond exists in Janus ZrOS monolayer, instead of sp2 hybridized covalent bond in graphene. The larger values of Born effective charges Z11∗(Zr)=Z22∗(Zr)=6.76, Z11∗(O)=Z22∗(O)=-3.58 and Z11∗(S)=Z22∗(S)=-3.18 in-plane than the nominal chemical valences of +4 for Zr and −2 for O and S, reflecting a strong dynamic charge transfer from Zr atom to O and S atoms. After taking into account the Born effective charge and dielectric constant, the three acoustic-active modes (one A1 and two Eu) are degenerated (zero frequency) at Γ point, while a LO-TO splitting is observed for either Raman-active mode Eg or IR-active mode Eu at Γ point. The large dielectric permittivity, mechanical stability and dynamical stability make Janus ZrOS monolayer a promising dielectric material in modern semiconductor industry. The high and sharp absorption peaks in visible and UV light region propose Janus ZrOS monolayer a visible and UV light sensor application.

Machine Learning Assisted Visible Light Sensing of the Rotation of a Robotic Arm

With the rise of LED (light-emitting diode)-based luminaires, artificial lighting has become a technology platform, which, besides providing illumination, also provides communication and positioning functionalities. Apart from this, most recently Visible Light Sensing (VLS), in which lighting is used for sensing purposes, emerged as another embodiment of functionalities lighting could take over in the future. Here we show that machine learning assisted VLS has promising potentials to become a meaningful enabler for the Industrial Internet of Things. We show that the rotation of a robotic arm can be accurately monitored by VLS simply by equipping the robotic arm with sequences of colored retroreflective foils. Moreover, we show that the sensing task is compatible with a modulation of the light. This paths the way that sensing and communication tasks can be performed in parallel with one and the same low-complexity infrastructure, that apart from this also could take over the task of the obligatory room lighting. We demonstrate the capability of the approach even if the overall illumination conditions change. Therewith, VLS accentuates as an alternative option for industrial robot monitoring in combination with optical wireless communication.

Seeing Pedestrian in the Dark via Multi-Task Feature Fusing-Sharing Learning for Imaging Sensors.

Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method—the parameter sharing mechanism—in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.

Fast eyes detection in thermal images

In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.

Non-Contact Vital Signs Monitoring Through Visible Light Sensing

This paper presents a non-contact vital signs (respiration and heartbeat) monitoring system that utilizes visible light sensing (VLS) technology. We have for the first time demonstrated the ability to wirelessly (non-contact) sense vital signs using only reflected incoherent light signals from a human subject. The VLS-based system is implemented by using simple visible light source, photodetector and data acquisition/processing unit, and is used with the developed signal processing algorithms to turn slight variations in reflected light power into accurate measurements of respiration and heart rate. To assess the accuracy of our method, the results were compared with reliable measurements using a contact-based monitoring device (ground truth). More than 94% of accuracy was observed in test results including both breathing and heartbeat rates in different scenarios as compared to the state-of-the-art baseline methods such as contact-based vitals monitoring devices. These competitive results have demonstrated that VLS-based vitals monitoring innovation is indeed a viable, powerful, attractive, low-cost and safe method. This study represents a substantive departure from the traditional ways of doing non-contact vitals monitoring methods (e.g., radio-frequency-based radars and imaging-based cameras) and is poised to make big contributions to this area. Since vital signs monitoring is a ubiquitous element of medicine, this work would also impact the entire health care community, from patients in their homes, to doctor’s offices, to large medical institutions and industries. This technology has potential to address numerous conditions and situations in which vital signs are critical indicators such as sleep apnea and human-computer-interaction applications.

Passive Visible Light Detection Of Humans.

This paper experimentally investigates passive human visible light sensing (VLS). A passive VLS system is tested consisting of one light emitting diode (LED) and one photodiode-based receiver, both ceiling-mounted. There is no line of sight between the LED and the receiver, so only reflected light can be considered. The influence of a human is investigated based on the received signal strength (RSS) values of the reflections of ambient light at the photodiode. Depending on the situation, this influence can reach up to . The experimental results show the influence of three various clothing colors, four different walking directions and four different layouts. Based on the obtained results, a human pass-by detection system is proposed and tested. The system achieves a detection rate of 100% in a controlled environment for 21 experiments. For a realistic corridor experiment, the system keeps its detection rate of 100% for 19 experiments.

Improved localisation and discrimination of heat emitting household objects with the artificial vision therapy system by integration with thermal sensor

AimTo demonstrate the potential clinically meaningful benefits of a thermal camera integrated with the Argus II, an artificial vision therapy system, for assisting Argus II users in localising and discriminating...

Photon-counting, energy-resolving and super-resolution phase contrast X-ray imaging using an integrating detector.

This work demonstrates the use of a scientific-CMOS (sCMOS) energy-integrating detector as a photon-counting detector, thereby eliminating dark current and read-out noise issues, that simultaneously provides both energy resolution and sub-pixel spatial resolution for X-ray imaging. These capabilities are obtained by analyzing visible light photon clouds that result when X-ray photons produce fluorescence from a scintillator in front of the visible light sensor. Using low-fluence monochromatic X-ray projections to avoid overlapping photon clouds, the centroid of individual X-ray photon interactions was identified. This enabled a tripling of the spatial resolution of the detector to 6.71 ± 0.04 µm. By calculating the total charge deposited by this interaction, an energy resolution of 61.2 ± 0.1% at 17 keV was obtained. When combined with propagation-based phase contrast imaging and phase retrieval, a signal-to-noise ratio of up to 15 ± 3 was achieved for an X-ray fluence of less than 3 photons/mm2.

Passive Visible-to-Telecom Converter Using Tunable Perovskites and Silicon Photonics

Visible-to-telecom converter, which converts the information from visible light to infrared telecommunication band, has huge potential in applications such as remote monitoring of visible light communication (VLC) links or sunlight illuminance in uninhabited regions to data centers. However, the existing converters based on parametric process suffer from high energy consumption, and a requirement of extra pumping supply. Herein we propose a passive visible-to-telecom converter by combining a perovskite solar cell with a silicon photonic chip, and further demonstrate its typical application scenario in monitoring VLC links. We succeed in converting the VLC signal with 200 kHz modulated rate into infrared light and transmitting it over 5-km-fiber without distortion. Thanks to the tunable bandgap properties of perovskite materials in perovskite solar cell, we double the capacity of a VLC link with a wavelength division multiplexing strategy. This visible-to-telecom converter via hybrid of perovskite and silicon photonics reveals potentials as a large range distributed visible-light sensor network converging on an optical interconnect data center.

Deep Networks With Detail Enhancement for Infrared Image Super-Resolution

Due to the limitation of hardware, infrared (IR) images have low-resolution (LR) and poor visual quality. Image super-resolution (SR) is a good solution to this problem. In this paper, we present a new convolution network (CNN) to improve the spatial resolution of infrared (IR) images. Our network is able to restore fine details by decomposing the input image into low-frequency and high-frequency domains. In low-frequency domains, we reconstruct image structure by deep networks. In high frequency domains, we reconstruct IR image details. Furthermore, we proposed another network to remove artifacts. Additionally, we propose a new loss function using visible (VIS) images to enhance the details of IR images. In training phase, we use VIS images to guide IR image restoration and in testing phase we get SR IR images with LR IR images input only. We optimize our deep network with a targeted function which penalizes images at different semantic levels using the corresponding terms. Besides, we build a dataset where paired LR-VIS images on the same scene are captured by a camera with both infrared and visible light sensors which both sensors have the same optical axis. Extensive experiments demonstrate that the proposed algorithm achieves superior performance and visual improvements against the state-of-the-arts.

A high performance surface acoustic wave visible light sensor using novel materials: Bi2S3 nanobelts.

Low dimensional Bi2S3 materials are excellent for use in photodetectors with excellent stability and fast response time. In this work, we developed a visible light sensor with good performance based on surface acoustic wave (SAW) devices using Bi2S3 nanobelts as the sensing materials. The SAW delay-line sensor was fabricated on ST-cut quartz with a designed wavelength of 15.8 microns using conventional photolithography techniques. The measured center frequency was 200.02 MHz. The Bi2S3 nanobelts prepared by a facile hydrothermal process were deposited onto SAW sensors by spin-coating. Under irradiation of 625 nm visible light with a power intensity of 170 μW cm−2, the sensor showed a fast and large response with a frequency upshift of 7 kHz within 1 s. The upshift of the frequency of the SAW device is mainly attributed to the mass loading effect caused by the desorption of oxygen from the Bi2S3 nanobelts under visible light radiation.

Highly Transparent ITO/HfO2/ITO Device for Visible-Light Sensing

Transparent visible-light sensors are of critical importance in invisible optoelectronic circuits as the interface between the optical and electrical domains. However, devices designed based on photocarrier generation for light sensing limit the transparency of the device in the visible region, as the optically active layer also passively absorbs light continuously. In this work, we present an all-transparent ITO/HfO 2 /ITO device with the wide bandgap HfO 2 functioning as the resistive switching and the light sensing element. Electrically switching the resistive state of the device to a low-resistance soft-breakdown state is demonstrated to cause the wide-bandgap visible-light blind HfO 2 to become photoresponsive. This allows us to optically functionalize the device for an optical response by electrical breakdown in the oxide and can be triggered on-demand. Apart from the on-demand optical response in the device, the inherent resistive switching properties of the HfO 2 device allow for integration of memory and optical sensing capabilities, presenting a novel phenomenon for optical sensing in invisible optoelectronics.