Sensors For Target Detection Research Articles

Ultrasensitive and specific photoelectrochemical sensor for hydrogen peroxide detection based on pillar[5]arene-functionalized Au nanoparticles and MWNTs hybrid BiOBr heterojunction.

A photoelectrochemical sensor for target detection of hydrogen peroxide was designed based on a new heterojunction nanocomposite which was sulfhydryl-borate ester-modified A1/B1-type pillar[5]arene (BP5)-functionalized Au NPs and multi-walled carbon nanotubes hybridized with bismuth bromide oxide (Au@BP5/MWNTs-BiOBr). The specific sensor was based on the direct induction of oxidation by hydrogen peroxide of the borate ester group of pillar[5]arene. Additionally, the local surface plasmon resonance (LSPR) of Au NPs enhanced visible light capture, the host-guest complexation of BP5 with H2O2 enhanced photocurrent response, the layer-by-layer stacked nanoflower structure of BiOBr provided large specific surface area with more active sites, and the conductivity of MWNTs enhanced the charge separation efficiency and significantly improves the stability of PEC. Their synthesis effect significantly increased the photocurrent signal and further enhanced the detection result. Under the optimal conditions, the linear concentration range of H2O2 detected by the Au@BP5/MWNTs-BiOBr sensor was from 1 to 60pmol/L. The limit of detection (LOD) and the limit of quantification (LOQ) of the method were 0.333pmol/L and 1pmol/L, respectively, and the sensitivity was 6.471pmol/L. Importantly, the PEC sensor has good stability, reproducibility, and interference resistance and can be used for the detection of hydrogen peroxide in real cells.

Stereo Vision Controlled Variable Rate Sprayer for Specialty Crops: Part I. Controller Development

Highlights Developed a stereo vision spray controller with a single board computer for precision sprayers. Developed algorithms to process stereo vision data, calculate spray volumes, and activate spray nozzles. Developed a graphical user interface to embed algorithms and operate the controller. Controller detected target volume and sprayed target in real time at various travel speeds before target was on spray area. The spray controller consumed 111 to 141 ms to acquire and process data and determine spray output. Abstract. Under laboratory conditions, a variable rate spray controller using stereo vision as a target detection sensor was developed and evaluated with moving spray targets at travel speeds ranging from 3.2 to 8.0 km h-1. The controller detected the target with a resolution of 480 pixels (vertical) × 640 pixels (horizontal) at a frame rate of 5 Hz and modulated spray nozzles at 10 Hz based on the analysis results of the stereo vision data. A customized graphical user interface was developed to operate the controller, display incoming data, and control other components. Evaluations of the controller with a flat, 3-dimensional box column and artificial tree targets with travel speeds ranging from 3.2 to 8.0 km h-1 showed that the controller consistently activated the nozzles 158.7 to 293.6 ms before the spray target arrived on the spray axis. The controller was also capable of repeatedly detecting the volume of the spray target with variations less than 12% for the same target at one travel speed, although it measured the volume generally higher than the manually calculated volume by as much as 33%, which resulted from duplicate target detection between detection cycles of the controller. These duplicated detections caused the overapplications of as much as 38%, although the controller repeatedly discharged spray volumes with variations less than 11% for the same target at the same travel speed. On average, the spray controller required 111.2 to 140.7 ms for data processing in one detection cycle, from image acquisition to activating spray nozzles for travel speeds ranging from 3.2 to 8.0 km h-1, with the potential to reduce this further. The developed controller would provide an economical and versatile platform for future variable rate sprayers with the benefits of stereo vision technology. Keywords: Automation, Crop protection, Precision Agriculture, Pesticide, RealSenseTM.

Effectiveness of a Camera as a UAV Mounted Search Sensor for Target Detection: An Experimental Investigation

In this paper, we consider a problem of autonomous search using single or multiple Unmanned Ariel Vehicles (UAVs) mounted with downward-facing cameras. A model of the effectiveness of the search sensor, camera, in this case, is essential for developing strategies for optimal deployment and path planning of UAVs for efficient search. The probability of detection of a target of interest as a function of its distance from the point directly below the camera is used to model the search effectiveness. We carried out experiments and obtained a search effectiveness model for a camera in the laboratory environment using ArUco markers and triangular shapes as targets.

Analysis of Passive Infrared Detector for Target Detection in an Iot Based Outdoor Environment

Passive infrared (PIR) sensor is widely used in target detection applications as it has remarkable advantages such as low power consumption, less computation and less communication overheads. Non cooperative target tracking such as human or animal in an outdoor environment using wireless sensor node enabled with PIR sensor is very complex process. Sensor characteristics and environmental influences are the factors which make it difficult, use off the shelved binary PIR sensor for this task. This is an attempt to put forward the issues and challenges and its possible solution, while developing a system with the PIR sensor for target detection and approximate tracking in an outdoor environment.

The Empirical Application of Automotive 3D Radar Sensor for Target Detection for an Autonomous Surface Vehicle’s Navigation

Avoiding collisions with other objects is one of the most basic safety tasks undertaken in the operation of floating vehicles. Addressing this challenge is essential, especially during unmanned vehicle navigation processes in autonomous missions. This paper provides an empirical analysis of the surface target detection possibilities in a water environment, which can be used for the future development of tracking and anti-collision systems for autonomous surface vehicles (ASV). The research focuses on identifying the detection ranges and the field of view for various surface targets. Typical objects that could be met in the water environment were analyzed, including a boat and floating objects. This study describes the challenges of implementing automotive radar sensors for anti-collision tasks in a water environment from the perspective of target detection with the application for small ASV performing tasks on the lake.

A New Strategy Involving the Use of Peptides and Graphene Oxide for Fluorescence Turn-on Detection of Proteins.

The detection of proteins is of great biological significance as disease biomarkers in early diagnosis, prognosis tracking and therapeutic evaluation. Thus, we developed a simple, sensitive and universal protein-sensing platform based on peptide and graphene oxide (GO). The design consists of a fluorophore (TAMRA, TAM), a peptide containing eight arginines and peptide ligand that could recognize the target protein, and GO used as a quencher. To demonstrate the feasible use of the sensor for target detection, Bcl-xL was evaluated as the model target. The sensor was proved to be sensitive and applied for the detection of the target proteins in buffer, 2% serum and living cells.

The IR Characteristics Modeling and Simulation of the HEO Target

Space target infrared (IR) characteristics model can be used to design space target detection sensor, generating simulation data to validate the data processing algorithms, such as target detection and tracking. In the work, a HEO target IR characteristics model is built. The model consists of the geometry module and the IR radiometric module. Unlike the traditional space IR model, the temperature of cabin inner is used as the simulation origin. Using the model, the irradiance spectra of HEO target are calculated for different target temperature. With the calculation results, the main wavelength range for HEO detection is analyzed. Using the equvalent temperature, the work also designs a simulative target, which has the similar IR characteristics of a HEO target. The simulator can be used in the ground test for the imaging sensor or target decoy.

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Target Detection and Classification Using Seismic and PIR Sensors

Unattended ground sensors (UGS) are widely used to monitor human activities, such as pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is often limited by high false alarm rates, possibly due to inadequacies of the underlying algorithms and limitations of onboard computation. In this regard, this paper presents a wavelet-based method for target detection and classification. The proposed method has been validated on data sets of seismic and passive infrared sensors for target detection and classification, as well as for payload and movement type identification of the targets. The proposed method has the advantages of fast execution time and low memory requirements and is potentially well-suited for real-time implementation with onboard UGS systems.

Gaussian-state quantum-illumination receivers for target detection

The signal half of an entangled twin beam, generated using spontaneous parametric downconversion, interrogates a region of space that is suspected of containing a target and has high loss and high (entanglement-breaking) background noise. A joint measurement is performed on the returned light and the idler beam that was retained at the transmitter. An optimal quantum receiver, whose implementation is not yet known, was shown to achieve 6 dB gain in the error-probability exponent relative to that achieved with a single coherent-state (classical) laser transmitter and the optimum receiver. We present two structured optical receivers that achieve up to 3 dB gain in the error exponent over that attained with the classical sensor. These are designs of quantum-optical sensors for target detection, which can be readily implemented in a proof-of-concept experiment, that appreciably outperform the best classical sensor in the low-signal-brightness, high-loss, and high-noise operating regime.

The Effect of Task Based Automation for the Control of Unmanned Systems on Operator Performance

Robotic technology will be a vital component of future combat. However, the combination of robotic operational tasks with other traditional military tasks will create high workload peaks during military operations. The objective of this research is to develop and evaluate flexible automation strategies to aid the operator in this complex military environment. In this experiment we evaluated the effect of an automation that was invoked based on task load. Participants conducted a military reconnaissance mission using a simulation that required them to: use an unmanned air vehicle sensor for target detection, monitor an unmanned ground vehicle, and respond to multi-level communications. Participants completed sixteen missions in the environment, during which task load and automation were manipulated. The results of this experiment showed that operator performance did improve when the automation, an aided target recognition for the unmanned air vehicle, was invoked relative to when it was not invoked. Further, when automation was appropriately applied (high task load conditions) workload decreased significantly. This data along with the results of other experiments discussed in this paper indicate that adaptive automation may be a useful mitigation strategy to help offset the potential deleterious effects of high cognitive load on Army robotic operators in a multitasking environment.

Radiometric analysis of infrared sensor performance

The performance of IR sensors for target detection is analyzed with model SENSAT. The model calculates the radiometric relations for passive IR sensors with up to three homogeneous objects in the instantaneous field of view. For the atmospheric part, the computer code LOwTRAN-6 is used within SENSAT. The sensor model has been improved by introducing a noise model for quantum detectors. It takes into account photon noise, thermal detector/preamplifier noise, and g-r and 1/f noise. In combination with a spectral band optimization with respect to the SNR an efficient tool for the radiometric analysis of IR sensor performance is presented. The comparison of model calculations in the 3-5-micro and 8-14-microm bands with experimental measurements yields excellent agreement.