Cascade Detector Research Articles

Theoretical simulation of T2SLs InAs/GaSb cascade photodetector for HOT condition * * Project supported by the Polish National Science Centre (No. OPUS/UMO-2015/19/B/ST7/02200).

The investigation of the interband type-II superlattice InAs/GaSb cascade photodetector in the temperature range of 320–380 K is presented. The article is devoted to the theoretical modeling of the cascade detector characteristics by the use of the SimuApsys platform and the 4-band model (k∙p 8 × 8 method). The obtained theoretical characteristics are comparable with experimentally measured ones, suggesting that transport in the absorber is determined by the dynamics of intrinsic carriers and by their lifetime. An overlap equal to 120 meV was used in calculations and a correction term in the " non-common atom” model Hxy = 700 meV was added to the Hamiltonian. The electron and hole effective masses from dispersion curves were estimated and absorption coefficient α was calculated. Based on the simulation detectivity, D* characteristics in the temperature range 320–380 K were calculated. The simulated theoretical characteristics at 320 K are comparable to experimentally measured ones, however at higher temperatures, the experimental value of D* does not reach the theoretical values due to the low resistance of the device and short diffusion length.

Long wavelength infrared quantum cascade detector with a broadband response

A novel structure to achieve long wavelength infrared broadband detection ranging from 7.6–10.4 µm is proposed via quantum cascade detector. With a pair of identical coupled quantum wells separated by a thin barrier, acting as absorption regions, the relative linewidth (ΔE/E) of response can be dramatically broadened to 30.7%. Moreover, this structure introduces about 30% enhancement in blackbody responsivity. It is shown that the spectral shape can be tuned by the bias voltage effectively, while it is insensitive to the working temperature. The device can work at liquid nitrogen temperature with Johnson noise limited blackbody detectivity of 5.4 × 108 Jones.

Hand Detection Using Cascade of Softmax Classifiers

Sliding-window based multiclass hand posture detections are often performed by detecting postures of each predefined category using an independent detector, which makes it lack efficiency and results in high postures confusion rates in real-time applications. To tackle such problems, in this work, an efficient cascade detector that integrates multiple softmax-based binary (SftB) models and a softmax-based multiclass (SftM) model is investigated to perform multiclass posture detection in parallel. The SftB models are used to distinguish the predefined postures from the background regions, and the SftM model is applied to discriminate among all the predefined hand posture categories. Another usage of the cascade structure is that it could effectively decompose the complexity of background pattern space and therefore improve the detection accuracy. In addition, to balance the detection accuracy and efficiency, the HOG features of increasing resolutions will be adopted by classifiers of increasing stage-levels in the cascade structure. The experiments are implemented under various scenarios with complicated background and challenging lightings. Results show the superiority of the proposed SftB classifiers over the traditional binary classifiers such as logistic regression, as well as the accuracy and efficiency improvements brought by the softmax-based cascade architecture compared with the noncascade multiclass softmax detectors.

Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection

Abstract The essential goal of this paper is to extend the functionality of the bio-inspired intelligent HTM (Hierarchical Temporal Memory) network towards two capabilities: (i) object recognition in color images, (ii) detection of multiple objects located in clutter color images. The former extension is based on the development of a novel scheme for the application of three parallel HTM networks that separately process color, texture, and shape information in color images. For the latter HTM extension, we propose a novel system in which HTM is combined with a modified model of computational visual attention. We adopt the results of Bi et al. (2010), Hu et al. (2005), and Kucerova (2011) and add new elements for the calculation of image saliency maps. The proposed algorithm enables one to automatically locate individual objects in clutter images. For computer experiments, a special image database is created to simulate ideal single object images and cluttered images with multiple objects on an inhomogeneous background. The recognition performance of HTM alone and in combination with the salient-region detection method is evaluated. We show that the attention subsystem is able to satisfactorily locate multiple objects in clutter color images with an inhomogeneous background. We also perform benchmark calculations for two selected computer vision methods used for object detection in color clutter images. Namely, the cascade detector and template matching methods are used. Our study confirms that the proposed attention system can improve the capabilities of HTM for object classification in cluttered images. The compound system of visual attention and HTM outperforms the compared methods in both criteria (recall and correct detection rate). However, as expected, the system cannot match the recognition accuracy achieved by HTM for single object images, and thus, further research is needed.

Normal-incidence quantum cascade detector coupled by nanopore structure

A normal-incidence quantum cascade detector coupled by a nanopore array structure (NPS) is demonstrated. The NPS is fabricated on top of an In0.53Ga0.47As contact layer by inductively coupled plasma etching using anodic aluminum oxide as a mask. Because of the nonuniform volume fraction at different areas of the device mesa, the NPS acts as subwavelength random gratings. Normal-incidence light can be scattered into random oblique directions for inter-sub-band transition absorption. With normal incidence, the responsivities of the device reach 24 mA/W at 77 K and 15.7 mA/W at 300 K, which are enhanced 2.23 and 1.96 times, respectively, compared with that of the 45°-edge device.

A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection From SAR Images

Convolutional neural networks (CNNs) have found applications in ship detection from synthetic aperture radar (SAR) images. However, there are some challenges hamper their advance. First, the detected bounding boxes are not very compact. Second, there are quite a few missing detections for small and densely clustered ships. Third, objects with analogical scatterings on land are detected as ships by making mistake. This is due to: 1) the CNN-based SAR ship detectors cannot utilize the spatial information very sufficiently; 2) features learned from CNNs only describe SAR images in space domain while neglecting the information hidden in frequency domain; and 3) information contained in the meta-data file, which may link to other sources, is not taken into account. To overcome these problems, in this paper, a cascade coupled CNN-guided (3C2N-guided) visual attention method for SAR ship detection is proposed. This method considers the newly presented 3C2N model as a qualified ship proposal generator because the images’ spatial information is utilized more sufficiently. The 3C2N model, with coupled CNN as the baseline, consists of a sequence of cascade detectors for training. Complementally, a pulse cosine transformation-based visual attention model in frequency domain is operated on the adaptive regions for ship discrimination. This could further refine the proposals’ locations and could significantly reduce the missing detections and false alarms. In addition, the digital elevation model data are adopted to remove ship-like targets on land. Experimental evaluations on 25 Sentinel-1 images demonstrate that the proposed method is superior to the previous state-of-the-art methods.

Fast road obstacle detection method based on maximally stable extremal regions

Road obstacle detection is an important component of the advanced driver assistance system, and to improve the speed and accuracy of road obstacle detection method is a vital task. In this article, fast image region-matching method based on the maximally stable extremal regions method is proposed to improve the speed of image matching. The theoretical feasibility of detection method combining monocular camera with inertial measurement unit (IMU) is clarified. The fast road obstacle detection method based on maximally stable extremal regions combining fast image region-matching method based on maximally stable extremal regions and the vision-IMU-based obstacle detection method is proposed to bypass obstacle classification and to reduce time and space complexity for road environment perception. The AdaBoost cascade detector, the speeded-up robust features-based obstacle detection method, and the proposed method are used to detect obstacles in outdoor contrast tests. Test results show that the proposed method has higher accuracy, and the reason of high accuracy is analyzed. The processing time of AdaBoost cascade detector, speeded-up robust features-based obstacle detection method, and proposed method are compared, and the results show that the proposed method has faster processing speed, and the reason of faster processing speed is analyzed.

Characterization of multilayer Thick-GEM geometries as 10B converters aiming thermal neutron detection

Boron-based thermal neutron detectors have recently regained some attention from the instrumentation community as a strong alternative to helium-3 detectors. From the existing concepts exploiting boron layers in position sensitive detectors, the Cascade [1] is the one that takes full advantage of the 2D capabilities of gaseous detectors, with the position resolution not limited by the architecture of the detector. In this work, a proposal for the Cascade detector, based on Thick-GEMs is presented, together with some preliminary studies of the suitable pitch that optimizes the neutron conversion efficiency, while keeping the collection efficiency intact. The characterization of Thick-GEM prototypes produced in Brazil with hole pitch from 0.75 to 3 mm shows that these devices already present a stable performance at low gains, also resulting in fair energy resolution, when cascaded with a standard KaptonTM 50 µm GEM. Results of the first attempts of boron film depositions with Ion Beam Assisted Deposition and characterization by Ion Beam Analysis are also presented.

Spatially Enhanced Bags of Visual Words Representation to Improve Traffic Signs Recognition

Traffic signs play a very vital role in safe driving and in avoiding accidents by informing the driver about the speed limits or possible dangers such as icy roads, imminent road works or pedestrian crossings. Considering the processing time and classification accuracy as a whole, a novel approach for visual words construction was presented, which takes the spatial information of keypoints into account in order to enhance the quality of visual words generated from extracted keypoints using the distance and angle information in the Bags of Visual Words (BoVW) representation. In this paper, we proposed a new computationally efficient method to model global spatial distribution of visual words by taking into consideration the spatial relationships of its visual words. In the first step, the region of interest is extracted using a scanning window with a Haar cascade detector and an AdaBoost classifier to reduce the computational region in the hypothesis generation step. Second, the regions are represented with BoVW and spatial information for classification. Experimental results show that the suggested method could reach comparable performance of the state-of-the-art approaches with less computational complexity and shorter training time. It clearly demonstrates the complementarity of the additional relative spatial information provided by our approach to improve accuracy while maintaining short retrieval time, and can obtain a better traffic sign recognition accuracy than the methods based on the traditional BoVW model.

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

Self-Consistent Calculation of Potential Profile of GaN/AlN Resonace Tunnelling Structures

For resonant tunneling structure with GaN – potential wells and AlN – potential barriers, calculation of internal fields caused by piezoelectric and spontaneous polarization was carried. In the model the effective mass of an electron and a dielectric continuum model using finite difference method self-consistent solutions of the Schrödinger and Poisson system of equations taking into account the contribution of piezoelectric and spontaneous polarizations was found.Based on the found solutions of the Schrödinger and Poisson system of equations for resonance tunneling structure, which functioned as a cascade experimentally realized a quantum cascade detector, calculation of the potential profile and the electron energy spectrum was carried. It was found, that calculated value of detected energy is different from the experimentally obtained not more than 3 %.

Ultra-thin infrared metamaterial detector for multicolor imaging applications.

The next generation of infrared imaging systems requires control of fundamental electromagnetic processes - absorption, polarization, spectral bandwidth - at the pixel level to acquire desirable information about the environment with low system latency. Metamaterial absorbers have sparked interest in the infrared imaging community for their ability to enhance absorption of incoming radiation with color, polarization and/or phase information. However, most metamaterial-based sensors fail to focus incoming radiation into the active region of a ultra-thin detecting element, thus achieving poor detection metrics. Here our multifunctional metamaterial absorber is directly integrated with a novel mid-wave infrared (MWIR) and long-wave infrared (LWIR) detector with an ultra-thin (~λ/15) InAs/GaSb Type-II superlattice (T2SL) interband cascade detector. The deep sub-wavelength metamaterial detector architecture proposed and demonstrated here, thus significantly improves the detection quantum efficiency (QE) and absorption of incoming radiation in a regime typically dominated by Fabry-Perot etalons. Our work evinces the ability of multifunctional metamaterials to realize efficient wavelength selective detection across the infrared spectrum for enhanced multispectral infrared imaging applications.

In-vehicle augmented reality system to provide driving safety information

Improving traffic safety is one of the important goals of intelligent transportation systems. Traffic signs play a very vital role in safe driving and in avoiding accidents by informing the driver about the speed limits or possible dangers such as icy roads, imminent road works or pedestrian crossings. In-vehicle contextual Augmented reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we propose a new AR traffic sign recognition system (AR-TSR) to improve driving safety and enhance the driver’s experience based on the Haar cascade and the bag-of-visual-words approach, using spatial information to improve accuracy and an overview of studies related to the driver’s perception and the effectiveness of the AR in improving driving safety. In the first step, the region of interest (ROI) is extracted using a scanning window with a Haar cascade detector and an AdaBoost classifier to reduce the computational region in the hypothesis-generation step. Second, we proposed a new computationally efficient method to model global spatial distribution of visual words by taking into consideration the spatial relationships of its visual words. Finally, a multiclass sign classifier takes the positive ROIs and assigns a 3D traffic sign for each one using a linear SVM. Experimental results show that the suggested method could reach comparable performance of the state-of-the-art approaches with less computational complexity and shorter training time, and the AR-TSR more strongly impacts the allocation of visual attention during the decision-making phase.

The limit of quantum cascade detectors: A single period device

In this work, we demonstrate a 4.1 μm quantum cascade photodetector with external quantum efficiencies of 40% at 80 K and 25% at 300 K. Such high efficiencies have been made possible by using a single period active region embedded in a facet coupled low-loss dielectric ridge waveguide. This emphasizes the relevance of enhancing the optical interaction for this type of detector in a different manner from increasing the number of periods. Low noise operation was achieved by using photovoltaic operation at zero bias and an elaborate band structure design to prevent undesired scattering paths. A noise equivalent power of 10 pW/Hz and a corresponding specific detectivity of 7×107 cmHz/W at room-temperature, as well as background limited operation below 124 K with a detectivity close to an ideal photodetector, are demonstrated.

Effect of polarization on the performance of ZnO/MgZnO quantum cascade detector

The impact of polarization effect on the performance of ZnO/MgZnO quantum cascade detector (QCD) is investigated with different scattering mechanisms taken into account. The energy band structure and wave functions are achieved by self-consistent calculation of Schrodinger-Poisson equations considering the spontaneous and piezoelectric polarization effects. The polarization field in the well or barrier of the QCD is obtained by assuming continuity of the displacement vector and the electronic transport is determined by piezoelectric polarization induced piezoelectric scattering, LO-phonon scattering and some other scattering mechanisms. The results show that the polarization charges at the interface make a significant contribution to the confining potential, which reduces the dark current, and increases the detectivity. The peak responsivity drops only 6.37% from 57 to 400 K without apparent redshift and the detectivity can be significantly improved by increasing doping concentration. This work is beneficial for the ZnO/MgZnO QCD design.

In-vehicle augmented reality TSR to improve driving safety and enhance the driver’s experience

In-vehicle contextual augmented reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we propose a new AR traffic sign recognition system (AR-TSR) to improve driving safety and enhance the driver’s experience based on the Haar cascade and the Bag-of-Visual-Words approach, using spatial information to improve accuracy and an overview of studies related to the driver’s perception and the effectiveness of the AR in improving driving safety. In the first step, the region of interest (ROI) is extracted using a scanning window with a Haar cascade detector and an AdaBoost classifier to reduce the computational region in the hypothesis generation step. Second, we proposed a new computationally efficient method to model global spatial distribution of visual words by taking into consideration the spatial relationships of its visual words. Finally, a multiclass sign classifier takes the positive ROIs and assigns a 3D traffic sign for each one using a linear SVM. Experimental results show that the suggested method could reach comparable performance of the state-of-the-art approaches with less computational complexity and shorter training time, and the AR-TSR more strongly impacts the allocation of visual attention during the decision-making phase.

Erratum: “High photoresponse in room temperature quantum cascade detector based on coupled quantum well design” [Appl. Phys. Lett. 109, 261107 (2016)

First Page

Role of interface roughness scattering, temperature, and structural parameters on the performance characteristics of III-nitride quantum cascade detectors

A III-nitride quantum cascade detector (QCD) for the fiber optic communication wavelength (∼1.5 μm) has been designed, and the effect of intersubband scattering processes such as longitudinal-optical phonon scattering, ionized impurity scattering, and more importantly interface roughness scattering on responsivity performance has been analyzed. Carrier transport in the detector is modeled using a simplified rate equation approach. It is observed that inclusion of interface roughness scattering in the carrier transport model significantly enhances the responsivity performance of the detector. The effects of roughness conditions for instance mean roughness height and correlation length on responsivity have been examined. The responsivity of the designed detector drops by 2.16 mA/W at 400 K compared to its low temperature value at 50 K and the detection wavelength change with temperature is insignificant, which are very helpful for the stable detection of the radiation for a wide range of operating temperatures and show the thermal stability of III-nitride QCDs. The effects of active well widths, extractor barrier widths, and extractor well widths have been further investigated. A higher responsivity performance is observed for narrower barrier widths. It is noticed that change in the active well width significantly modifies the responsivity of the detector and the wavelength gets red shifted for larger active well widths.

Quantum dot quantum cascade photodetector using a laser structure

We report on a quantum dot quantum cascade detector (QD-QCD), whose structure is derived from a QD cascade laser. In this structure, more ordered InAs QD layers formed in the Stranski–Krastanow growth mode on a thin GaAs buffer layer are incorporated into the active region. This QD-QCD can operate up to room temperature with a peak detection wavelength of 5.8 μm. A responsivity of 3.1 mA/W at 160 K and a detectivity of 3.6 × 108 Jones at 77 K are obtained. The initial performance of the detector is promising, and, by further optimizing the growth of InAs QDs, integrated QD-quantum cascade laser/QCD applications are expected.

Characterization of a scintillating lithium glass ultra-cold neutron detector

A $^{6}$Li glass based scintillation detector developed for the TRIUMF neutron electric dipole moment experiment was characterized using the ultra-cold neutron source at the Paul Scherrer Institute (PSI). The data acquisition system for this detector was demonstrated to perform well at rejecting backgrounds. An estimate of the absolute efficiency of background rejection of $99.7\pm0.1$% is made. For variable ultra-cold neutron rate (varying from $<$1 kHz to approx. 100 kHz per channel) and background rate seen at the Paul Scherrer Institute, we estimate that the absolute detector efficiency is $89.7^{+1.3}_{-1.9}$%. Finally a comparison with a commercial Cascade detector was performed for a specific setup at the West-2 beamline of the ultra-cold neutron source at PSI.