Invited Article: Single-shot THz detection techniques optimized for multidimensional THz spectroscopy

2015 ◽  
Vol 86 (5) ◽  
pp. 051301 ◽  
Author(s):  
Stephanie M. Teo ◽  
Benjamin K. Ofori-Okai ◽  
Christopher A. Werley ◽  
Keith A. Nelson
Keyword(s):  
Thz Spectroscopy ◽  
Single Shot ◽  
Detection Techniques ◽  
Thz Detection

scholarly journals 2D-Raman-THz Spectroscopy with Single-Shot THz Detection

2021 ◽  
Author(s):  
Marta Duchi ◽  
Saurabh Shukla ◽  
Andrey Shalit ◽  
Peter Hamm
Keyword(s):  
Thz Spectroscopy ◽  
Single Shot ◽  
Thz Detection

scholarly journals Organic crystal-based THz source for complex refractive index measurements of window materials using single-shot THz spectroscopy

2021 ◽  
Vol 127 (11) ◽  
Author(s):  
Lufan Du ◽  
Franz Roeder ◽  
Yun Li ◽  
Mostafa Shalaby ◽  
Burgard Beleites ◽  
...  
Keyword(s):  
Refractive Index ◽  
Complex Refractive Index ◽  
Accurate Determination ◽  
Laser Pulses ◽  
Thz Spectroscopy ◽  
Thz Radiation ◽  
Single Shot ◽  
Detection Scheme ◽  
Electro Optic

AbstractWe employed N-benzyl-2-methyl-4-nitroaniline (BNA) crystals bonded on substrates of different thermal conductivity to generate THz radiation by pumping with 800 nm laser pulses. Crystals bonded on sapphire substrate provided four times more THz yield than glass substrate. A pyrodetector and a single-shot electro-optic (EO) diagnostic were employed for measuring the energy and temporal characterisation of the THz pulse. Systematic studies were carried out for the selection of a suitable EO crystal, which allowed accurate determination of the emitted THz spectrum from both substrates. Subsequently, the THz source and single-shot electro-optic detection scheme were employed to measure the complex refractive index of window materials in the THz range.

scholarly journals Object detection in real time based on improved single shot multi-box detector algorithm

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ashwani Kumar ◽  
Zuopeng Justin Zhang ◽  
Hongbo Lyu
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Single Layer ◽  
Single Shot ◽  
Convolutional Network ◽  
Detection Techniques ◽  
Computational Performance ◽  
Speed Up

Abstract In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

scholarly journals A Decisive Object Detection using Deep Learning Techniques

2019 ◽  
Vol 9 (1S) ◽  
pp. 414-417
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
The Other ◽  
Single Shot ◽  
Detection Techniques ◽  
Image Processing Techniques ◽  
Detection Model ◽  
Learning Techniques ◽  
Processing Techniques ◽  
Speed And Accuracy

Object detection is one of the essential features of computer vision and image processing techniques. In today's world, the computer can replicate or outperform the operation that a human can do. One such thing is object detection, and In the case of it, the machines must be trained in such a way that it can recognize the object equivalent to the human does with maximum accuracy. Several object detection techniques are used to train the machine to detect the objects. Some of the most common object detection techniques are R-CNN, Fast R-CNN, Faster R-CNN) Single Shot MultiBox Detector (SSD), and You Only Look Once(YOLO),. Each of these techniques has a different way of approach and accuracy of detecting the objects in real-time. These techniques are differentiated based on their performances, i.e., speed and accuracy. Some techniques may be very accurate in detecting the objects but may lack in the time taken for detecting the objects, whereas, on the other hand, some techniques may be very fast in figuring out the objects but not with greater accuracy. We have trained an object detection model based on the YOLO technique which gave the best performance out of all other existing techniques, though the accuracy of the model is less, the speed of detection is extremely high. So based on our research we have figured out the best performance object detection techniques and also the most accurate technique. A well-trained object detection model must be very optimistic in terms of their speed and accuracy.

scholarly journals Jet Single Shot Detection

2021 ◽  
Vol 251 ◽  
pp. 04027
Author(s):  
Adrian Alan Pol ◽  
Thea Aarrestad ◽  
Katya Govorkova ◽  
Roi Halily ◽  
Tal Kopetz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Hadron Collider ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Hadron Collider ◽  
Single Shot ◽  
Cern Large Hadron Collider ◽  
Detection Techniques ◽  
Shot Detection ◽  
Channel Dependent

We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of calorimeter cells and using a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional regression tasks to measure jet features. We investigate TernaryWeight Networks with weights constrained to {-1, 0, 1} times a layer- and channel-dependent scaling factors. We show that the quantized version of the network closely matches the performance of its full-precision equivalent.

scholarly journals Recent Developments in Spectroscopic Techniques for the Detection of Explosives

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1364 ◽  
Author(s):  
Wei Zhang ◽  
Yue Tang ◽  
Anran Shi ◽  
Lirong Bao ◽  
Yun Shen ◽  
...  
Keyword(s):  
Ion Mobility ◽  
Thz Spectroscopy ◽  
Spectroscopic Techniques ◽  
Trace Detection ◽  
Detection Techniques ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Recent Developments ◽  
Critical Problems ◽  
Detection Of Explosives

Trace detection of explosives has been an ongoing challenge for decades and has become one of several critical problems in defense science; public safety; and global counter-terrorism. As a result, there is a growing interest in employing a wide variety of approaches to detect trace explosive residues. Spectroscopy-based techniques play an irreplaceable role for the detection of energetic substances due to the advantages of rapid, automatic, and non-contact. The present work provides a comprehensive review of the advances made over the past few years in the fields of the applications of terahertz (THz) spectroscopy; laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy; and ion mobility spectrometry (IMS) for trace explosives detection. Furthermore, the advantages and limitations of various spectroscopy-based detection techniques are summarized. Finally, the future development for the detection of explosives is discussed.

scholarly journals Self-referenced single-shot THz detection

2017 ◽  
Vol 25 (14) ◽  
pp. 16140 ◽  
Author(s):  
Brandon K. Russell ◽  
Benjamin K. Ofori-Okai ◽  
Zhijiang Chen ◽  
Matthias C. Hoffmann ◽  
Ying Y. Tsui ◽  
...  
Keyword(s):  
Single Shot ◽  
Thz Detection
Sign in / Sign up

Export Citation Format

Share Document