scholarly journals Spectrally encoded single-pixel machine vision using diffractive networks

2021 ◽  
Vol 7 (13) ◽  
pp. eabd7690
Author(s):  
Jingxi Li ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yi Luo ◽  
Xurong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Optical Networks ◽  
3D Imaging ◽  
Spatial Information ◽  
Spectral Power ◽  
Measurement Systems ◽  
Terahertz Spectrum ◽  
Spectral Encoding ◽  
Single Pixel

We demonstrate optical networks composed of diffractive layers trained using deep learning to encode the spatial information of objects into the power spectrum of the diffracted light, which are used to classify objects with a single-pixel spectroscopic detector. Using a plasmonic nanoantenna-based detector, we experimentally validated this single-pixel machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also coupled this diffractive network-based spectral encoding with a shallow electronic neural network, which was trained to rapidly reconstruct the images of handwritten digits based on solely the spectral power detected at these ten distinct wavelengths, demonstrating task-specific image decompression. This single-pixel machine vision framework can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with diffractive network-based spectral encoding of information.

Single-Pixel Machine Vision Using Spectral Encoding Through Diffractive Optical Networks

2021 ◽  
Author(s):  
Jingxi Li ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yi Luo ◽  
Xurong Li ◽  
...  
Keyword(s):  
Machine Vision ◽  
Optical Networks ◽  
Spectral Encoding ◽  
Single Pixel

scholarly journals Ultrasonic Time-of-Flight Computed Tomography for Investigation of Batch Crystallisation Processes

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 639
Author(s):  
Panagiotis Koulountzios ◽  
Tomasz Rymarczyk ◽  
Manuchehr Soleimani
Keyword(s):  
Spatial Information ◽  
Injection Rate ◽  
Ultrasound Tomography ◽  
Measurement Systems ◽  
Process Dynamics ◽  
Tomography System ◽  
Ultrasonic Time ◽  
Crystallisation Process ◽  
Liquid Suspensions

Crystallisation is a crucial step in many industrial processes. Many sensors are being investigated for monitoring such processes to enhance the efficiency of them. Ultrasound techniques have been used for particle sizing characterization of liquid suspensions, in crystallisation process. An ultrasound tomography system with an array of ultrasound sensors can provide spatial information inside the process when compared to single-measurement systems. In this study, the batch crystallisation experiments have been conducted in a lab-scale reactor in calcium carbonate crystallisation. Real-time ultrasound tomographic imaging is done via a contactless ultrasound tomography sensor array. The effect of the injection rate and the stirring speed was considered as two control parameters in these crystallisation functions. Transmission mode ultrasound tomography comprises 32 piezoelectric transducers with central frequency of 40 kHz has been used. The process-based experimental investigation shows the capability of the proposed ultrasound tomography system for crystallisation process monitoring. Information on process dynamics, as well as process malfunction, can be obtained via the ultrasound tomography system.

Diffractive network-based single-pixel machine vision

2021 ◽  
Author(s):  
Jingxi Li ◽  
Deniz Mengu ◽  
Nezih T. Yardimci ◽  
Yi Luo ◽  
Xurong Li ◽  
...  
Keyword(s):  
Machine Vision ◽  
Single Pixel

scholarly journals Concatenated Convolutional Neural Network Model for Cuffless Blood Pressure Estimation Using Fuzzy Recurrence Properties of PPG Signals

2022 ◽  
Author(s):  
Ali Bahari Malayeri ◽  
Mohammad Bagher Khodabakhshi
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Neural Networks ◽  
Network Architecture ◽  
Spatial Information ◽  
Recurrence Plot ◽  
Estimation Model ◽  
British Hypertension Society ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure

Abstract Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless blood pressure (BP) estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, Multi-Parameter Intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors (MAE) and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05±5.26 mmHg and 1.58±2.6 mmHg, in turn.

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 1-5
Author(s):  
D. Akbari ◽  
M. Moradizadeh ◽  
M. Akbari
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Vector Machines ◽  
Neural Network Algorithm ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

scholarly journals ARPEGE cloud cover forecast post-processing with convolutional neural network

2020 ◽  
Author(s):  
Florian Dupuy ◽  
Olivier Mestre ◽  
Léo Pfitzner
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Random Forests ◽  
Cloud Cover ◽  
Spatial Information ◽  
Weather Prediction ◽  
Excellent Result ◽  
Machine Learning Algorithms ◽  
Post Processing

&lt;p&gt;Cloud cover is a crucial information for many applications such as planning land observation missions from space. However, cloud cover remains a challenging variable to forecast, and Numerical Weather Prediction (NWP) models suffer from significant biases, hence justifying the use of statistical post-processing techniques. In our application, the ground truth is a gridded cloud cover product derived from satellite observations over Europe, and predictors are spatial fields of various variables produced by ARPEGE (M&amp;#233;t&amp;#233;o-France global NWP) at the corresponding lead time.&lt;/p&gt;&lt;p&gt;In this study, ARPEGE cloud cover is post-processed using a convolutional neural network (CNN). CNN is the most popular machine learning tool to deal with images. In our case, CNN allows to integrate spatial information contained in NWP outputs. We show that a simple U-Net architecture produces significant improvements over Europe. Compared to the raw ARPEGE forecasts, MAE drops from 25.1 % to 17.8 % and RMSE decreases from 37.0 % to 31.6 %. Considering specific needs for earth observation, special interest was put on forecasts with low cloud cover conditions (&lt; 10 %). For this particular nebulosity class, we show that hit rate jumps from 40.6 to 70.7 (which is the order of magnitude of what can be achieved using classical machine learning algorithms such as random forests) while false alarm decreases from 38.2 to 29.9. This is an excellent result, since improving hit rates by means of random forests usually also results in a slight increase of false alarms.&lt;/p&gt;

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