scholarly journals NV center based nano-NMR enhanced by deep learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nati Aharon ◽  
Amit Rotem ◽  
Liam P. McGuinness ◽  
Fedor Jelezko ◽  
Alex Retzker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bayesian Methods ◽  
Signal To Noise Ratio ◽  
Frequency Discrimination ◽  
Nitrogen Vacancy ◽  
Noise Model ◽  
Frequency Resolution ◽  
Great Promise ◽  
Nv Center ◽  
Computational Resources

AbstractThe growing field of nano nuclear magnetic resonance (nano-NMR) seeks to estimate spectra or discriminate between spectra of minuscule amounts of complex molecules. While this field holds great promise, nano-NMR experiments suffer from detrimental inherent noise. This strong noise masks to the weak signal and results in a very low signal-to-noise ratio. Moreover, the noise model is usually complex and unknown, which renders the data processing of the measurement results very complicated. Hence, spectra discrimination is hard to achieve and in particular, it is difficult to reach the optimal discrimination. In this work we present strong indications that this difficulty can be overcome by deep learning (DL) algorithms. The DL algorithms can mitigate the adversarial effects of the noise efficiently by effectively learning the noise model. We show that in the case of frequency discrimination DL algorithms reach the optimal discrimination without having any pre-knowledge of the physical model. Moreover, the DL discrimination scheme outperform Bayesian methods when verified on noisy experimental data obtained by a single Nitrogen-Vacancy (NV) center. In the case of frequency resolution we show that this approach outperforms Bayesian methods even when the latter have full pre-knowledge of the noise model and the former has none. These DL algorithms also emerge as much more efficient in terms of computational resources and run times. Since in many real-world scenarios the noise is complex and difficult to model, we argue that DL is likely to become a dominant tool in the field.

scholarly journals Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation

2021 ◽  
Vol 15 ◽  
pp. 26-32
Author(s):  
Nupur Choudhury ◽  
Kandarpa Kumar Sarma ◽  
Chinmoy Kalita ◽  
Aradhana Misra
Keyword(s):  
Deep Learning ◽  
Spectrum Sensing ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Feature Learning ◽  
Noise Model ◽  
Critical Element ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and derive certain detection performance. To deal with this uncertainty, learning based approaches are being adopted and more recently deep learning based tools have become popular. Here, we propose an approach of spectrum sensing which is based on long short term memory (LSTM) which is a critical element of deep learning networks (DLN). Use of LSTM facilitates implicit feature learning from spectrum data. The DLN is trained using several features and the performance of the proposed sensing technique is validated with the help of an empirical testbed setup using Adalm Pluto. The testbed is trained to acquire the primary signal of a real world radio broadcast taking place using FM. Experimental data show that even at low signal to noise ratio, our approach performs well in terms of detection and classification accuracies, as compared to current spectrum sensing methods.

A Psychophysical Evaluation of Spectral Enhancement

2005 ◽  
Vol 48 (5) ◽  
pp. 1121-1135 ◽  
Author(s):  
Jeffrey J. DiGiovanni ◽  
Peggy B. Nelson ◽  
Robert S. Schlauch
Keyword(s):  
Spectral Resolution ◽  
Speech Intelligibility ◽  
Signal To Noise Ratio ◽  
Frequency Discrimination ◽  
Broadband Noise ◽  
Frequency Resolution ◽  
Spectral Profile ◽  
Spectral Contrast ◽  
Spectral Enhancement ◽  
Specific Implementation

Listeners with sensorineural hearing loss have well-documented elevated hearing thresholds; reduced auditory dynamic ranges; and reduced spectral (or frequency) resolution that may reduce speech intelligibility, especially in the presence of competing sounds. Amplification and amplitude compression partially compensate for elevated thresholds and reduced dynamic ranges but do not remediate the loss in spectral resolution. Spectral-enhancement processing algorithms have been developed that putatively compensate for decreased spectral resolution by increasing the spectral contrast, or the peak-to-trough ratio, of the speech spectrum. Several implementations have been proposed, with mixed success. It is unclear whether the lack of strong success was due to specific implementation parameters or whether the concept of spectral enhancement is fundamentally flawed. The goal of this study was to resolve this ambiguity by testing the effects of spectral enhancement on detection and discrimination of simple, well-defined signals. To that end, groups of normal-hearing (NH) and hearing-impaired (HI) participants listened in 2 psychophysical experiments, including detection and frequency discrimination of narrowband noise signals in the presence of broadband noise. The NH and HI listeners showed an improved ability to detect and discriminate narrowband increments when there were spectral decrements (notches) surrounding the narrowband signals. Spectral enhancements restored increment detection thresholds to within the normal range when both energy and spectral-profile cues were available to listeners. When only spectral-profile cues were available for frequency discrimination tasks, performance improved for HI listeners, but not all HI listeners reached normal levels of discrimination. These results suggest that listeners are able to take advantage of the local improvement in signal-to-noise ratio provided by the spectral decrements.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

scholarly journals Sensing Electrochemical Signals Using a Nitrogen-Vacancy Center in Diamond

Nanomaterials ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 358
Author(s):  
Hossein T. Dinani ◽  
Enrique Muñoz ◽  
Jeronimo R. Maze
Keyword(s):  
Electric Field ◽  
Electron Spin ◽  
Electrolyte Solution ◽  
High Sensitivity ◽  
Theoretical Work ◽  
Coherence Time ◽  
Nitrogen Vacancy ◽  
Local Concentration ◽  
Concentration Of Ions ◽  
Nv Center

Chemical sensors with high sensitivity that can be used under extreme conditions and can be miniaturized are of high interest in science and industry. The nitrogen-vacancy (NV) center in diamond is an ideal candidate as a nanosensor due to the long coherence time of its electron spin and its optical accessibility. In this theoretical work, we propose the use of an NV center to detect electrochemical signals emerging from an electrolyte solution, thus obtaining a concentration sensor. For this purpose, we propose the use of the inhomogeneous dephasing rate of the electron spin of the NV center (1/T2★) as a signal. We show that for a range of mean ionic concentrations in the bulk of the electrolyte solution, the electric field fluctuations produced by the diffusional fluctuations in the local concentration of ions result in dephasing rates that can be inferred from free induction decay measurements. Moreover, we show that for a range of concentrations, the electric field generated at the position of the NV center can be used to estimate the concentration of ions.

scholarly journals Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1602
Author(s):  
Qiaoqiao Sun ◽  
Xuefeng Liu ◽  
Salah Bourennane
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Great Promise ◽  
Learning Models ◽  
Single Level ◽  
Multiple Networks ◽  
Multi Level ◽  
Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

scholarly journals Computing 3D Phase-Type Holograms Based on Deep Learning Method

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 280
Author(s):  
Huadong Zheng ◽  
Jianbin Hu ◽  
Chaojun Zhou ◽  
Xiaoxi Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Contrast Ratio ◽  
Angular Spectrum ◽  
Structural Similarity ◽  
Computational Time ◽  
Experimental Investigations ◽  
Training Strategy ◽  
Holographic Display ◽  
Phase Type

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

scholarly journals Evaluation of working memory in relation to cochlear implant consonant speech discrimination

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Mai M. El Ghazaly ◽  
Mona I. Mourad ◽  
Nesrine H. Hamouda ◽  
Mohamed A. Talaat
Keyword(s):  
Working Memory ◽  
Speech Perception ◽  
High Frequency ◽  
Word List ◽  
Frequency Discrimination ◽  
Frequency Resolution ◽  
Speech Discrimination ◽  
Place Of Articulation ◽  
Strong Positive Relationship ◽  
Speech And Language Development

Abstract Background Speech perception in cochlear implants (CI) is affected by frequency resolution, exposure time, and working memory. Frequency discrimination is especially difficult in CI. Working memory is important for speech and language development and is expected to contribute to the vast variability in CI speech reception and expression outcome. The aim of this study is to evaluate CI patients’ consonants discrimination that varies in voicing, manner, and place of articulation imparting differences in pitch, time, and intensity, and also to evaluate working memory status and its possible effect on consonant discrimination. Results Fifty-five CI patients were included in this study. Their aided thresholds were less than 40 dBHL. Consonant speech discrimination was assessed using Arabic consonant discrimination words. Working memory was assessed using Test of Memory and Learning-2 (TOMAL-2). Subjects were divided according to the onset of hearing loss into prelingual children and postlingual adults and teenagers. Consonant classes studied were fricatives, stops, nasals, and laterals. Performance on the high frequency CVC words was 64.23% ± 17.41 for prelinguals and 61.70% ± 14.47 for postlinguals. These scores were significantly lower than scores on phonetically balanced word list (PBWL) of 79.94% ± 12.69 for prelinguals and 80.80% ± 11.36 for postlinguals. The lowest scores were for the fricatives. Working memory scores were strongly and positively correlated with speech discrimination scores. Conclusions Consonant discrimination using high frequency weighted words can provide a realistic tool for assessment of CI speech perception. Working memory skills showed a strong positive relationship with speech discrimination abilities in CI.

Sign in / Sign up

Export Citation Format

Share Document