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Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using Python
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You?ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You?ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you?ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.
Book
Nonlinear encoding in diffractive information processing using linear optical materials
Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.
Journal Article
Opto-VLSI devices and circuits for biomedical and healthcare applications
\"The text comprehensively discusses the latest Opto-VLSI devices and circuits useful for healthcare and biomedical applications. It further emphasizes the importance of smart technologies such as artificial intelligence, machine learning, and the internet of things for the biomedical and healthcare industries\"-- Provided by publisher.
BOOK
Probing the interatomic potential of solids with strong-field nonlinear phononics
Ultrashort mid-infrared laser pulses can drive atoms far from their equilibrium positions in LiNbO
3
, exciting high phonon harmonics and providing a way to map the interatomic potential.
Solid display of phonon harmonics
High-harmonic generation of electromagnetic radiation is a well-known example of a nonlinear process. It occurs when photons—usually from a strong laser pulse—interact with nonlinear systems, such as a gas, plasma or solid, in a way that generates new photons with energies that are multiples of the original. This idea can also be applied to phonons—the quasiparticles associated with lattice vibrations—but high-order phonon modes are much harder to generate. Andrea Cavalleri and colleagues now show that ultrashort mid-infrared laser pulses can induce field strengths in lithium niobate (LiNbO
3
) that are large enough to drive atoms far away from their equilibrium positions. Such strong fields can excite up to five phonon harmonics and provide a way to map the interatomic potential, which can be used to benchmark
ab initio
calculations.
Nonlinear optical techniques at visible frequencies have long been applied to condensed matter spectroscopy
1
. However, because many important excitations of solids are found at low energies, much can be gained from the extension of nonlinear optics to mid-infrared and terahertz frequencies
2
,
3
. For example, the nonlinear excitation of lattice vibrations has enabled the dynamic control of material functions
4
,
5
,
6
,
7
,
8
. So far it has only been possible to exploit second-order phonon nonlinearities
9
at terahertz field strengths near one million volts per centimetre. Here we achieve an order-of-magnitude increase in field strength and explore higher-order phonon nonlinearities. We excite up to five harmonics of the A
1
(transverse optical) phonon mode in the ferroelectric material lithium niobate. By using ultrashort mid-infrared laser pulses to drive the atoms far from their equilibrium positions, and measuring the large-amplitude atomic trajectories, we can sample the interatomic potential of lithium niobate, providing a benchmark for
ab initio
calculations for the material. Tomography of the energy surface by high-order nonlinear phononics could benefit many aspects of materials research, including the study of classical and quantum phase transitions.
Journal Article
Investigation of Water-Diesel Emulsion Characteristics using Optical Technique
The idea of using water-in-Diesel (W/D) emulsion in recent studies as fuel for diesel engines is to reduce the emissions. The introduction of water into a diesel engine using W/D emulsion has a number of potential benefits and can be used as an alternative fuel. One of important factors to use this fuel was the distribution of water droplets in emulsion and emulsifier stability. In the present work, the effect of emulsifier dosage (water in diesel ratio) and heating of W/D emulsion on the stability period with using optical technique was investigated. Five samples of W/D emulsion at different emulsifier dosages (5%, 10%, 15%, 20%, and 25%) water content were studied, whereas the heating of emulsions was carried out for 40oC, 60oC, and 80oC. The results obtained from the current work manifested that an increase in water dosage to W/D emulsion had bad effects on the stability period, also, the increase in heating temperature for W/D emulsion revealed a negative effect on the emulsion stability.
Journal Article
Trends in in ovo sexing technologies: insights and interpretation from papers and patents
Numerous researchers and institutions have been developing in ovo sexing technologies to improve animal welfare by identifying male embryos in an early embryonic stage and disposing of them before pain perception. This review gives a complete overview of the technological approaches reported in papers and patents by performing a thorough search using Web of Science and Patstat/Espacenet databases for papers and patents, respectively. Based on a total of 49 papers and 115 patent families reported until May 2023 worldwide, 11 technology categories were defined: 6 non-optical and 5 optical techniques. Every category was described for its characteristics while assessing its potential for application. Next, the dynamics of the publications of in ovo sexing techniques in both paper and patent fields were described through growth curves, and the interest or actual status was visualized using the number of paper citations and the actual legal status of the patents. When comparing the reported technologies in papers to those in patents, scientific gaps were observed, as some of the patented technologies were not reported in the scientific literature, e.g., ion mobility and mass spectrometry approaches. Generally, more diverse approaches in all categories were found in patents, although they do require more scientific evidence through papers or industrial adoption to prove their robustness. Moreover, although there is a recent trend for non-invasive techniques, invasive methods like analyzing DNA through PCR or hormones through immunosensing are still being reported (and might continue to be) in papers and patents. It was also observed that none of the technologies complies with all the industry requirements, although 5 companies already entered the market. On the one hand, more research and harmony between consumers, industry, and governments is necessary. On the other hand, close monitoring of the market performance of the currently available techniques will offer valuable insights into the potential and expectations of in ovo sexing techniques in the poultry industry.
Journal Article
An Optical Flow Measurement Technique based on Continuous Wavelet Transform
Flow measurement underwater oil leak is a challenging problem, due to the complex nature of flow dynamics. Oil jet flow associated with a multi-scale coherent structure in both space and time direction. Optical plume velocimetry (OPV) was developed by (Crone, McDuff, and Wilcock, 2008), and it was the most accurate technique that used for oil leak flow measurement. Despite its better estimation, the OPV measured the oil flow rate with high uncertainty of 21%. This is due to the multi-scale phenomena of oil flow, as well as the limited accuracy of direct cross correlation (DCC) typically used by OPV. This paper proposed a novel technique that considers the multi-scale property of turbulence in flow measurement. The proposed technique is based on continuous wavelet transform and estimates the flow using the following steps: Decomposition of turbulent flow signal by using continuous wavelet transform (CWT), correlation coefficient estimation in which Fast Fourier Transform (FFT) algorithm was used, interpolation and peak detection for the estimated correlation coefficients, and finally, the velocity field estimation. In order to validate the CWT-based technique, a turbulent buoyant jet, which has a similar flow-type of oil jet was experimentally simulated. Then, the CWT-based technique was applied to measure the jet flow, and the outcomes of the technique was compared to the experimental results. As a result, utilizing a smaller number of wavelet scales lead in better flow measurement as compared to the use of larger scales. CWT-based technique was accurately estimated the jet flow rate with standard error of 0.15 m/s, and outperformed the classical algorithms, including FFT, and DCC algorithms, which were measured with error of 3.65 m/s and 4.53 m/s respectively.
Journal Article
Experimental and Numerical Analysis of Opaque Turbulent Buoyant Jet
Opaque fluid flow estimation is a challenging problem due to the complex nature of this flow type. Deepwater Horizon oil spill is one of the real examples of opaque fluid flow. Due to the complicated spill flow and the lack of dedicated flow measurement technique its flow rate was estimated with high uncertainty. In this paper, a simulation of jet flow is conducted experimentally and numerically. This is in order to analyze the difference between them. First, a turbulent buoyant jet was experimentally simulated considering various ranges of nozzle flow rates including laminar and turbulent flow. A video camera was used to capture the jet flow. Then, Fast Fourier Transform (FFT) based method was developed to estimate velocity field from video sequence. The outcomes of experimental results were compared to the outcomes of numerical simulation. As a result, the FFT-based method was estimated the nozzle flow rates with a relative error of 18.2% when it was compared to the measured experimental values. Despite this poor accuracy, a good agreement between experimental and numerical simulation outcomes was found in term of overall velocity field, centerline velocity, axial velocity as well as the distribution of radial velocity.
Journal Article
Electro-optic characterization of synthesized infrared-visible light fields
The measurement and control of light field oscillations enable the study of ultrafast phenomena on sub-cycle time scales. Electro-optic sampling (EOS) is a powerful field characterization approach, in terms of both sensitivity and dynamic range, but it has not reached beyond infrared frequencies. Here, we show the synthesis of a sub-cycle infrared-visible pulse and subsequent complete electric field characterization using EOS. The sampled bandwidth spans from 700 nm to 2700 nm (428 to 110 THz). Tailored electric-field waveforms are generated with a two-channel field synthesizer in the infrared-visible range, with a full-width at half-maximum duration as short as 3.8 fs at a central wavelength of 1.7 µm (176 THz). EOS detection of the complete bandwidth of these waveforms extends it into the visible spectral range. To demonstrate the power of our approach, we use the sub-cycle transients to inject carriers in a thin quartz sample for nonlinear photoconductive field sampling with sub-femtosecond resolution.
A continuum spanning from 300 and 3000 nm is used to synthesize a single-cycle field transient and measure its waveform through electro-optic sampling, speeding up this sensitive technique so that it can access the electric field of visible light.
Journal Article
Towards ultrafast dynamics with split-pulse X-ray photon correlation spectroscopy at free electron laser sources
One of the important challenges in condensed matter science is to understand ultrafast, atomic-scale fluctuations that dictate dynamic processes in equilibrium and non-equilibrium materials. Here, we report an important step towards reaching that goal by using a state-of-the-art perfect crystal based split-and-delay system, capable of splitting individual X-ray pulses and introducing femtosecond to nanosecond time delays. We show the results of an ultrafast hard X-ray photon correlation spectroscopy experiment at LCLS where split X-ray pulses were used to measure the dynamics of gold nanoparticles suspended in hexane. We show how reliable speckle contrast values can be extracted even from very low intensity free electron laser (FEL) speckle patterns by applying maximum likelihood fitting, thus demonstrating the potential of a split-and-delay approach for dynamics measurements at FEL sources. This will enable the characterization of equilibrium and, importantly also reversible non-equilibrium processes in atomically disordered materials.
X-ray photon correlation spectroscopy has been mainly used to measure slow dynamics using synchrotron sources. Here the authors demonstrate the split-and- delay pulse set-up to study nanosecond dynamics of gold nanoparticles using XPCS with free electron laser pulses.
Journal Article