Computer vision.pptx for pg students study about computer visionshesnasuneer
This document discusses low-level and high-level image processing techniques in computer vision. It explains that low-level methods use little knowledge about image content and involve steps like preprocessing, segmentation, and object description. High-level processing applies knowledge, goals, and plans to perform tasks like pattern recognition and make decisions based on image understanding. The document also covers basic concepts in computer vision like a priori knowledge, heuristics, and syntactic and semantic analysis, and describes how images can be modeled as signals and functions.
this slide is used to know how image processing is done and applications of image processing and its advantages in various sectors .And also some research topics related to image processing
Unit 3 discusses image segmentation techniques. Similarity based techniques group similar image components, like pixels or frames, for compact representation. Common applications include medical imaging, satellite images, and surveillance. Methods include thresholding and k-means clustering. Segmentation of grayscale images is based on discontinuities in pixel values, detecting edges, or similarities using thresholding, region growing, and splitting/merging. Region growing starts with seed pixels and groups neighboring pixels with similar properties. Region splitting starts with the full image and divides non-homogeneous regions, while region merging combines small similar regions.
1) Fourier analysis transforms images from the spatial domain to the frequency domain, allowing images to be manipulated in unexpected ways.
2) It represents any signal as a sum of sinusoids, encoding spatial frequency, magnitude, and phase information for each pixel.
3) This frequency domain representation can then be modified and transformed back, providing a means to filter images and extract geometric information.
This document discusses image processing and digital images. It covers topics like:
- How images can be represented as functions that map pixel locations to intensity values.
- The process of sampling and quantizing to convert a continuous image into a digital image represented as a matrix of integer values.
- Different types of image processing operations including point processing that transform pixel values independently and neighborhood processing that considers pixel locations.
- Specific point processing techniques like negative, log transformations, and gamma correction.
- Image enhancement methods like contrast stretching, histograms, and histogram equalization.
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
This document discusses various techniques for image enhancement in spatial domain. It defines image enhancement as improving visual quality or converting images for better analysis. Key techniques covered include noise removal, contrast adjustment, intensity adjustment, histogram equalization, thresholding, gray level slicing, and image rotation. Conversion methods like grayscale and different file formats are also summarized. Experimental results and applications in fields like medicine, astronomy, and security are mentioned.
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
This document presents a method for non-blind image deblurring using partial differential equations (PDEs). It introduces a PDE-based model to describe the blurring process caused by relative motion between the camera and object. The model is discretized using the Navier-Stokes equation, resulting in a PDE that can be used to deblur images. Algorithms are presented to deblur images blurred in the vertical and horizontal directions separately, as well as a combined algorithm to handle two-directional motion blur. Experimental results on blurred and noisy test images show the PDE method achieves better deblurring compared to other techniques like Wiener filtering, as measured by higher peak signal-to-noise ratio values
Computer vision.pptx for pg students study about computer visionshesnasuneer
This document discusses low-level and high-level image processing techniques in computer vision. It explains that low-level methods use little knowledge about image content and involve steps like preprocessing, segmentation, and object description. High-level processing applies knowledge, goals, and plans to perform tasks like pattern recognition and make decisions based on image understanding. The document also covers basic concepts in computer vision like a priori knowledge, heuristics, and syntactic and semantic analysis, and describes how images can be modeled as signals and functions.
this slide is used to know how image processing is done and applications of image processing and its advantages in various sectors .And also some research topics related to image processing
Unit 3 discusses image segmentation techniques. Similarity based techniques group similar image components, like pixels or frames, for compact representation. Common applications include medical imaging, satellite images, and surveillance. Methods include thresholding and k-means clustering. Segmentation of grayscale images is based on discontinuities in pixel values, detecting edges, or similarities using thresholding, region growing, and splitting/merging. Region growing starts with seed pixels and groups neighboring pixels with similar properties. Region splitting starts with the full image and divides non-homogeneous regions, while region merging combines small similar regions.
1) Fourier analysis transforms images from the spatial domain to the frequency domain, allowing images to be manipulated in unexpected ways.
2) It represents any signal as a sum of sinusoids, encoding spatial frequency, magnitude, and phase information for each pixel.
3) This frequency domain representation can then be modified and transformed back, providing a means to filter images and extract geometric information.
This document discusses image processing and digital images. It covers topics like:
- How images can be represented as functions that map pixel locations to intensity values.
- The process of sampling and quantizing to convert a continuous image into a digital image represented as a matrix of integer values.
- Different types of image processing operations including point processing that transform pixel values independently and neighborhood processing that considers pixel locations.
- Specific point processing techniques like negative, log transformations, and gamma correction.
- Image enhancement methods like contrast stretching, histograms, and histogram equalization.
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
This document discusses various techniques for image enhancement in spatial domain. It defines image enhancement as improving visual quality or converting images for better analysis. Key techniques covered include noise removal, contrast adjustment, intensity adjustment, histogram equalization, thresholding, gray level slicing, and image rotation. Conversion methods like grayscale and different file formats are also summarized. Experimental results and applications in fields like medicine, astronomy, and security are mentioned.
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
This document presents a method for non-blind image deblurring using partial differential equations (PDEs). It introduces a PDE-based model to describe the blurring process caused by relative motion between the camera and object. The model is discretized using the Navier-Stokes equation, resulting in a PDE that can be used to deblur images. Algorithms are presented to deblur images blurred in the vertical and horizontal directions separately, as well as a combined algorithm to handle two-directional motion blur. Experimental results on blurred and noisy test images show the PDE method achieves better deblurring compared to other techniques like Wiener filtering, as measured by higher peak signal-to-noise ratio values
1) Machine learning techniques can be used to learn priors for solving inverse problems like image reconstruction from limited data.
2) Fully learned reconstruction is infeasible due to the large number of parameters needed. Learned post-processing and learned iterative reconstruction methods provide better results.
3) Learned iterative reconstruction formulates the problem as learning updating operators in an iterative optimization scheme, but is computationally challenging due to the need to differentiate through the whole solver. Future work includes methods to address this issue.
Image pre-processing involves operations on images to improve image data by suppressing distortions or enhancing features. There are four categories of pre-processing methods based on pixel neighborhood size used: pixel brightness transformations, geometric transformations, local neighborhood methods, and global image restoration. Pre-processing aims to correct degradations by using prior knowledge about the degradation, image acquisition device, or objects in the image. Common pre-processing methods include brightness and geometric transformations as well as brightness interpolation when re-sampling images.
The document discusses point processing operations in image processing which perform transformations independently on each pixel without considering spatial information. Point processing includes operations like negative, log, power-law transformations, and gamma correction that define a new image as a function of the existing image applied to each pixel. While point processing loses all spatial information, it can be used for basic image enhancement tasks like contrast stretching, histogram equalization, and matching.
This document provides an introduction to image processing. It discusses key concepts such as signals, signal processing, and how images can be represented as signals and matrices. The document covers how images are converted to digital form and stored on computers. It also describes different levels of image processing from low-level operations like enhancement to higher-level tasks like recognition and interpretation. Overall, the document gives an overview of the fundamentals of digital image processing.
Digital images are represented as a finite set of digital values called pixels arranged in a grid. There are several types of digital images including grayscale, RGB color, and binary. Digital image processing involves tasks like image enhancement, restoration, compression, and analysis. The key steps in digital image processing are image acquisition, representation and description, segmentation, recognition and display. The human visual system perceives brightness in a logarithmic fashion and can adapt to a wide range of light intensities. Proper sampling and quantization are required to convert a natural image into a digital image without loss of information.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
This document presents a technique for data hiding using double key integer wavelet transform. The cover image is first transformed to the integer wavelet domain. Then coefficients are randomly selected using Key 2 for embedding secret data. Key 1 determines the number of bits embedded in each coefficient. The optimal pixel adjustment process is applied to improve quality. This technique aims to achieve high hiding capacity, security and visual quality. Experimental results on test images show the proposed method embedding more data than other techniques while maintaining good peak signal to noise ratios, especially when using certain key combinations.
Tomographic reconstruction in nuclear medicineSUMAN GOWNDER
This document discusses various techniques for tomographic reconstruction in nuclear medicine imaging. It begins with an overview of backprojection and Fourier-based reconstruction techniques like simple backprojection, direct Fourier transform reconstruction, and filtered backprojection. It then discusses multislice imaging, factors that influence image quality, and iterative reconstruction algorithms as an alternative to filtered backprojection. Finally, it covers reconstruction of fan-beam, cone-beam, and pinhole SPECT data that require 3D reconstruction algorithms.
This document provides an overview of digital image processing. It discusses what digital images are composed of and how they are processed using computers. The key steps in digital image processing are described as image acquisition, enhancement, restoration, representation and description, and recognition. A variety of techniques can be used at each step like filtering, segmentation, morphological operations, and compression. The document also outlines common sources of digital images, such as from the electromagnetic spectrum, and applications like medical imaging, astronomy, security screening, and human-computer interfaces.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
Color image analyses using four deferent transformationsAlexander Decker
This document discusses and compares four different image transformations: discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (DWT), and discrete multiwavelet transform (DMWT). It analyzes the effectiveness of each transform for processing color images in terms of noise reduction, enhancement, brightness, compression, and resolution. The performance of the techniques is evaluated using computer simulations in Visual Basic 6.
Color image analyses using four deferent transformationsAlexander Decker
This document discusses and compares four different image transformations: discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (DWT), and discrete multiwavelet transform (DMWT). It analyzes the effectiveness of each transform for processing color images in terms of noise reduction, enhancement, brightness, compression, and resolution. The performance of the techniques is evaluated using computer simulations in Visual Basic 6.
This document discusses single object tracking and velocity determination. It begins with an introduction and objectives of the project which is to develop an algorithm for tracking a single object and determining its velocity in a sequence of video frames. It then provides details on preprocessing techniques like mean filtering, Gaussian smoothing and median filtering to reduce noise. It describes segmentation methods including histogram-based, single Gaussian background and frame difference approaches. Feature extraction methods like edges, bounding boxes and color are explained. Object detection using optical flow and block matching is covered. Finally, it discusses tracking and calculating velocity of the moving object. MATLAB is introduced as a technical computing language for solving these types of problems.
This document discusses image deblurring using sparse domain selection. It begins with an introduction that discusses sources of blur and the need for deblurring. It then provides an overview of image deblurring basics including modeling blur with a point spread function. The main method presented is an adaptive sparse domain selection approach that learns image structures to better model patches. It provides experimental results showing improved peak signal to noise ratio and structural similarity index values compared to other methods. In conclusion, the adaptive sparse domain selection is shown to significantly improve sparse modeling and image restoration results.
This document describes a hybrid technique for image enhancement that uses both frequency domain and spatial domain techniques. It begins with applying frequency domain techniques like discrete cosine transform (DCT) or discrete wavelet transform (DWT) to separate an image into magnitude and phase spectra. The magnitude is then enhanced before recombining it with the phase using inverse DCT/DWT. Spatial domain techniques like power law or log transforms are then applied to further enhance contrast and brightness. The technique is evaluated on sample images and shown to achieve better PSNR and lower MSE than frequency domain techniques alone. In conclusion, combining frequency and spatial domain methods provides an effective approach for image enhancement.
Deep Local Parametric Filters for Image EnhancementSean Moran
The document proposes a new DeepLPF method for image enhancement using learnable parametric filters. DeepLPF estimates parameters for elliptical, graduated, and polynomial filters using a CNN to reproduce local image retouching practices. It introduces a novel architecture that regresses spatially localized filter parameters and a plug-and-play neural block with a filter fusion mechanism. Evaluation on benchmark datasets shows DeepLPF achieves state-of-the-art performance for tasks like classical image retouching and low-light enhancement, with an efficient model containing only a few neural weights.
This document compares and analyzes several histogram equalization techniques for image enhancement:
1) Contrast Limited Adaptive Histogram Equalization (CLAHE) divides an image into contextual regions and applies histogram equalization to each region separately, limiting contrast.
2) Dualistic Sub-image Histogram Equalization (DSIHE) decomposes an image into two equal-area sub-images based on the probability density function, equalizes each sub-image, and combines the results.
3) Dynamic Histogram Equalization (DHE) partitions an image histogram based on local minima, allocates a gray scale range to each partition, and applies histogram equalization to each partition within its allocated range.
This document provides an introduction to image enhancement techniques, including spatial domain and frequency domain methods. Spatial domain techniques operate directly on pixel values, including point operations like brightness modification, inverse transformation, thresholding, and gray-level slicing. Spatial operations involve filtering neighborhoods of pixels. Frequency domain methods apply filters in the Fourier domain. Applications of image enhancement include contrast improvement, blur reduction, and preprocessing for computer vision tasks.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.
Here are some useful examples and methods of image enhancement:
Filtering with morphological operators, Histogram equalization, Noise removal using a Wiener filter, Linear contrast adjustment, Median filtering, Unsharp mask filtering, Contrast-limited adaptive histogram equalization (CLAHE). Decorrelation stretch
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
More Related Content
Similar to Image enhancement in the spatial domain chapter 3
1) Machine learning techniques can be used to learn priors for solving inverse problems like image reconstruction from limited data.
2) Fully learned reconstruction is infeasible due to the large number of parameters needed. Learned post-processing and learned iterative reconstruction methods provide better results.
3) Learned iterative reconstruction formulates the problem as learning updating operators in an iterative optimization scheme, but is computationally challenging due to the need to differentiate through the whole solver. Future work includes methods to address this issue.
Image pre-processing involves operations on images to improve image data by suppressing distortions or enhancing features. There are four categories of pre-processing methods based on pixel neighborhood size used: pixel brightness transformations, geometric transformations, local neighborhood methods, and global image restoration. Pre-processing aims to correct degradations by using prior knowledge about the degradation, image acquisition device, or objects in the image. Common pre-processing methods include brightness and geometric transformations as well as brightness interpolation when re-sampling images.
The document discusses point processing operations in image processing which perform transformations independently on each pixel without considering spatial information. Point processing includes operations like negative, log, power-law transformations, and gamma correction that define a new image as a function of the existing image applied to each pixel. While point processing loses all spatial information, it can be used for basic image enhancement tasks like contrast stretching, histogram equalization, and matching.
This document provides an introduction to image processing. It discusses key concepts such as signals, signal processing, and how images can be represented as signals and matrices. The document covers how images are converted to digital form and stored on computers. It also describes different levels of image processing from low-level operations like enhancement to higher-level tasks like recognition and interpretation. Overall, the document gives an overview of the fundamentals of digital image processing.
Digital images are represented as a finite set of digital values called pixels arranged in a grid. There are several types of digital images including grayscale, RGB color, and binary. Digital image processing involves tasks like image enhancement, restoration, compression, and analysis. The key steps in digital image processing are image acquisition, representation and description, segmentation, recognition and display. The human visual system perceives brightness in a logarithmic fashion and can adapt to a wide range of light intensities. Proper sampling and quantization are required to convert a natural image into a digital image without loss of information.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
This document presents a technique for data hiding using double key integer wavelet transform. The cover image is first transformed to the integer wavelet domain. Then coefficients are randomly selected using Key 2 for embedding secret data. Key 1 determines the number of bits embedded in each coefficient. The optimal pixel adjustment process is applied to improve quality. This technique aims to achieve high hiding capacity, security and visual quality. Experimental results on test images show the proposed method embedding more data than other techniques while maintaining good peak signal to noise ratios, especially when using certain key combinations.
Tomographic reconstruction in nuclear medicineSUMAN GOWNDER
This document discusses various techniques for tomographic reconstruction in nuclear medicine imaging. It begins with an overview of backprojection and Fourier-based reconstruction techniques like simple backprojection, direct Fourier transform reconstruction, and filtered backprojection. It then discusses multislice imaging, factors that influence image quality, and iterative reconstruction algorithms as an alternative to filtered backprojection. Finally, it covers reconstruction of fan-beam, cone-beam, and pinhole SPECT data that require 3D reconstruction algorithms.
This document provides an overview of digital image processing. It discusses what digital images are composed of and how they are processed using computers. The key steps in digital image processing are described as image acquisition, enhancement, restoration, representation and description, and recognition. A variety of techniques can be used at each step like filtering, segmentation, morphological operations, and compression. The document also outlines common sources of digital images, such as from the electromagnetic spectrum, and applications like medical imaging, astronomy, security screening, and human-computer interfaces.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
Color image analyses using four deferent transformationsAlexander Decker
This document discusses and compares four different image transformations: discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (DWT), and discrete multiwavelet transform (DMWT). It analyzes the effectiveness of each transform for processing color images in terms of noise reduction, enhancement, brightness, compression, and resolution. The performance of the techniques is evaluated using computer simulations in Visual Basic 6.
Color image analyses using four deferent transformationsAlexander Decker
This document discusses and compares four different image transformations: discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (DWT), and discrete multiwavelet transform (DMWT). It analyzes the effectiveness of each transform for processing color images in terms of noise reduction, enhancement, brightness, compression, and resolution. The performance of the techniques is evaluated using computer simulations in Visual Basic 6.
This document discusses single object tracking and velocity determination. It begins with an introduction and objectives of the project which is to develop an algorithm for tracking a single object and determining its velocity in a sequence of video frames. It then provides details on preprocessing techniques like mean filtering, Gaussian smoothing and median filtering to reduce noise. It describes segmentation methods including histogram-based, single Gaussian background and frame difference approaches. Feature extraction methods like edges, bounding boxes and color are explained. Object detection using optical flow and block matching is covered. Finally, it discusses tracking and calculating velocity of the moving object. MATLAB is introduced as a technical computing language for solving these types of problems.
This document discusses image deblurring using sparse domain selection. It begins with an introduction that discusses sources of blur and the need for deblurring. It then provides an overview of image deblurring basics including modeling blur with a point spread function. The main method presented is an adaptive sparse domain selection approach that learns image structures to better model patches. It provides experimental results showing improved peak signal to noise ratio and structural similarity index values compared to other methods. In conclusion, the adaptive sparse domain selection is shown to significantly improve sparse modeling and image restoration results.
This document describes a hybrid technique for image enhancement that uses both frequency domain and spatial domain techniques. It begins with applying frequency domain techniques like discrete cosine transform (DCT) or discrete wavelet transform (DWT) to separate an image into magnitude and phase spectra. The magnitude is then enhanced before recombining it with the phase using inverse DCT/DWT. Spatial domain techniques like power law or log transforms are then applied to further enhance contrast and brightness. The technique is evaluated on sample images and shown to achieve better PSNR and lower MSE than frequency domain techniques alone. In conclusion, combining frequency and spatial domain methods provides an effective approach for image enhancement.
Deep Local Parametric Filters for Image EnhancementSean Moran
The document proposes a new DeepLPF method for image enhancement using learnable parametric filters. DeepLPF estimates parameters for elliptical, graduated, and polynomial filters using a CNN to reproduce local image retouching practices. It introduces a novel architecture that regresses spatially localized filter parameters and a plug-and-play neural block with a filter fusion mechanism. Evaluation on benchmark datasets shows DeepLPF achieves state-of-the-art performance for tasks like classical image retouching and low-light enhancement, with an efficient model containing only a few neural weights.
This document compares and analyzes several histogram equalization techniques for image enhancement:
1) Contrast Limited Adaptive Histogram Equalization (CLAHE) divides an image into contextual regions and applies histogram equalization to each region separately, limiting contrast.
2) Dualistic Sub-image Histogram Equalization (DSIHE) decomposes an image into two equal-area sub-images based on the probability density function, equalizes each sub-image, and combines the results.
3) Dynamic Histogram Equalization (DHE) partitions an image histogram based on local minima, allocates a gray scale range to each partition, and applies histogram equalization to each partition within its allocated range.
This document provides an introduction to image enhancement techniques, including spatial domain and frequency domain methods. Spatial domain techniques operate directly on pixel values, including point operations like brightness modification, inverse transformation, thresholding, and gray-level slicing. Spatial operations involve filtering neighborhoods of pixels. Frequency domain methods apply filters in the Fourier domain. Applications of image enhancement include contrast improvement, blur reduction, and preprocessing for computer vision tasks.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.
Here are some useful examples and methods of image enhancement:
Filtering with morphological operators, Histogram equalization, Noise removal using a Wiener filter, Linear contrast adjustment, Median filtering, Unsharp mask filtering, Contrast-limited adaptive histogram equalization (CLAHE). Decorrelation stretch
Similar to Image enhancement in the spatial domain chapter 3 (20)
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
This presentation is about Food Delivery Systems and how they are developed using the Software Development Life Cycle (SDLC) and other methods. It explains the steps involved in creating a food delivery app, from planning and designing to testing and launching. The slide also covers different tools and technologies used to make these systems work efficiently.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
1. Image Enhancement in the
Spatial Domain
(chapter 3)
Math 5467, Spring 2008
Most slides stolen from Gonzalez &
Woods, Steve Seitz and Alexei Efros
2. Image Enhancement (Spatial)
• Image enhancement:
1. Improving the interpretability or perception of
information in images for human viewers
2. Providing `better' input for other automated
image processing techniques
• Spatial domain methods:
operate directly on pixels
• Frequency domain methods:
operate on the Fourier transform of an image
3. Point Processing
• The simplest kind of range transformations
are these independent of position x,y:
g = T(f)
• This is called point processing.
• Important: every pixel for himself – spatial
information completely lost!
4. Obstacle with point processing
• Assume that f is the clown image and T
is a random function and apply g = T(f):
• What we take from this?
1. May need spatial information
2. Need to restrict the class of
transformation, e.g. assume monotonicity
9. Why power laws are popular?
• A cathode ray tube (CRT), for example,
converts a video signal to light in a
nonlinear way. The light intensity I is
proportional to a power (γ) of the source
voltage VS
• For a computer CRT, γ is about 2.2
• Viewing images properly on monitors
requires γ-correction