Radar application project help

Sep 19, 20131 like406 views

This document discusses de-speckling synthetic aperture radar (SAR) images. It provides code to preprocess an input image, add speckle noise, and apply various filters including Savitzky-Golay, median, and non-decimated wavelet thresholding. The brute force thresholding algorithm is used to select the optimal threshold for wavelet-based de-speckling. Metrics like PSNR and MSE are computed and the original, noisy, and de-speckled images are visualized for comparison.

RADAR APPLICATION (SAMPLE ASSIGNMENT)
Our online Tutors are available 24*7 to provide Help with Help with Radar Application
Homework/Assignment or a long term Graduate/Undergraduate Help with Radar
Application Project. Our Tutors being experienced and proficient in Help with Radar
Application ensure to provide high quality Help with Radar Application Homework Help.
Upload your Help with Radar Application Assignment at ‘Submit Your Assignment’
button or email it to . You can use our ‘Live
Chat’ option to schedule an Online Tutoring session with our Help with Radar
Application Tutors.
De-Speckling SAR (Synthetic Aperture RADAR) Image
Analyze.m
clear all;clc
% Get Image File from the user
[FileName,PathName] = uigetfile(...
{'*.jpg;*.tif;*.png;*.gif','All Image Files';...
'*.*','All Files'},...
'Select Images','MultiSelect','off');
% Constructing FileName and FilePath for reading selected image
I = strcat(PathName,FileName);
RGB = imread(I); % Read Selected Image
% RGB = imread('football.jpg');
OI = preprocess(RGB); % Preprocess Seleted Image
% Get variance of noise from user
v = input('Enter variance of speckle noise = ');
NI = AddSpecNoise(OI,v);
% Applying Savitzky-Golay Filter on Noisy Image
B = sgolayfilt(NI,3,41,[],2);
% Applying Median Filter on Noisy Image
C = medfilt2(NI,[3 3]);
% Get level of wavelet decomposition from user
L = input('Enter level of wavelet decomposition = ');
% Compute Non-Decimated Two Dimensional Wavelet Transform
AI = ndwt2(OI,L,'db1');
BI = ndwt2(B,L,'db1');
CI = ndwt2(C,L,'db1');
% Applying Brute Force Threshold Algorithm for finding threshold
[threshtemp MSEtemp PSNRtemp] = bft(NI,AI,BI,CI,L,2,'try');
info@assignmentpedia.com

% Selecting best threshold value from previous BFT ouput which gives
% maximum PSNR as selecting for minimum MSE degrades the visual quality of
% image.
thresh = threshtemp(PSNRtemp==max(max(PSNRtemp)));
thresh = max(max(thresh));
% Applying Brute Force Threshold Algorithm for computing best result
[thresh MSE PSNR DI] = bft(NI,AI,BI,CI,L,2,'execute',thresh);
% Visualize Image
subplot(2,3,1);imshow(OI);title('Original Image');
subplot(2,3,2);imshow(NI);title('Speckled Image');
subplot(2,3,3);imshow(B);title('Savitzky-Golay Filetered Image');
subplot(2,3,4);imshow(C);title('Median Filetered Image');
subplot(2,3,5);imshow(DI);title('De-Speckled Image');
xlabel(['PSNR = ',num2str(PSNR),' dB',' ','MSE = ',num2str(MSE)]);
figure(2);
subplot(1,2,1);imshow(NI);title('Speckled Image');
subplot(1,2,2);imshow(DI);title('De-Speckled Image');
xlabel(['PSNR = ',num2str(PSNR),' dB',' ','MSE = ',num2str(MSE)]);
visit us at www.assignmentpedia.com or email us at info@assignmentpedia.com or call us at +1 520 8371215

발표자: 배재성(KAIST 석사과정) 발표일: 2018.10. 최근 딥러닝을 이용한 방법은 다양한 음성 인식 과제에서 괄목할 만한 성과를 내고 있습니다. 특히 Convolutional Neural Network (CNN)을 이용한 방식은 지역적인 특징 (local feature)들을 효과적으로 잡아낼 수 있기 때문에 비교적 짧은 시간 의존도를 가지는 음성 키워드 인식이나 음소 단위 인식과 같은 과제들에서 활발히 사용되고 있습니다. 그러나 CNN은 낮은 레벨의 특징들 간의 공간적 관계성을 고려하지 않는다는 한계점이 있습니다. 이를 극복하기 위해 캡슐 네트워크 구조를 도입하여 음성 스펙트로그램에서 추출된 특징들의 공간적 관계성을 고려하고자 하였습니다. 구글 음성 단어 데이터셋에서 CNN과 그 성능을 비교해 보았으며, 깨끗한 환경과 잡음 환경 모두에서 주목할만한 성능 향상을 이끌어 냈습니다.

Fractal Image Compression Using Quadtree DecompositionHarshit Varshney

This document summarizes a student project on fractal image compression using quadtree decomposition. It introduces fractal image compression and quadtree decomposition partitioning. The proposed algorithm divides the original image using quadtree decomposition with a threshold of 0.2 and minimum and maximum block sizes of 2 and 64. It records fractal coding information and uses Huffman coding for compression. Experimental results on test images show compression ratios from 2.45 to 12.79 and reconstruction PSNR values from 22.24 to 27.35.

Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering

The document summarizes speech recognition front-end technologies including voice activity detection (VAD) and speech enhancement. It discusses conventional signal processing based approaches and more recent deep learning based methods. For VAD, it describes adaptive context attention models that can dynamically adjust the context used based on noise type and SNR. For speech enhancement, it proposes a two-step neural network approach consisting of a prior network that makes multiple predictions from noisy features and a post network that combines these using a boosting method to produce enhanced features, allowing end-to-end training without an explicit masking step. The approach aims to better exploit neural network modeling power while reducing computation cost compared to conventional methods or single-step deep learning frameworks.

Tutorial on convolutional neural networksHojin Yang

Deep Learning in Computer VisionSungjoon Choi

Image CompressionParamjeet Singh Jamwal

SVM Based Saliency Map Technique for Reducing Time Complexity in HEVCIRJET Journal

This document proposes a technique using support vector machine-based saliency maps to reduce computational complexity in HEVC video coding. It describes saliency detection methods that identify prominent regions of images and discusses implementing an SVM-based saliency detection algorithm in MATLAB. The results show transmitting time is significantly reduced compared to full search, reducing complexity while maintaining comparable compression efficiency.

Compression: Images (JPEG)danishrafiq

The document discusses the JPEG image compression standard. It describes the basic JPEG compression pipeline which involves encoding, decoding, colour space transform, discrete cosine transform (DCT), quantization, zigzag scan, differential pulse code modulation (DPCM) on the DC component, run length encoding (RLE) on the AC components, and entropy coding using Huffman or arithmetic coding. It provides details on quantization methods, quantization tables, zigzag scan, DPCM, RLE, and Huffman coding used in JPEG to achieve maximal compression of images.

image compression pptShivangi Saxena

This document discusses image compression techniques. It begins by defining image compression as reducing the data required to represent a digital image. It then discusses why image compression is needed for storage, transmission and other applications. The document outlines different types of redundancies that can be exploited in compression, including spatial, temporal and psychovisual redundancies. It categorizes compression techniques as lossless or lossy and describes several algorithms for each type, including Huffman coding, LZW coding, DPCM, DCT and others. Key aspects like prediction, quantization, fidelity criteria and compression models are also summarized.

Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R

JPEG Image CompressionAishwarya K. M.

The document provides an overview of JPEG image compression. It discusses that JPEG is a commonly used lossy compression method that allows adjusting the degree of compression for a tradeoff between file size and image quality. The JPEG compression process involves splitting the image into 8x8 blocks, converting color space, applying discrete cosine transform (DCT), quantization, zigzag scanning, differential pulse-code modulation (DPCM) on DC coefficients, run length encoding on AC coefficients, and Huffman coding for entropy encoding. Quantization is the main lossy step that discards high frequency data imperceptible to human vision to achieve higher compression ratios.

Discrete Cosine Transform StegonagraphyKaushik Chakraborty

The document discusses DCT (Discrete Cosine Transform) based steganography. It introduces steganography and some examples of its historical uses. It then summarizes the basics of DCT, why it is useful for steganography, an example steganography algorithm that embeds messages in the DCT coefficients of images, and possibilities for future improvements like using both steganography and cryptography for increased security. The presentation was created by a group of students for their steganography project.

Image compression AlgorithmsShivam Shrivastava

1) The document discusses implementing various image compression algorithms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), run length encoding (RLE), and quantization. 2) These algorithms aim to reduce image file size by eliminating redundant or unnecessary pixel data in order to more efficiently store and transmit images. 3) Key steps involve applying transforms to extract coefficients, then quantizing coefficients to remove insignificant values without significantly impacting image quality.

High Speed and Area Efficient 2D DWT Processor Based Image Compressionsipij

The document describes a proposed high speed and area efficient 2D discrete wavelet transform (DWT) processor design for image compression applications implemented on FPGAs. The design uses a pipelined partially serial architecture to enhance speed while optimally utilizing FPGA resources. Simulation results show the design operating at 231MHz on a Spartan 3 FPGA, a 15% improvement over alternative designs. Resource utilization and speed are improved compared to previous implementations through the optimized DWT processor architecture and FPGA platform choice.

Design of Image Compression Algorithm using MATLABIJEEE

This document summarizes an image compression algorithm designed using Matlab. It discusses various image compression techniques including lossy and lossless compression methods. Lossy compression methods like JPEG are suitable for natural images while lossless methods are preferred for medical or archival images. The document also describes the image compression process involving encoding, quantization, decoding and calculation of compression ratios and quality metrics like PSNR and SNR. Specific compression algorithms discussed include Block Truncation Coding and techniques that exploit coding, inter-pixel and psychovisual redundancies like DCT used in JPEG.

convolutional neural network (CNN, or ConvNet)RakeshSaran5

This presentation provides an overview of Convolutional Neural Networks (CNNs). It begins with an introduction to CNNs and their advantages over fully connected networks for image recognition. It then describes the key components of a CNN, including convolution layers, ReLU layers, pooling layers, and fully connected layers. Examples of each component are provided. The presentation concludes with a discussion of CNN use cases for image recognition.

Convolutional neural networkItachi SK

Convolutional Neural Network (CNN) is a type of neural network that can take in an input image, assign importance to areas in the image, and distinguish objects in the image. CNNs use convolutional layers and pooling layers, which help introduce translation invariance to allow the network to recognize patterns and objects regardless of their position in the visual field. CNNs have been very effective for tasks involving visual imagery like image classification but may be less effective for natural language processing tasks that rely more on word order and sequence. Recurrent neural networks (RNNs) that can model sequential data may perform better than CNNs for some natural language processing tasks like text classification.

Project presentation image compression by manish myst, ssgbcoetManish Myst

This document discusses different image compression formats including GIF, PNG, JPEG, and MNG. It provides details on each format such as the algorithms and applications used. GIF uses LZW lossless compression while JPEG uses lossy compression to reduce file sizes. PNG also uses lossless compression with DEFLATE and prediction algorithms. The objective of image compression is to reduce file sizes by eliminating redundant image data through either lossy or lossless compression methods.

lossy compression JPEGMahmoud Hikmet

The document discusses JPEG image compression, which involves lossy compression. It describes the major steps in JPEG coding as: transforming RGB to YIQ/YUV color space and subsampling color; applying discrete cosine transformation (DCT); quantization; zig-zag ordering; DPCM on DC component; run-length encoding; and entropy coding like Huffman coding. Quantization is the main source of loss in JPEG compression, where each DCT value is divided by a quantization value and rounded down.

GAMeshGiuseppe De Marco

The document describes GAMesh, a genetic algorithm-based approach for automatically placing wireless mesh nodes. GAMesh uses genetic algorithms to evolve placement solutions, representing placements as chromosomes with genes encoding node positions. The fitness function evaluates placements based on connectivity and coverage criteria, with penalties for violating constraints like network capacity. Simulations show GAMesh converges to high-quality placements in a number of iterations independent of problem size. The approach requires few parameters and avoids costly site measurements compared to other placement methods.

CNN and its applications by ketakiKetaki Patwari

The document describes a vehicle detection system using a fully convolutional regression network (FCRN). The FCRN is trained on patches from aerial images to predict a density map indicating vehicle locations. The proposed system is evaluated on two public datasets and achieves higher precision and recall than comparative shallow and deep learning methods for vehicle detection in aerial images. The system could help with applications like urban planning and traffic management.

W4101139143IJERA Editor

International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access. Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.

Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...saumyatapu

This document presents a VLSI architecture for an image compression system using vector quantization. It discusses motivation, objectives, and provides an introduction to image compression techniques including scalar and vector quantization. It describes the LBG algorithm for generating codebooks and proposes a multistage vector quantization approach. A cost-effective VLSI architecture for multistage vector quantization is presented, including the design of individual components like the image processing unit and decompression control unit. Results show the proposed architecture can compress images with good quality while maintaining low hardware complexity.

A Review on Image Compression using DCT and DWTIJSRD

This document reviews image compression techniques using discrete cosine transform (DCT) and discrete wavelet transform (DWT). It discusses how DCT transforms images from spatial to frequency domains, allowing for energy compaction and efficient encoding. DWT is a multi-resolution technique that represents images at different frequency bands. The document analyzes various studies that have used DCT and DWT for compression and compares their performance in terms of metrics like peak signal-to-noise ratio and compression ratio. It finds that DWT generally provides better compression performance than DCT, though DCT requires less computational resources. A hybrid DCT-DWT technique is also proposed to combine the advantages of both methods.

Data compression using huffman codingSATYENDRAKUMAR279

DISCRETE COSINE TRANSFORM WITH ADAPTIVE HUFFMAN CODING BASED IMAGE COMPRESSION code with ressult ABSTRACT Method of compression which is Huffman coding based on histogram information and image segmentation. It is used for lossless and lossy compression. Theamount of image will be compressed in lossy manner, and in lossless manner, depends on theinformation obtained by the histogram of the image. The results show that the difference betweenoriginal and compressed images is visually negligible. The compression ratio(CR) and peak signal tonoise ratio(PSNR) are obtained for different images. The relation between compression ratio and peaksignal to noise ratio shows that whenever we increase compression ratio we get PSNR high. We can alsoobtain minimum mean square error. It shows that if we get high PSNR than our image quality is better.

Progressive TextureDr Rupesh Shet

This document describes a proposed algorithm for progressive texture synthesis on 3D surfaces that is optimized for bandwidth-constrained applications. It uses Discrete Wavelet Transform (DWT) and Embedded Zerotree Wavelet (EZW) to decompose textures into multi-resolution coefficients that are then prioritized for progressive transmission based on importance. This allows textures to be incrementally reconstructed at the receiver based on available bandwidth. Experimental results demonstrate the approach synthesizing textures on a 3D bunny model at increasing levels of detail. The algorithm aims to improve on previous work by making texture representation and encoding more seamless and embedded for adaptive streaming applications.

Understanding Convolutional Neural NetworksJeremy Nixon

Deep learningRouyun Pan

This document provides an overview of deep learning concepts including neural networks, regression and classification, convolutional neural networks, and applications of deep learning such as housing price prediction. It discusses techniques for training neural networks including feature extraction, cost functions, gradient descent, and regularization. The document also reviews deep learning frameworks and notable deep learning models like AlexNet that have achieved success in tasks such as image classification.

Road edge recognition using the stripe hough transform from millimeter wave r...I3E Technologies

Protecting commercial radar and communication systemsTBSS Group

This paper discusses the importance of ensuring signal integrity of radar and communication signals that are to be mobilized by the government agencies during war time. It presents the advantages of mobilizing commercial systems and the risk that are associated with it. In addition, it discusses the complexity of sharing these resources among different interested agencies and presents suggested methodologies to mediate the complexities.

More Related Content

What's hot (20)

image compression pptShivangi Saxena

Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R

JPEG Image CompressionAishwarya K. M.

Discrete Cosine Transform StegonagraphyKaushik Chakraborty

Image compression AlgorithmsShivam Shrivastava

High Speed and Area Efficient 2D DWT Processor Based Image Compressionsipij

Design of Image Compression Algorithm using MATLABIJEEE

convolutional neural network (CNN, or ConvNet)RakeshSaran5

Convolutional neural networkItachi SK

Project presentation image compression by manish myst, ssgbcoetManish Myst

lossy compression JPEGMahmoud Hikmet

GAMeshGiuseppe De Marco

CNN and its applications by ketakiKetaki Patwari

W4101139143IJERA Editor

Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...saumyatapu

A Review on Image Compression using DCT and DWTIJSRD

Data compression using huffman codingSATYENDRAKUMAR279

Progressive TextureDr Rupesh Shet

Understanding Convolutional Neural NetworksJeremy Nixon

Deep learningRouyun Pan

image compression pptShivangi Saxena

Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R

JPEG Image CompressionAishwarya K. M.

Discrete Cosine Transform StegonagraphyKaushik Chakraborty

Image compression AlgorithmsShivam Shrivastava

High Speed and Area Efficient 2D DWT Processor Based Image Compressionsipij

Design of Image Compression Algorithm using MATLABIJEEE

convolutional neural network (CNN, or ConvNet)RakeshSaran5

Convolutional neural networkItachi SK

Project presentation image compression by manish myst, ssgbcoetManish Myst

lossy compression JPEGMahmoud Hikmet

GAMeshGiuseppe De Marco

CNN and its applications by ketakiKetaki Patwari

W4101139143IJERA Editor

Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUA...saumyatapu

A Review on Image Compression using DCT and DWTIJSRD

Data compression using huffman codingSATYENDRAKUMAR279

Progressive TextureDr Rupesh Shet

Understanding Convolutional Neural NetworksJeremy Nixon

Deep learningRouyun Pan

Viewers also liked (10)

Road edge recognition using the stripe hough transform from millimeter wave r...I3E Technologies

Protecting commercial radar and communication systemsTBSS Group

RADARSwayoni Bandopadhyay

Radar uses radio waves to detect distant objects. It works by transmitting radio pulses and measuring their reflection off targets to determine range, angle, speed and other characteristics. There are two main types of radar: continuous wave radar which transmits a continuous radio signal, and pulsed radar which uses high power pulses. Radar has many applications including air traffic control, weather observation, speed enforcement, and military uses like air defense and missile guidance. It can operate in all weather conditions and is a vital detection system.

Landmine detection using impulse ground penetrating radarPRADEEP Cheekatla

The document discusses landmine detection using ground penetrating radar (GPR). It begins with background on the landmine problem and existing detection technologies. It then provides a block diagram of the impulse GPR system, describing the impulse generator, antenna system, pulse extender, A/D converter, processor, and visual display. The document discusses implementation of the impulse GPR system and its applications in fields like engineering and military demining. It notes advantages like accurate measurements and ability to detect non-metallic mines, as well as limitations like false alarms. Future work aims to improve detection probabilities and scan rates.

Speed detection-of-moving-vehicle-using-speed-camerasVIKAS SINGH BHADOURIA

Radar Applicationmathurrohitji

The document discusses various applications of radar technology across several fields. It describes how radar is used for military purposes like air defense systems and targeting weapons. It also outlines applications in remote sensing, air traffic control, law enforcement, aircraft and ship safety, space exploration, and more. Some specific examples mentioned include using ground penetrating radar to locate buried objects and map landfills, and impulse radar to search rubble for trapped people.

radar technologyvipin mishra

This document provides an overview of radar systems. It discusses what radar is, the evolution of radar from its initial uses detecting objects with radio waves in the late 1800s. It then explains the basic principles of how radar works to detect objects using radio signal transmission and reflection. Key components of radar systems like transmitters, receivers, antennas and signal processing are described. Applications of radar systems include military, remote sensing, air traffic control, and navigation. The document also discusses radar modulators and antenna design considerations for radar.

Military RadarJeet Adhikary

Radar stands for Radio Detection and Ranging. It is a system that transmits electromagnetic waves and analyzes the echoes from objects to detect and determine their range, altitude, direction or speed. The basic parts of a radar system include a transmitter, receiver, antenna and indicator. The radar equation describes the power returning to the receiving antenna based on factors like the transmitted power, antenna gains, radar cross section of the target, and distance. There are different types of radars like pulse radar, moving target indication radar, pulse Doppler radar and tracking radar used for various applications like air traffic control, missile guidance and ground surveillance.

Radar pptpratibha007

Military radarTouheed Pasha

This document provides an overview of military radar systems. It begins with an introduction to radar and its basic principles of radio detection and ranging. It then describes the radar range equation and different types of radar systems including primary radar like continuous wave and pulse radar, as well as secondary radar. Applications of military radar are discussed, along with advantages such as all-weather capability and disadvantages including limited tracking abilities. The document concludes that military radars are important for early detection and targeting during wartime.

Road edge recognition using the stripe hough transform from millimeter wave r...I3E Technologies

Protecting commercial radar and communication systemsTBSS Group

RADARSwayoni Bandopadhyay

Landmine detection using impulse ground penetrating radarPRADEEP Cheekatla

Speed detection-of-moving-vehicle-using-speed-camerasVIKAS SINGH BHADOURIA

Radar Applicationmathurrohitji

radar technologyvipin mishra

Military RadarJeet Adhikary

Radar pptpratibha007

Military radarTouheed Pasha

Similar to Radar application project help (20)

Build Your Own 3D Scanner: 3D Scanning with Structured LightingDouglas Lanman

Build Your Own 3D Scanner: 3D Scanning with Structured Lighting http://mesh.brown.edu/byo3d/ SIGGRAPH 2009 Courses Douglas Lanman and Gabriel Taubin This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.

Sequential radar trackingAssignmentpedia

This document contains sample MATLAB code for designing and analyzing radar systems. It includes examples for waveform design, target detection, beamforming, and space-time adaptive processing. The code samples show how to perform tasks like plotting the array response, displaying range Doppler maps and estimated target ranges, visualizing antenna and target trajectories in both global and local coordinate systems, and streaming radar return signals.

DIP_Manual.pdfNetraBahadurKatuwal

This document outlines 13 experiments for a digital image processing lab manual, including: 1) displaying color/grayscale images and their negatives, 2) analyzing pixel relationships, 3) applying image transformations, 4) enhancing low contrast images using histogram equalization, 5) examining bit planes, 6) computing FFTs, 7) analyzing image statistics, 8) applying smoothing/median filters, 9) detecting edges using gradient filters, 10) compressing images, 11) restoring images, 12) enhancing using intensity slicing, and 13) detecting edges with Canny edge detection. The document was prepared by Abhishek Appaji.

Image processing project using matlabAssignmentpedia

This document provides information about image processing homework help and an image processing tool. It describes an online tutoring service that provides assistance with image processing assignments and projects. It then shows code for an image processing tool that performs opening and closing operations on an image. The code opens an image, converts it to grayscale, creates a structuring element, performs morphological opening to detect light objects on a dark background, then performs morphological closing to detect dark objects on a light background. It displays the original and resultant images for comparison.

matlab.docxAraniNavaratnarajah2

This MATLAB code applies the Roberts edge detection filter to an input image. It first reads in the image and converts it to grayscale. It then initializes an output matrix to store the filtered image. Next, it defines the Roberts filter masks and loops through the image, calculating the gradient approximations in the x and y directions using the masks. It calculates the magnitude of the gradient vector and stores it in the output matrix. Finally, it displays the original and filtered images, with the filtered image highlighting edges detected by the Roberts filter.

Scmad Chapter07Marcel Caraciolo

The document discusses various classes and APIs in MIDP related to timers, user interfaces, games, and graphics including the Timer class, GameCanvas, Sprite, TiledLayer, LayerManager, Font, and Image. It provides details on methods for each class and examples of how to use timers, animate sprites, detect collisions, and manage game layers. The document also covers basics of the record management system in MIDP.

virtualtouchscreenk.jagan.20

The document describes a virtual touch screen project that allows basic computer operations to be controlled using a laser pointer and webcam. Key components are a laser pointer to indicate location and a webcam to capture the projected area. Frames from the webcam are analyzed to identify bright spots from the laser pointer and transform the coordinates to mouse movements and clicks on the computer screen.

dip.pdfNetraBahadurKatuwal

This document contains a laboratory manual for a Digital Image Processing course for final year engineering students. It includes 10 MATLAB programs covering topics like image attribute extraction, negation, transformations, histogram processing, smoothing, sharpening, edge detection, morphological operations, pseudo coloring, and discrete cosine transform/inverse discrete cosine transform computation. It also provides instructions for labs, a quiz on digital image processing concepts, guidelines for conducting viva exams, and evaluating student performance. The overall aim is to help students enhance their understanding of digital image processing concepts through practical sessions.

Performance analysis of transformation and bogdonov chaotic substitution base...IJECEIAES

In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.

Advanced Game Development with the Mobile 3D Graphics APITomi Aarnio

This document provides an overview of the Mobile 3D Graphics API (M3G), which was designed for 3D graphics on mobile devices. It discusses why developers should use M3G and highlights some of its key features, including scene graphs, dynamic meshes, animation, textures, and more. The document also provides code examples for common tasks like setting up a camera, rendering a rotating cube, and creating animated keyframe sequences.

Smart home security using Telegram chatbotSanjay Crúzé

TestKinni MEW

This document contains solutions to two questions about image processing techniques. Question 1 involves implementing Sobel edge detection using 4 convolution masks and calculating the approximate gradient magnitude. Question 2 involves implementing a corner detector by building a neighborhood matrix around each pixel, computing its singular values via SVD, and highlighting locations where the second singular value exceeds a threshold after removing neighboring duplicates.

Detection and tracking of red color by using matlabAbhiraj Bohra

Introduction to image contrast and enhancement methodAbhishekvb

This document discusses various methods for contrast enhancement of digital images. It defines contrast and describes how contrast is important for distinguishing objects from backgrounds. Two main categories of contrast enhancement methods are described: spatial domain methods, which operate directly on pixel values; and frequency domain methods, which modify the Fourier transform of an image. Specific spatial techniques covered include logarithmic transformation, power law transformation, gamma correction, and histogram equalization. Contrast stretching is also discussed. Examples of code implementations and results are provided. Contrast enhancement has applications in medical imaging, surveillance, and other fields where improving image interpretability is important.

JPEG XR objective and subjective evaluationsTouradj Ebrahimi

The document discusses objective and subjective quality assessment methods for evaluating the rate-distortion performance of JPEG-XR image compression. It provides details on various objective metrics used to compare JPEG-XR to JPEG2000 and JPEG, including PSNR, SSIM, VIF, and others. It also proposes a methodology for subjective testing using a continuous quality scale to validate the results of objective metrics. Preliminary results are presented comparing different JPEG-XR implementations using the objective metrics.

12.節能人因管理：人員數量估計管理系統鄧有光Zih-Ming Yang

The document proposes a population estimation management system using intelligent energy-saving video surveillance technologies. The system uses video stream analysis and a predefined "SeatMap" interface to detect and count the number of people in different areas. It applies background subtraction and thresholding techniques to binarize the video frames for object detection and counting. Various experiments test the system's accuracy under different illumination conditions such as shadows, color variations, and camera motion. The system aims to estimate populations efficiently to help control energy usage and aid in disaster prevention applications.

20210711 deepI2PTakuya Minagawa

This document summarizes a paper titled "DeepI2P: Image-to-Point Cloud Registration via Deep Classification". The paper proposes a method for estimating the camera pose within a point cloud map using a deep learning model. The model first classifies whether points in the point cloud fall within the camera's frustum or image grid. It then performs pose optimization to estimate the camera pose by minimizing the projection error of inlier points onto the image. The method achieves more accurate camera pose estimation compared to existing techniques based on feature matching or depth estimation. It provides a new approach for camera localization using point cloud maps without requiring cross-modal feature learning.

C Graphics FunctionsSHAKOOR AB

This document provides information about graphics functions in C. It begins by explaining graphics modes and how images are displayed on screens using pixels. It then provides details on the initgraph() function which initializes the graphics system. The rest of the document summarizes many common graphics functions like line(), rectangle(), circle(), putpixel(), getpixel() and more, explaining what they do and their parameters.

Final Design Project - Memo (with GUI)Alex Larcheveque

This document describes a beam deflection analysis program developed by Alex Larcheveque and Matthew Cheng. The program calculates beam deflections and rotation angles for simply supported and cantilever beams. It allows the user to input beam properties and load values, then graphs the deflection vs. distance and rotation angle vs. distance. The program was developed by first writing pseudocode, then creating the calculation function and GUI. It was thoroughly tested against provided test cases and graphs to ensure accuracy.

XebiCon'17 : Faites chauffer les neurones de votre Smartphone avec du Deep Le...Publicis Sapient Engineering

Nous entendons aujourd’hui parler de Deep Learning un peu partout : reconnaissance d’images, de sons, génération de textes, etc. Suite aux récentes annonces sur Android Neural Network API et TensorFlowLite et à la release du framework CoreML d’Apple, tout nous pousse vers le “on-device intelligence”. Bien que les techniques et frameworks soient en train de se démocratiser, il reste difficile d’en voir les applications concrètes en entreprise, et encore moins sur des applications mobiles. Nous avons donc décidé de construire un Proof Of Concept pour relever les défis du domaine. A travers une application mobile à but éducatif, utilisant du Deep Learning pour de la reconnaissance d’objets, nous aborderons les impacts de ce type de modèles sur les smartphones, l’architecture pour l’entraînement et le déploiement de modèles sur un service Cloud, ainsi que la construction de l’application mobile avec les dernières nouveautés annoncées.

Build Your Own 3D Scanner: 3D Scanning with Structured LightingDouglas Lanman

Sequential radar trackingAssignmentpedia

DIP_Manual.pdfNetraBahadurKatuwal

Image processing project using matlabAssignmentpedia

matlab.docxAraniNavaratnarajah2

Scmad Chapter07Marcel Caraciolo

virtualtouchscreenk.jagan.20

dip.pdfNetraBahadurKatuwal

Performance analysis of transformation and bogdonov chaotic substitution base...IJECEIAES

Advanced Game Development with the Mobile 3D Graphics APITomi Aarnio

Smart home security using Telegram chatbotSanjay Crúzé

TestKinni MEW

Detection and tracking of red color by using matlabAbhiraj Bohra

Introduction to image contrast and enhancement methodAbhishekvb

JPEG XR objective and subjective evaluationsTouradj Ebrahimi

12.節能人因管理：人員數量估計管理系統鄧有光Zih-Ming Yang

20210711 deepI2PTakuya Minagawa

C Graphics FunctionsSHAKOOR AB

Final Design Project - Memo (with GUI)Alex Larcheveque

XebiCon'17 : Faites chauffer les neurones de votre Smartphone avec du Deep Le...Publicis Sapient Engineering

More from Assignmentpedia (20)

Transmitter side componentsAssignmentpedia

The document describes the components of a CDMA transmitter and receiver. It shows how a data sequence is spread using a gold code by performing an XOR operation between the bits of data and gold code. The spread signal is transmitted and at the receiver, the original data sequence is recovered by performing another XOR operation between the received spread signal and the same gold code. Matlab code is provided to simulate this process of spreading the data at the transmitter and despreading it at the receiver.

Single object range detectionAssignmentpedia

This document describes a sample assignment to simulate and optimize the range of a simplified trebuchet model. The model predicts projectile range based on the fulcrum position and launch angle. It defines variables for the launch angle and fulcrum position, evaluates the projectile range for combinations of these variables using a treb function, and plots the range as a surface over the variable values. The overall goal is to illustrate how optimization of design variables can maximize the projectile range for a trebuchet.

Resolution projectAssignmentpedia

Radar cross section projectAssignmentpedia

Parallel computing homework helpAssignmentpedia

This document compares the speed of performing finite difference calculations using parallel computing toolbox functions on a GPU versus a CPU. It presents a script that runs timed loops of a 2D finite difference heat equation calculation on arrays of varying sizes, both on a GPU and CPU. The results are stored in a performance table showing the millions of operations per second for different loop and array sizes. The GPU is faster for larger problems, with certain GPU optimizations like using arrayfun and GPU arrays providing additional speedups over normal MATLAB syntax.

Network costing analysisAssignmentpedia

This document contains code for a neural network model to perform trend impact analysis. It includes functions for generating event severity degrees probabilistically, computing impact vectors after events occur, and updating steady state impacts over time. The model is trained on time series data and used to generate many scenarios of impacted time series which are summarized and plotted for analysis.

Matlab simulation projectAssignmentpedia

This document provides instructions for calculating pi using the Monte Carlo method in MATLAB. It describes generating random points within a circle of radius 1, counting the total number of points and number inside the circle, and using those values to calculate an approximate value of pi. The code provided generates random points on a plot in real time, displays a running count, and stops when the user clicks a toggle button. It outputs the approximate value of pi based on the random point counts.

Matlab programming projectAssignmentpedia

This document provides a sample MATLAB assignment to solve the wave equation on a square domain with zero boundary conditions. It defines the problem variables, generates a mesh, sets the initial conditions, defines the time discretization, finds the finite element solution, and animates the solution over time. The wave equation is solved using the hyperbolic function in MATLAB's Partial Differential Equation Toolbox.

Links designAssignmentpedia

This document provides functions for calling the statistical package R from within MATLAB. It includes functions to open a connection to an R server process (openR), run R commands from MATLAB (evalR), copy data from MATLAB to an R variable (putRdata), copy data from an R variable to MATLAB (getRdata), and close the connection to the R server process (closeR). It also includes an example (Rdemo.m) that demonstrates connecting MATLAB to an R session, pushing data into R, running simple R commands, and retrieving the results back into MATLAB before closing the connection.

Help with root locus homework1Assignmentpedia

This document contains a MATLAB program that performs stability analysis using the Routh-Hurwitz criterion. It allows the user to input the coefficients of a characteristic equation and analyzes the stability of the system based on the signs of the resulting Routh array. The program displays the number of roots in each quadrant of the s-plane and whether the system is stable, unstable, or marginally stable based on the Routh array calculations. It also provides options for the user to rerun the analysis or clear variables and end the program.

Transmitter subsystemAssignmentpedia

This document provides a sample MATLAB code for implementing an MB-OFDM transmission block according to the ECMA-368 standard. The code takes in a random binary source, scrambles it, performs convolutional encoding and puncturing, and then does three stages of interleaving - symbol, tone, and cyclic interleaving. The interleaved data is then QPSK modulated, has guard and pilot carriers added, undergoes 128-point IFFT, and is transmitted as the baseband signal. A plot of the transmitted signal is also shown.

Computer Networks Homework HelpAssignmentpedia

For further details, do visit our page: http://www.assignmentpedia.com/computer-networking-assignment-help.html We also have a specialised website for Computer Networks Assignment Help. http://www.computernetworkassignmenthelp.com/ Computer Network Homework Help, TCP/IP Protocol, Data Communication Assignment Help, Network Layers Problem Solution, Help with Socket Programming.

Theory of computation homework helpAssignmentpedia

The document discusses proofs related to regular languages, Turing machines, and decidability. 1) It proves that a single-tape TM that cannot write over the input recognizes only regular languages by showing there are a finite number of states the TM can be in after reading the input. 2) It uses diagonalization to show that for any Turing-recognizable language A of TM descriptions, there exists a decidable language not decided by any TM in A. 3) It proves the language of DFAs accepting a string with more 1s than 0s is decidable by reducing the problem to testing if the intersection of a CFL and regular language is empty. 4) It proves a

Econometrics Homework HelpAssignmentpedia

For further details, please pay a visit at: http://www.assignmentpedia.com/econometrics-homework-assignment-help.html We also have an exclusive website for our Econometrics Asignment Help: http://www.statisticsassignmentexperts.com/econometrics-assignment-help.html Do have a look for Online Economics Help, Economics Assignment Tutor, Econometrics Assignment Help, Econometrics Homework Help, Econometrics Solutions using softwares like SPSS, R, STATA, SAS, Minitab, Excel.

Video CodecAssignmentpedia

This document discusses an H.264 baseline video codec implemented in MATLAB. It describes decoding a 4x4 block of data using context-adaptive variable length coding (CAVLC). Key steps include determining the number of non-zero coefficients and trailing ones, decoding coefficient levels and total zeros, and reordering the data into a 4x4 block. The codec takes a bitstream as input and outputs the decoded 4x4 data block.

Radar Spectral AnalysisAssignmentpedia

This document discusses computing and plotting the radially averaged power spectral density (PSD) of a 2D real-valued matrix using MATLAB. It provides a function called raPsd2d that takes an image matrix and spatial resolution as inputs, computes the power spectrum of the image using an FFT, normalizes it, pads it to be square if needed, calculates the radially averaged PSD by averaging over rings in polar coordinates, and plots the result on a log-log scale with labeled axes.

Pi ControllerAssignmentpedia

This document discusses PI controllers and provides a MATLAB function to tune PI and PID controllers using the Ziegler-Nichols S-curve method. The function takes in parameters like set value, settling value, time to reach 23.8% of set value, and time to reach 63.2% of set value. It then calculates the proportional gain Kp, integral time Ti, derivative time Td, and proportional Kc, integral Ki, and derivative Kd constants based on the Ziegler-Nichols tuning formulae for PI or PID control. Online tutoring help is available for PI controller homework and projects.

Help With Digital Communication ProjectAssignmentpedia

The document discusses using MATLAB to simulate a multi-feedback sigma delta modulator and shows code for a simulation of a 3rd order multi-feedback sigma delta modulator. The simulation models the quantization and accumulation stages of the modulator and plots the output spectra to analyze the noise shaping performance. It also provides information about an online tutoring service that provides help with digital communication homework, projects, and assignments.

Fpga Design ProjectAssignmentpedia

This document simulates a phased array radar using MATLAB. It defines a 900-element uniform rectangular array and associates it with the radar's transmitter and receiver. The radar is configured to scan from 45 to -45 degrees azimuth in steps of 6 degrees within 1 second. Two non-fluctuating targets are defined, one approaching and one moving away. Received signals are processed using matched filtering, pulse integration, and detection to estimate the range, angle, and Doppler speed of each target. The estimated parameters match closely with the true parameters of the simulated targets.

Filter Implementation And Evaluation ProjectAssignmentpedia

Transmitter side componentsAssignmentpedia

Single object range detectionAssignmentpedia

Resolution projectAssignmentpedia

Radar cross section projectAssignmentpedia

Parallel computing homework helpAssignmentpedia

Network costing analysisAssignmentpedia

Matlab simulation projectAssignmentpedia

Matlab programming projectAssignmentpedia

Links designAssignmentpedia

Help with root locus homework1Assignmentpedia

Transmitter subsystemAssignmentpedia

Computer Networks Homework HelpAssignmentpedia

Theory of computation homework helpAssignmentpedia

Econometrics Homework HelpAssignmentpedia

Video CodecAssignmentpedia

Radar Spectral AnalysisAssignmentpedia

Pi ControllerAssignmentpedia

Help With Digital Communication ProjectAssignmentpedia

Fpga Design ProjectAssignmentpedia

Filter Implementation And Evaluation ProjectAssignmentpedia

Recently uploaded (20)

Metamorphosis: Life's Transformative JourneyArshad Shaikh

Marie Boran Special Collections Librarian Hardiman Library, University of Gal...Library Association of Ireland

Sinhala_Male_Names.pdf Sinhala_Male_Namekeshanf79

World war-1(Causes & impacts at a glance) PPT by Simanchala Sarab(BABed,sem-4...larencebapu132

Unit 6_Introduction_Phishing_Password Cracking.pdfKanchanPatil34

YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)

A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 795 from Texas, New Mexico, Oklahoma, and Kansas. 95 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003. The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include: - Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources. - Accessibility: Designed for easy reading, wide distribution, and interactive use. - Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.

To study Digestive system of insect.pptxArshad Shaikh

GDGLSPGCOER - Git and GitHub Workshop.pptxazeenhodekar

The ever evoilving world of science /7th class science curiosity /samyans aca...Sandeep Swamy

The Ever-Evolving World of Science Welcome to Grade 7 Science4not just a textbook with facts, but an invitation to question, experiment, and explore the beautiful world we live in. From tiny cells inside a leaf to the movement of celestial bodies, from household materials to underground water flows, this journey will challenge your thinking and expand your knowledge. Notice something special about this book? The page numbers follow the playful flight of a butterfly and a soaring paper plane! Just as these objects take flight, learning soars when curiosity leads the way. Simple observations, like paper planes, have inspired scientific explorations throughout history.

YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)

A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 817 from Texas, New Mexico, Oklahoma, and Kansas. 97 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003. The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include: - Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources. - Accessibility: Designed for easy reading, wide distribution, and interactive use. - Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner. CURRENT CASE COUNT: 817 (As of 05/3/2025) • Texas: 688 (+20)(62% of these cases are in Gaines County). • New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County) • Oklahoma: 16 (+1) • Kansas: 46 (32% of the cases are from Gray County) HOSPITALIZATIONS: 97 (+2) • Texas: 89 (+2) - This is 13.02% of all TX cases. • New Mexico: 7 - This is 10.6% of all NM cases. • Kansas: 1 - This is 2.7% of all KS cases. DEATHS: 3 • Texas: 2 – This is 0.31% of all cases • New Mexico: 1 – This is 1.54% of all cases US NATIONAL CASE COUNT: 967 (Confirmed and suspected): INTERNATIONAL SPREAD (As of 4/2/2025) • Mexico – 865 (+58) ‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality • Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24) ‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations. • Europe: 6,814

Biophysics Chapter 3 Methods of Studying Macromolecules.pdfPKLI-Institute of Nursing and Allied Health Sciences Lahore , Pakistan.

This chapter provides an in-depth overview of the viscosity of macromolecules, an essential concept in biophysics and medical sciences, especially in understanding fluid behavior like blood flow in the human body. Key concepts covered include: ✅ Definition and Types of Viscosity: Dynamic vs. Kinematic viscosity, cohesion, and adhesion. ⚙️ Methods of Measuring Viscosity: Rotary Viscometer Vibrational Viscometer Falling Object Method Capillary Viscometer 🌡️ Factors Affecting Viscosity: Temperature, composition, flow rate. 🩺 Clinical Relevance: Impact of blood viscosity in cardiovascular health. 🌊 Fluid Dynamics: Laminar vs. turbulent flow, Reynolds number. 🔬 Extension Techniques: Chromatography (adsorption, partition, TLC, etc.) Electrophoresis (protein/DNA separation) Sedimentation and Centrifugation methods.

apa-style-referencing-visual-guide-2025.pdfIshika Ghosh

Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition) This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes: ✅ Real examples from original research papers ✅ Color-coded diagrams for clarity ✅ Key rules for in-text citation and reference list formatting ✅ Free citation tools like Mendeley & Zotero explained Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent. Created by: Prof. Ishika Ghosh, Faculty. 📩 For queries or feedback: [email protected]

New Microsoft PowerPoint Presentation.pptxmilanasargsyan5

Introduction to Vibe Coding and Vibe EngineeringDamian T. Gordon

How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...Celine George

Analytic accounts are used to track and manage financial transactions related to specific projects, departments, or business units. They provide detailed insights into costs and revenues at a granular level, independent of the main accounting system. This helps to better understand profitability, performance, and resource allocation, making it easier to make informed financial decisions and strategic planning.

To study the nervous system of insect.pptxArshad Shaikh

The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview: Structure 1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information. 2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing. 3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles. Functions 1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell. 2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding. 3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview: Structure 1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information. 2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing. 3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles. Functions 1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell. 2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding. 3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions. Characteristics 1. *Decentralized*: Insect nervous systems have some autonomy in different body parts. 2. *Specialized*: Different parts of the nervous system are specialized for specific functions. 3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli. The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments. The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive

2541William_McCollough_DigitalDetox.docxcontactwilliamm2546

Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland

Exploring-Substances-Acidic-Basic-and-Neutral.pdfSandeep Swamy

Exploring Substances: Acidic, Basic, and Neutral Welcome to the fascinating world of acids and bases! Join siblings Ashwin and Keerthi as they explore the colorful world of substances at their school's National Science Day fair. Their adventure begins with a mysterious white paper that reveals hidden messages when sprayed with a special liquid. In this presentation, we'll discover how different substances can be classified as acidic, basic, or neutral. We'll explore natural indicators like litmus, red rose extract, and turmeric that help us identify these substances through color changes. We'll also learn about neutralization reactions and their applications in our daily lives. by sandeep swamy

SPRING FESTIVITIES - UK AND USA -Colégio Santa Teresinha

Metamorphosis: Life's Transformative JourneyArshad Shaikh

Marie Boran Special Collections Librarian Hardiman Library, University of Gal...Library Association of Ireland

Sinhala_Male_Names.pdf Sinhala_Male_Namekeshanf79

World war-1(Causes & impacts at a glance) PPT by Simanchala Sarab(BABed,sem-4...larencebapu132

Unit 6_Introduction_Phishing_Password Cracking.pdfKanchanPatil34

YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)

To study Digestive system of insect.pptxArshad Shaikh

GDGLSPGCOER - Git and GitHub Workshop.pptxazeenhodekar

The ever evoilving world of science /7th class science curiosity /samyans aca...Sandeep Swamy

YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)

Biophysics Chapter 3 Methods of Studying Macromolecules.pdfPKLI-Institute of Nursing and Allied Health Sciences Lahore , Pakistan.

apa-style-referencing-visual-guide-2025.pdfIshika Ghosh

New Microsoft PowerPoint Presentation.pptxmilanasargsyan5

Introduction to Vibe Coding and Vibe EngineeringDamian T. Gordon

How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...Celine George

To study the nervous system of insect.pptxArshad Shaikh

2541William_McCollough_DigitalDetox.docxcontactwilliamm2546

Niamh Lucey, Mary Dunne. Health Sciences Libraries Group (LAI). Lighting the ...Library Association of Ireland

Exploring-Substances-Acidic-Basic-and-Neutral.pdfSandeep Swamy

SPRING FESTIVITIES - UK AND USA -Colégio Santa Teresinha

Radar application project help

1. RADAR APPLICATION (SAMPLE ASSIGNMENT) Our online Tutors are available 24*7 to provide Help with Help with Radar Application Homework/Assignment or a long term Graduate/Undergraduate Help with Radar Application Project. Our Tutors being experienced and proficient in Help with Radar Application ensure to provide high quality Help with Radar Application Homework Help. Upload your Help with Radar Application Assignment at ‘Submit Your Assignment’ button or email it to . You can use our ‘Live Chat’ option to schedule an Online Tutoring session with our Help with Radar Application Tutors. De-Speckling SAR (Synthetic Aperture RADAR) Image Analyze.m clear all;clc % Get Image File from the user [FileName,PathName] = uigetfile(... {'*.jpg;*.tif;*.png;*.gif','All Image Files';... '*.*','All Files'},... 'Select Images','MultiSelect','off'); % Constructing FileName and FilePath for reading selected image I = strcat(PathName,FileName); RGB = imread(I); % Read Selected Image % RGB = imread('football.jpg'); OI = preprocess(RGB); % Preprocess Seleted Image % Get variance of noise from user v = input('Enter variance of speckle noise = '); NI = AddSpecNoise(OI,v); % Applying Savitzky-Golay Filter on Noisy Image B = sgolayfilt(NI,3,41,[],2); % Applying Median Filter on Noisy Image C = medfilt2(NI,[3 3]); % Get level of wavelet decomposition from user L = input('Enter level of wavelet decomposition = '); % Compute Non-Decimated Two Dimensional Wavelet Transform AI = ndwt2(OI,L,'db1'); BI = ndwt2(B,L,'db1'); CI = ndwt2(C,L,'db1'); % Applying Brute Force Threshold Algorithm for finding threshold [threshtemp MSEtemp PSNRtemp] = bft(NI,AI,BI,CI,L,2,'try'); [email protected]

2. % Selecting best threshold value from previous BFT ouput which gives % maximum PSNR as selecting for minimum MSE degrades the visual quality of % image. thresh = threshtemp(PSNRtemp==max(max(PSNRtemp))); thresh = max(max(thresh)); % Applying Brute Force Threshold Algorithm for computing best result [thresh MSE PSNR DI] = bft(NI,AI,BI,CI,L,2,'execute',thresh); % Visualize Image subplot(2,3,1);imshow(OI);title('Original Image'); subplot(2,3,2);imshow(NI);title('Speckled Image'); subplot(2,3,3);imshow(B);title('Savitzky-Golay Filetered Image'); subplot(2,3,4);imshow(C);title('Median Filetered Image'); subplot(2,3,5);imshow(DI);title('De-Speckled Image'); xlabel(['PSNR = ',num2str(PSNR),' dB',' ','MSE = ',num2str(MSE)]); figure(2); subplot(1,2,1);imshow(NI);title('Speckled Image'); subplot(1,2,2);imshow(DI);title('De-Speckled Image'); xlabel(['PSNR = ',num2str(PSNR),' dB',' ','MSE = ',num2str(MSE)]); visit us at www.assignmentpedia.com or email us at [email protected] or call us at +1 520 8371215

Radar application project help

Recommended

More Related Content

What's hot (20)

Viewers also liked (10)

Similar to Radar application project help (20)

More from Assignmentpedia (20)

Recently uploaded (20)

Radar application project help