00841nam a22002413i 450099100322400970753620040202084441.0040802s1992 it a||||||||||||||||ita 884441192Xb13043080-39ule_instARCHE-099569ExLBiblioteca InterfacoltàitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.709.2Presta, Salvador488642Salvador PrestaMilano :Scheiwiller,c1992210 p. :ill. ;30 cm.b1304308002-04-1405-08-04991003224009707536LE002 Ar. V H 1912002000246196le002C. 1-E0.00-l- 00000.i1366675705-08-04Salvador Presta287029UNISALENTOle00205-08-04ma -itait 0104471nam 22006735 450 991025419360332120200629115812.03-319-21524-810.1007/978-3-319-21524-2(CKB)3710000000452184(EBL)3567952(SSID)ssj0001534893(PQKBManifestationID)11835554(PQKBTitleCode)TC0001534893(PQKBWorkID)11497217(PQKB)11649750(DE-He213)978-3-319-21524-2(MiAaPQ)EBC3567952(PPN)187690057(EXLCZ)99371000000045218420150725d2016 u| 0engur|n|---|||||txtccrUltra-Low-Power and Ultra-Low-Cost Short-Range Wireless Receivers in Nanoscale CMOS /by Zhicheng Lin, Pui-In Mak (Elvis), Rui Paulo Martins1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (119 p.)Analog Circuits and Signal Processing,1872-082XDescription based upon print version of record.3-319-21523-X Includes bibliographical references and index.Introduction -- Design and Implementation of Ultra-Low-Power ZigBee/WPAN Receiver -- A 2.4-GHz ZigBee Receiver Exploiting an RF-to-BB-Current-Reuse Blixer + Hybrid Filter Topology in 65-nm CMOS -- Analysis and Modeling of a Gain-Boosted N-Path Switched-Capacitor Bandpass Filter -- A 0.5-V 1.15-mW 0.2-mm2 Multi-Band ZigBee Receiver Using Function Reuse and Gain-Boosted N-Path Techniques for IoT Applications -- Conclusion.This book provides readers with a description of state-of-the-art techniques to be used for ultra-low-power (ULP) and ultra-low-cost (ULC), short-range wireless receivers. Readers will learn what is required to deploy these receivers in short-range wireless sensor networks, which are proliferating widely to serve the internet of things (IoT) for “smart cities.” The authors address key challenges involved with the technology and the typical tradeoffs between ULP and ULC. Three design examples with advanced circuit techniques are described in order to address these trade-offs, which specially focus on cost minimization. These three techniques enable respectively, cascading of radio frequency (RF) and baseband (BB) circuits under an ultra-low-voltage (ULV) supply, cascoding of RF and BB circuits in current domain for current reuse, and a novel function-reuse receiver architecture, suitable for ULV and multi-band ULP applications such as the sub-GHz ZigBee. ·         Summarizes the state-of-the-art in ultra-low-power (ULP) wireless receivers; ·         Includes novel, ultra-low-power and ultra-low-cost (ULC), analog and RF circuit techniques--from concepts to practice; ·         Describes and demonstrates the first RF-to-baseband current-reuse 2.4GHz receiver and the first gain-boosted function-reuse sub-GHz receiver, with ULP and ULC in 65nm CMOS.  .Analog Circuits and Signal Processing,1872-082XElectronic circuitsElectrical engineeringElectronicsMicroelectronicsCircuits and Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/T24068Communications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Electronics and Microelectronics, Instrumentationhttps://scigraph.springernature.com/ontologies/product-market-codes/T24027Electronic circuits.Electrical engineering.Electronics.Microelectronics.Circuits and Systems.Communications Engineering, Networks.Electronics and Microelectronics, Instrumentation.620Lin Zhichengauthttp://id.loc.gov/vocabulary/relators/aut764143Mak (Elvis) Pui-Inauthttp://id.loc.gov/vocabulary/relators/autMartins Rui Pauloauthttp://id.loc.gov/vocabulary/relators/autBOOK9910254193603321Ultra-Low-Power and Ultra-Low-Cost Short-Range Wireless Receivers in Nanoscale CMOS2512393UNINA08513nam 22004813 450 991091578820332120231121080239.097893904757979390475791(MiAaPQ)EBC30954313(Au-PeEL)EBL30954313(Exl-AI)30954313(CKB)28887494400041(EXLCZ)992888749440004120231121d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMastering OpenCV with Python1st ed.Delhi :Orange Education PVT Ltd,2023.©2023.1 online resource (239 pages)Print version: Vaishya, Ayush Mastering OpenCV with Python: Use NumPy, Scikit, TensorFlow, and Matplotlib to Learn Advanced Algorithms for Machine Learning Through a Set of Practical Projects Delhi : Orange Education PVT Ltd,c2023 Intro -- Cover Page -- Title Page -- Copyright Page -- Dedication Page -- About the Author -- About the Technical Reviewer -- Acknowledgements -- Preface -- Errata -- Table of Contents -- 1. Introduction to Computer Vision -- Introduction -- Structure -- Introduction to Computer Vision -- Applications of Computer Vision -- Python -- OpenCV. -- Brief history of OpenCV -- OpenCV 4.7 -- Supporting Libraries -- NumPy -- Matplotlib -- SciPy -- Scikit-Learn -- Scikit-Image -- Mahotas -- TensorFlow -- Keras -- Dlib -- Environment Setup -- Installing Python -- Installing Python on Windows -- Installing Python on Ubuntu and Mac -- Package Manager -- Installing libraries -- Installing Mahotas -- Installing OpenCV -- Verifying our installation -- IDE -- Documentation -- Conclusion -- Test Your Understanding -- 2. Getting Started with Images -- Structure -- Introduction to images and pixels -- Loading and displaying images -- Imread() -- Imshow -- Imwrite -- WaitKey -- DestroyAllWindows -- Manipulating images with pixels -- Accessing individual pixels -- Accessing a region of interest (ROI) -- Drawing in OpenCV -- Line -- Rectangle -- Circle -- Text -- Conclusion -- Points to remember -- Test your understanding -- 3. Image Processing Fundamentals -- Structure -- Geometric transformations -- Image translation -- Rotation -- Scaling -- Flipping -- Shearing -- Cropping -- Arithmetic Operations -- Addition -- Subtraction -- Multiplication and division -- Bitwise operations -- AND -- OR -- XOR -- NOT -- Channels and color spaces -- Red Green Blue (RGB) color space -- Blue Green Red (BGR) color space -- Hue Saturation Value (HSV) color space -- Hue Saturation Lightness (HSL) color space -- cvtColor() 67 Hue Saturation Lightness (HSL) color space -- LAB color space -- YCbCr color space -- Conclusion -- Points to Remember -- Test Your Understanding.4. Image Operations -- Structure -- Morphological operations on images -- Erosion -- cv2.Erode() -- Dilation -- cv2.Dilate() -- Opening -- Cv2.morphologyex() -- Closing -- Morphological gradient -- Top hat -- Bottom hat -- Smoothing and blurring -- Average blurring -- Cv2.blur() -- Median blur -- cv2.medianBlur() -- Gaussian blur -- cv2.gaussianBlur() -- Bilateral filter -- cv2.bilateralFilter() -- Conclusion -- Points to remember -- Test your understanding -- 5. Image Histograms -- Structure -- Introduction to histograms -- cv2.calcHist() -- Matplotlib helper functions -- Histogram for colored images -- Two-dimensional histograms -- Histogram with masks -- Histogram equalization -- cv2.equalizeHist() -- Histogram equalization on colored images -- Adaptive histogram equalization -- Contrast limited adaptive histogram equalization (CLAHE) -- cv2.createCLAHE() -- Histograms for feature extraction -- Conclusion -- Points to remember -- Test your understanding -- 6. Image Segmentation -- Structure -- Introduction to Image Segmentation -- Basic Segmentation Techniques -- Image thresholding -- Simple Thresholding -- cv2.threshold() -- Adaptive Thresholding -- cv2.adaptiveThreshold() -- Otsu’s Thresholding -- Edge and contour-based segmentation -- Advanced Segmentation Techniques -- Watershed Algorithm -- GrabCut algorithm -- cv2.grabCut() -- Clustering-based Segmentation -- Deep Learning-based Segmentation -- Conclusion -- Points to Remember -- Test your understanding -- 7. Edges and Contours -- Structure -- Introduction to edges -- Image gradients -- Filters for image gradients -- Sobel Filters -- cv2.Sobel() -- Scharr Operator -- cv2.filter2D -- Laplacian Operators -- Canny Edge Detector -- cv2.Canny() -- Introduction to Contours -- Contour Hierarchy -- Extracting and Visualizing Contours -- cv2.findContours() -- cv2.drawContours() -- Contour Moments.cv2.Moments() -- Properties of Contours -- Area -- cv2.contourArea() -- Perimeter -- Centroid/Center Of mass -- Bounding Rectangle -- cv2.boundingRect() -- cv2.minAreaRect() -- cv2.boxPoints() -- Extent -- Convex Hull -- cv2.convexHull() -- cv2.polyLines() -- Solidity -- Contour Approximation -- cv2.approxPolyDP() -- Contour Filtering and Selection -- Conclusion -- Points to Remember -- Test your understanding -- 8. Machine Learning with Images -- Structure -- Introduction to Machine Learning -- Overfitting and Underfitting -- Evaluation Metrics -- Hyperparameters and Tuning -- KMeans Clustering -- cv2.kmeans() -- k-Nearest Neighbors (k-NN) -- Feature Scaling -- Hyperparameters -- Logistic Regression -- Hyperparameters -- Decision Trees -- Hyperparameters -- Ensemble Learning -- Random Forest -- Randomness -- Hyperparameters -- Support Vector Machines -- Conclusion -- Points to Remember -- Test your understanding -- 9. Advanced Computer Vision Algorithms -- Structure -- FAST (Features from Accelerated Segment Test) -- cv2.FastFeatureDetector_create -- Harris Keypoint Detection -- cv2.cornerHarris -- BRIEF (Binary Robust Independent Elementary Features) -- cv2.ORB_create -- ORB (Oriented FAST and Rotated BRIEF) -- SIFT (Scale-Invariant Feature Transform) -- cv2.SIFT_create -- RootSIFT (Root Scale-Invariant Feature Transform) -- SURF (Speeded-Up Robust Features) -- Local Binary Patterns -- Histogram of Oriented Gradients -- Conclusion -- Points to Remember -- Test Your Understanding -- 10. Neural Networks -- Structure -- Introduction to Neural Networks -- Design of a Neural Network -- Activation Functions -- Training a Neural Network -- Gradient descent -- Convolutional neural networks -- Layers in a CNN -- Convolutional Layer -- Pooling Layer -- Fully Connected Layer -- Activation Layer -- First Neural Network Model -- Data Loading.Model Instantiation -- Results -- Dropout Regularization -- Neural network architectures -- LeNet -- AlexNet -- VGGNET -- Transfer Learning -- Other Network Architectures -- GoogleNet -- Inception Module -- Architecture -- ResNet -- Conclusion -- Points to remember -- Test your understanding -- 11. Object Detection Using OpenCV -- Structure -- Introduction to object detection -- Detecting objects using sliding windows -- Template matching using OpenCV -- cv2.matchTemplate -- Haar cascades -- Feature extraction for object detection -- Image pyramids -- Facial landmarks with DLIB -- Object tracking using OpenCV -- Conclusion -- Points to remember -- Test your understanding -- 12.Projects Using OpenCV -- Structure -- Automated book inventory system -- Document scanning using OpenCV and OCR -- Face recognition -- Drowsiness detection -- Conclusion -- Index."Mastering OpenCV with Python" immerses you in the captivating realm of computer vision, with a structured approach that equips you with the knowledge and skills essential for success in this rapidly evolving field. From grasping the fundamental concepts of image processing and OpenCV to mastering advanced techniques such as neural networks and object detection, you will gain a comprehensive understanding. Each chapter is enriched with hands-on exercises and real-world projects, ensuring the acquisition of practical skills that can be immediately applied in your professional journey.Computer visionGenerated by AIImage processingGenerated by AIComputer vision.Image processing.Vaishya Ayush1779756MiAaPQMiAaPQMiAaPQBOOK9910915788203321Mastering OpenCV with Python4303311UNINA