Ashutosh rana (20 resultados)

hardcover. Condición: New.

Paperback. Condición: new. Paperback. From Sensor to Cloud: Build Real, Working Edge-AI Devices With TinyML, Computer Vision, and Voice RecognitionFor most of the last decade, Arduino books stopped at blinking LEDs, and Raspberry Pi books stopped at web servers - while "AI" lived somewhere else entirely, behind a cloud API. That's no longer how smart products get built. Today's security cameras detect people on the chip inside them. Factory sensors flag a failing bearing before the vibration ever reaches a server. Smart irrigation systems make decisions in milliseconds, with no internet connection required. This is edge AI, and this book teaches you to build it, one working project at a time.AI and Deep Learning with Arduino and Raspberry Pi is a hands-on, project-based guide to running real machine learning models directly on the two most accessible platforms in electronics. Every chapter after the foundations builds something physical and testable, so that by the end you're not just following instructions - you're able to design your own devices.What makes this book different: Most books pick a lane - Arduino or Raspberry Pi, hardware or machine learning, theory or projects. This one is deliberately built across all three, because that's how real products get made. Sensor nodes run on microcontrollers for battery life; gateways and cameras run on small Linux computers for horsepower, and learning how they talk to each other is how the industry actually works. Every model here is trained, converted, and deployed to run on the device itself, using the TinyML and edge-AI techniques central to modern IoT and embedded product design. Every project ships with a complete parts list, a wiring diagram, working code, and a troubleshooting section - the parts most tutorials skip and most beginners get stuck on.Inside, you'll find: Foundations: why edge AI matters, a full Arduino vs. Raspberry Pi hardware comparison, and how to set up a complete development environmentCore skills: sensors and actuators, C++ on Arduino, Python on Raspberry Pi, and a from-scratch machine learning crash course with an introduction to TinyMLSix hands-on builds: a gesture-controlled smart switch, a vibration-based predictive maintenance monitor, a smart agriculture soil and climate sensor node, a real-time object detection security camera, an AI voice-controlled home assistant, and a smart people-counting systemDeployment: connecting edge devices to the cloud, model optimization and security, and taking a project from prototype to shippable productReference material: a complete parts-sourcing appendix, a troubleshooting quick reference, a code-pattern cheat sheet, a glossary, and 22 cited sourcesWho this book is for: you should be comfortable with basic programming concepts - variables, loops, functions - but no prior machine learning experience is required; Chapter 6 builds it from the ground up. Students, hobbyists, engineers moving into IoT, and educators looking for a semester's worth of lab projects will all find a direct path through this material. You don't need to own every piece of hardware to benefit from every chapter - the reasoning, wiring diagrams, and code are written to be understood even before the parts are in your hands.Written by Dr Rajesh Dey (Principal, Camellia Institute of Engineering & Technology, and Director, Electrovent LLP) with contributing authors Ashutosh Rana (enterprise AI consultant based in Minneapolis), Dr. Mudassir Khan (Assistant Professor, King Khalid University, and holder of seventeen patents), and Dr. Rupali Mahajan (Professor and Associate Dean for Research, Vishwakarma Institute of Technology, author of ten books). This book pairs practical, tested engineering wi Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. pp. 316.

Soft cover. Condición: New. ISBN:9789360632823 N.A.

Condición: New. pp. 316.

Condición: New. pp. 316.

Soft cover. Condición: New. ISBN:9789390885183 N.A.

Condición: New. pp. 179.

Condición: New.

Taschenbuch. Condición: Neu. Neuware.

Paperback. Condición: new. Paperback. From Sensor to Cloud: Build Real, Working Edge-AI Devices With TinyML, Computer Vision, and Voice RecognitionFor most of the last decade, Arduino books stopped at blinking LEDs, and Raspberry Pi books stopped at web servers - while "AI" lived somewhere else entirely, behind a cloud API. That's no longer how smart products get built. Today's security cameras detect people on the chip inside them. Factory sensors flag a failing bearing before the vibration ever reaches a server. Smart irrigation systems make decisions in milliseconds, with no internet connection required. This is edge AI, and this book teaches you to build it, one working project at a time.AI and Deep Learning with Arduino and Raspberry Pi is a hands-on, project-based guide to running real machine learning models directly on the two most accessible platforms in electronics. Every chapter after the foundations builds something physical and testable, so that by the end you're not just following instructions - you're able to design your own devices.What makes this book different: Most books pick a lane - Arduino or Raspberry Pi, hardware or machine learning, theory or projects. This one is deliberately built across all three, because that's how real products get made. Sensor nodes run on microcontrollers for battery life; gateways and cameras run on small Linux computers for horsepower, and learning how they talk to each other is how the industry actually works. Every model here is trained, converted, and deployed to run on the device itself, using the TinyML and edge-AI techniques central to modern IoT and embedded product design. Every project ships with a complete parts list, a wiring diagram, working code, and a troubleshooting section - the parts most tutorials skip and most beginners get stuck on.Inside, you'll find: Foundations: why edge AI matters, a full Arduino vs. Raspberry Pi hardware comparison, and how to set up a complete development environmentCore skills: sensors and actuators, C++ on Arduino, Python on Raspberry Pi, and a from-scratch machine learning crash course with an introduction to TinyMLSix hands-on builds: a gesture-controlled smart switch, a vibration-based predictive maintenance monitor, a smart agriculture soil and climate sensor node, a real-time object detection security camera, an AI voice-controlled home assistant, and a smart people-counting systemDeployment: connecting edge devices to the cloud, model optimization and security, and taking a project from prototype to shippable productReference material: a complete parts-sourcing appendix, a troubleshooting quick reference, a code-pattern cheat sheet, a glossary, and 22 cited sourcesWho this book is for: you should be comfortable with basic programming concepts - variables, loops, functions - but no prior machine learning experience is required; Chapter 6 builds it from the ground up. Students, hobbyists, engineers moving into IoT, and educators looking for a semester's worth of lab projects will all find a direct path through this material. You don't need to own every piece of hardware to benefit from every chapter - the reasoning, wiring diagrams, and code are written to be understood even before the parts are in your hands.Written by Dr Rajesh Dey (Principal, Camellia Institute of Engineering & Technology, and Director, Electrovent LLP) with contributing authors Ashutosh Rana (enterprise AI consultant based in Minneapolis), Dr. Mudassir Khan (Assistant Professor, King Khalid University, and holder of seventeen patents), and Dr. Rupali Mahajan (Professor and Associate Dean for Research, Vishwakarma Institute of Technology, author of ten books). This book pairs practical, tested e Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: Brand New. 100 pages. 8.66x5.91x0.23 inches. In Stock.

Paperback. Condición: new. Paperback. Turn battery data into insight, with code that runs.Every electric vehicle has to answer two questions that it cannot measure directly: how much energy is left, and how long will the battery last? This practical guide shows engineers, students and data scientists how to answer them with physics and machine learning, from the first line of Python to a working battery health monitor on a Raspberry Pi.You need no laboratory and no downloads to start. The book includes evbatt, a compact battery simulator that generates realistic drive cycles, voltages, temperatures and ageing histories for whole fleets of cells, with the hidden ground truth included, so you can learn every method before you touch real data. It also points you to the major public battery datasets for when you are ready.Inside you will learn to: Build coulomb counting, an equivalent-circuit model and an extended Kalman filter, and see exactly where they failEstimate state of charge across -10 to 45 0C with gradient boosting and LSTM neural networks in PyTorchEstimate state of health from partial charging curves, with calibrated uncertainty from Gaussian process regressionPredict remaining useful life from the first 100 cycles, with honest conformal prediction intervalsDetect internal short circuits, loose connections and thermal-runaway precursors in battery modulesOptimise health-aware EV charging around solar power and time-of-day tariffsDeploy models on the edge with NumPy, 8-bit quantisation and C code for the ESP32Build a self-calibrating Raspberry Pi battery health monitor as a capstone projectEvery code listing in the book was run before publication, and every figure was produced by the code beside it. Written for engineering students, researchers, BMS and EV engineers, data scientists moving into energy and mobility, and educators looking for meaningful hands-on AI projects. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condición: new. Paperback. Turn battery data into insight, with code that runs.Every electric vehicle has to answer two questions that it cannot measure directly: how much energy is left, and how long will the battery last? This practical guide shows engineers, students and data scientists how to answer them with physics and machine learning, from the first line of Python to a working battery health monitor on a Raspberry Pi.You need no laboratory and no downloads to start. The book includes evbatt, a compact battery simulator that generates realistic drive cycles, voltages, temperatures and ageing histories for whole fleets of cells, with the hidden ground truth included, so you can learn every method before you touch real data. It also points you to the major public battery datasets for when you are ready.Inside you will learn to: Build coulomb counting, an equivalent-circuit model and an extended Kalman filter, and see exactly where they failEstimate state of charge across -10 to 45 0C with gradient boosting and LSTM neural networks in PyTorchEstimate state of health from partial charging curves, with calibrated uncertainty from Gaussian process regressionPredict remaining useful life from the first 100 cycles, with honest conformal prediction intervalsDetect internal short circuits, loose connections and thermal-runaway precursors in battery modulesOptimise health-aware EV charging around solar power and time-of-day tariffsDeploy models on the edge with NumPy, 8-bit quantisation and C code for the ESP32Build a self-calibrating Raspberry Pi battery health monitor as a capstone projectEvery code listing in the book was run before publication, and every figure was produced by the code beside it. Written for engineering students, researchers, BMS and EV engineers, data scientists moving into energy and mobility, and educators looking for meaningful hands-on AI projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Parkash JaiJai Parkash Godara is currently working as Asst. Prof. in Dept of Electronics & Comm. Engg. at Amity University,Noida,INDIA. He received his M.Sc.(VLSI) in 2003 & M.Tech (Instrumentation) in 2006. His area of interest are.…