Dhariwal neeraj (26 risultati)

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New. In English.

Hardcover. Condizione: new. Hardcover. This book provides a combined effect and an in-depth exploration of how advanced materials and energy-harvesting technologies create smarter and more adaptive sensing device. Self-powered gas sensors have become a cornerstone of modern technological innovation, finding critical utilities in various sectors ranging from medical and environmental monitoring to industrial safety and smart agriculture. The future demands of gas sensors not only depend on its efficiency and sensitivity but also lie in the fabrication of devices that are flexible, environmental friendly, and self-powered. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Hardback. Condizione: New. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering.…

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a combined effect and an in-depth exploration of how advanced materials and energy-harvesting technologies create smarter and more adaptive sensing device. Self-powered gas sensors have become a cornerstone of modern technological innovation, finding critical utilities in various sectors ranging from medical and environmental monitoring to industrial safety and smart agriculture. The future demands of gas sensors not only depend on its efficiency and sensitivity but also lie in the fabrication of devices that are flexible, environmental friendly, and self-powered.…

Hardcover. Condizione: Brand New. 240 pages. 9.18x6.12x9.45 inches. In Stock.

Condizione: New. Brand New ! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Condizione: New.

Hardcover. Condizione: Brand New. 250 pages. 9.26x6.11x9.49 inches. In Stock.

Hardback. Condizione: New. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering.…

Hardcover. Condizione: new. Hardcover. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: new. Questo è un articolo print on demand.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. 226 pp. Englisch.…

Hardcover. Condizione: new. Hardcover. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a combined effect and an in-depth exploration of how advanced materials and energy-harvesting technologies create smarter and more adaptive sensing device. Self-powered gas sensors have become a cornerstone of modern technological innovation, finding critical utilities in various sectors ranging from medical and environmental monitoring to industrial safety and smart agriculture. The future demands of gas sensors not only depend on its efficiency and sensitivity but also lie in the fabrication of devices that are flexible, environmental friendly, and self-powered. 304 pp. Englisch.…

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a combined effect and an in-depth exploration of how advanced materials and energy-harvesting technologies create smarter and more adaptive sensing device. Self-powered gas sensors have become a cornerstone of modern technological innovation, finding critical utilities in various sectors ranging from medical and environmental monitoring to industrial safety and smart agriculture. The future demands of gas sensors not only depend on its efficiency and sensitivity but also lie in the fabrication of devices that are flexible, environmental friendly, and self-powered.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 316 pp. Englisch.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.