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物品 #:
185711686
Star Identification: Methods, Techniques and Algorithms, (Paperback)
物品 #:
185711686
HKD 1533
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現在訂購，約可在下列時間收貨： 星期三, 十一月 04
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Star identification is the key step for celestial navigation and greatly improves the performance of star sensors.
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What Stands Out
Comprehensive Coverage
Offers a thorough exploration of various methods and algorithms for star identification, making it essential for astrophysics students and enthusiasts seeking to deepen their knowledge in this specialized field.
Expert Insights
Includes contributions from leading researchers, providing cutting-edge perspectives and techniques that differentiate it from competitors and enhance the reader's understanding of contemporary star identification challenges.
Practical Applications
Equips readers with real-world applications, guiding them in applying theoretical concepts to practical star identification scenarios, thus bridging the gap between theory and practice in astronomy.
產品詳情
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | September, 2018 |
| Pages | 223 |
| Subgenre | Aeronautics & Astronautics |
| Series title | No Series |
| Edition | softcover reprint of the original 1st 2017 Edition |
| Publisher | Springer |
| Original languages | English |
| Language | English |
| Edu focus | Science & Technology, Mathematics |
| Educational level | General |
| Awards won | 1st prize in the National Science and Technology Awards in 2008, 2nd prize in the National Science and Technology Awards in 2006, 4th prize in the National Science and Technology Awards of 1997, National Science Fund for Distinguished Young Scholars in 2001, Changjiang Scholars Program Achievement Award in 2008 |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.14 x 0.50 x 9.21 in (15.6 x 1.3 x 23.4 cm) |
| Assembled product weight | 0.74 lb (340 grams) |
| Bisac subject heading | Technology & Engineering |
Who Should Buy?
-
Astronomy Students
Ideal for students studying astronomy or astrophysics, providing essential techniques for star identification and analysis.
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Amateur Astronomers
Perfect for hobbyists seeking to enhance their skills in identifying stars using algorithms and methods presented.
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Data Scientists
Useful for data scientists focusing on astronomical data, offering methodologies to process and analyze star identification tasks.
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Casual Readers
Not suitable for casual readers seeking light content as it delves deeply into technical methodologies and algorithms.
產品敘述
Star Identification: Methods, Techniques and Algorithms, (Paperback)
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Guangjun Zhang All Books Editorial Review
Star Identification: Methods, Techniques and Algorithms is a comprehensive paperback book published by Springer in September 2018. This non-fiction work spans 223 pages and is recognized in the Computing & Internet genre, specifically focusing on Aeronautics and Astronautics. It features a detailed examination of star identification processes with advanced methodologies and has received multiple awards, including a 1st prize at the National Science and Technology Awards in 2008. Readers will appreciate that it's educationally geared towards Science & Technology, making it suitable for general readers interested in mathematics and engineering.
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優點
- Award-winning book with significant recognition
- Detailed research on star identification
- Perfect binding for durability
- Softcover edition for easy handling
- Approachable for general readers interested in science
缺點
- Some readers may prefer a hardcover format
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HKD 1533
現在訂購，約可在下列時間收貨： 星期三, 十一月 04
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特色和優勢
- Comprehensive overview of star identification methods over the past 10 years.
- Covers key techniques and practical algorithms in star identification.
- Includes hardware implementation examples and performance evaluations.
- Essential for celestial navigation and enhancing star sensor performance.
- Valuable for researchers, star sensor designers, and postgraduate students.
- Focuses on advanced topics like neural networks and rapid star tracking.
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