دانلود کتاب رویکرد مهندسی داده به تحلیل پراکندگی موج با کاربردهایی در رادار، سونار، تشخیص پزشکی، تشخیص نقص ساختاری و رباتیک هوشمند

In this book, scattering analysis is applied tomany seemingly different things, including but not in any way limited to: ● Radar: The atomic bomb may have ended WWII, but radar won it. Radar kept Britain in the war long enough for the arsenal of democracy to get fully into the game. After the Cold War ended, radar-scattering folks ended up making video games realistic and cell phones reliable. ● Sonar: For decades, humans have been hard at work trying to duplicate the innate abilities of echolocating mammals like dolphins, whales, and bats. Acoustic scattering is more than just a scalar version of electromagnetics. ● Medical diagnostics: Ultrasound images, mammograms, etc. are 2D “cuts” of threedimensional anatomy. Doctors are expert at interpreting them, but the diagnosis is still quite subjective. Machine learning can assist doctors by highlighting suspicious features in signals. ● Structural flaw detection: Technicians are not as highly trained at diagnosis as are doctors, plus there is no standard “anatomy” and the structure can’t tell where it hurts. For many applications, machine learning can take the lead by automatically identifying flaws that could lead to structural failure. ● On-line inspection: Process engineers don’t want to interpret images. They want the instrumentation to give a green light if the process is OK, and a red light if it’s out of spec. Automatic, real-time interpretation of complex process-monitoring signals is now doable. ● Intelligent robotics: The key to useful robots is a combination of imaging sensors and the on-board intelligence to interpret them in real time. You want to simply tell the robot to turn left at the big tree, not feed it GPS coordinates. The modeling techniques, and applications of them, we’ll be discussing allow one to implement new and bettermeasurements with both novel instrumentation and artificial intelligence that automates the interpretation of the various (and multiple) imaging data streams. There’s rather a lot of high-level math, of course, but that’s good for us because we’re not the sort of laymen who Einstein said “have a secret grudge against arithmetic.” The underlying mathematical and computational methodswe’ll discuss transcend any particular application(s). If you can do radar, you can do sonar, seismology, nondestructive evaluation, and so on. That turns out to be pretty important because lifequakes come along about every six to eight years, and any particular thing that you happen to be expert at might go fromhot to not in an instant. That has happened to me repeatedly since 1986 and so all these years later, I’ve accumulated a seemingly diverse set of subject matter expertise with a mindset of always being on the lookout for new applications of what I know and can do. It has recently come to my attention that the current cohort of graduate students are Gen Z, who did at least some college via Zoom and are fundamentally different from the generations that came before them. They’ve never not had all the world’s knowledge in a portable, semidisposable device that they carry with them at all times. One goal of this book is to help connect this generation with the vast scientific literature that has long existed in musty libraries but now is available for download at places like Internet Archive, but only if you know what sources to seek out and use as the basis for your personal reference library. It’s also important to pay homage to those scientists and mathematicians who spent their professional lives developing tools that you can now use to solve problems. Tea will be spilled; the index is designed to allow a quick Hollywood read. This book is based on a multisemester sequence of graduate classes that I’ve been giving for three decades now. I started by typing up my own hand-written notes, and as I was going along, many of my students made plots for this book as exercises in class. Thanks for that, BTW. I’ve also drawn examples whenever possible from the dissertation research of my graduate students, with discussion of the real-world problems that motivated their research. I’ve deliberately included anecdotes of the sorts of issues that can arise in collaborative research that includes multiple investigators and multiple institutions. Much of our work is done in close collaboration with small companies, several of whom I’ve shepherded from de novo startups on through VC investment or M&A, but I’ve always avoided taking any equity stake so I’d be at arm’s length and could focus on doing what’s best for my students’ career progression. This isn’t intended to be a comprehensive reference work on scattering for radar, sonar, etc. Indeed, I’ve deliberately downplayed techniques that aren’t all that important now that ubiquitous computing of sufficient power means we can simulate realistic 3D scattering scenarios. I do try to point the reader to classic texts where those mature areas of research are discussed in great detail by those notables who developed the methods, doing the best they could with the computational power they had available. Once upon a time, of course, computer was a job title.