Embedding Sensors and Motors Specialization

Start Date: 06/16/2019

Course Type: Specialization Course

Course Link: https://www.coursera.org/specializations/embedding-sensors-motors

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About Course

Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.

Course Syllabus

Sensors and Sensor Circuit Design
Motors and Motor Control Circuits
Pressure, Force, Motion, and Humidity Sensors
Sensors and PID Control

Deep Learning Specialization on Coursera

Course Introduction

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Course Tag

sensor integration firmware motor design embedded engineering hardware

Related Wiki Topic

Article Example
Embedding Equivalently, an isometric embedding (immersion) is a smooth embedding (immersion) which preserves length of curves (cf. Nash embedding theorem).
Embedding An embedding can also refer to an embedding functor.
Embedding In other words, an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold. An immersion is a local embedding (i.e. for any point formula_22 there is a neighborhood formula_23 such that formula_24 is an embedding.)
Embedding In general topology, an embedding is a homeomorphism onto its image. More explicitly, an injective continuous map formula_2 between topological spaces formula_3 and formula_4 is a topological embedding if formula_5 yields a homeomorphism between formula_3 and formula_7 (where formula_7 carries the subspace topology inherited from formula_4). Intuitively then, the embedding formula_2 lets us treat formula_3 as a subspace of formula_4. Every embedding is injective and continuous. Every map that is injective, continuous and either open or closed is an embedding; however there are also embeddings which are neither open nor closed. The latter happens if the image formula_7 is neither an open set nor a closed set in formula_4.
Embedding Let formula_18 and formula_19 be smooth manifolds and formula_20 be a smooth map. Then formula_5 is called an immersion if its derivative is everywhere injective. An embedding, or a smooth embedding, is defined to be an injective immersion which is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image).
Embedding A mapping formula_59 of metric spaces is called an "embedding"
Embedding An isometric embedding is a smooth embedding "f" : "M" → "N" which preserves the metric in the sense that "g" is equal to the pullback of "h" by "f", i.e. "g" = "f"*"h". Explicitly, for any two tangent vectors
Embedding In general, for an algebraic category "C", an embedding between two "C"-algebraic structures "X" and "Y" is a "C"-morphism "e:X→Y" which is injective.
Graph embedding the vertices and edges of formula_1 is a family of regions (or faces). A 2-cell embedding or map is an embedding in which every face is homeomorphic to an open disk. A closed 2-cell embedding is an embedding in which the closure of every face is homeomorphic to a closed disk.
Center embedding Center embedding is the focus of a science fiction novel, Ian Watson's "The Embedding", and plays a part in Ted Chiang's "Story of Your Life".
Greedy embedding For more general graphs, some greedy embedding algorithms such as the one by Kleinberg start by finding a spanning tree of the given graph, and then construct a greedy embedding of the spanning tree. The result is necessarily also a greedy embedding of the whole graph. However, there exist graphs that have a greedy embedding in the Euclidean plane but for which no spanning tree has a greedy embedding.
Digital sensors Sensors used to be of analogue type, but today more and more digital sensors are used. This article describes the difference and the reason for the development of digital sensors.
Robotic sensors Sensors provide analogs to human senses and can monitor other phenomena for which humans lack explicit sensors.
Center embedding Embedding on its own refers to all types of clauses occurring as subordinate parts of a superordinate clause. There are three types of sub-clauses: complement, relative, and adverbial. Subordinators or relative pronouns indicate which sub clause is being used. Center embedding (abbreviated "C" or "c") contains words of the superordinate clause on the left and the right of the sub-clauses. Multiple center embedding of the same type of clause is called self-embedding.
Font embedding Font embedding has been possible with Portable Document Format (PDF), Microsoft Word for Windows and some other applications for many years. LibreOffice has supported font embedding since version 4.1 in its Writer, Calc and Impress applications.
Embedding An important case is formula_25. The interest here is in how large formula_26 must be for an embedding, in terms of the dimension formula_27 of formula_18. The Whitney embedding theorem states that formula_29 is enough, and is the best possible linear bound. For example the real projective space RP of dimension formula_27, where formula_27 is a power of two, requires formula_29 for an embedding. However, this does not apply to immersions; for instance, RP can be immersed in formula_33 as is explicitly shown by Boy's surface—which has self-intersections. The Roman surface fails to be an immersion as it contains cross-caps.
Embedding When the domain manifold is compact, the notion of a smooth embedding is equivalent to that of an injective immersion.
Embedding In field theory, an embedding of a field "E" in a field "F" is a ring homomorphism .
Embedding In order theory, an embedding of partial orders is a function F from X to Y such that:
Digital sensors Digital sensors are the modern successors of analog sensors. Digital sensors replace analog sensors stepwise, because they overcome the traditional drawbacks of analog sensor systems (cf chapter 3)