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Professor Jennifer Hasler's Circuit Lectures
เข้าร่วมเมื่อ 4 พ.ค. 2020
This channel has a number of circuit lectures and related topics created and recorded by Dr. Jennifer Hasler.
A Second-Order HPF built with a single Op-Amp Gyrator Approximation
This video discusses a single Op-Amp Gyrator approximation circuit that yields approximately a series resistor and inductor. Using this circuit, the video discusses a second-Order HPF built with this core block.
มุมมอง: 198
วีดีโอ
A Second-Order LPF using two Op-Amps
มุมมอง 2282 หลายเดือนก่อน
This video shows an op-amp circuit version of a second-order Low-Pass Filter (LPF) with two op-amps based upon the Gm-C Diff2 stage which derived from a Tau-Thomas op-amp topology. The first stage provides a signal difference enabling the circuit to operate with two op-amps. The circuit with all the same Resistor (R) values uses two capacitors (C1 & C2) to tune both the center frequency (fc) an...
Time Response and Numerical Calculation of a Second-Order Linear Circuit
มุมมอง 1632 หลายเดือนก่อน
This video discusses the time response for two Second Order Linear Circuits with a Resistor (R), Capacitor (C) and Inductor (L). One circuit is a Low-Pass Filter, and one circuit is a High-Pass Filter. The video also discusses how one sets up the numerics to solve these equations as well as what constant and initial conditions are required for those calculations.
Magnitude Response of a Second-Order Linear Circuit
มุมมอง 692 หลายเดือนก่อน
This video discusses the magnitude responses for three Second Order Linear Circuits with a Resistor (R), Capacitor (C) and Inductor (L). One circuit is a Low-Pass Filter (LPF), one circuit is a Bandpass Filter (BPF), and one circuit is a High-Pass Filter (HPF). The LPF and HPF are similar responses, effectively mirrors around the center frequency (fc).
Phase Response of a Second-Order Linear Circuit
มุมมอง 482 หลายเดือนก่อน
This video discusses the Phase responses for three Second Order Linear Circuits with a Resistor (R), Capacitor (C) and Inductor (L). One circuit is a Low-Pass Filter (LPF), one circuit is a Bandpass Filter (BPF), and one circuit is a High-Pass Filter (HPF). The phase plots are similar for all three cases, but they start and end at different phases.
Multiple First Order Circuits
มุมมอง 3113 หลายเดือนก่อน
This video discusses multiple core first-order circuits, the core ODEs & Laplace results, and the resulting frequency responses for these circuits. This video is a good review over the range of first-order circuit concepts.
Capacitive Feedthrough for an Amplifier
มุมมอง 2033 หลายเดือนก่อน
This video discusses the effect of capacitive coupling in an amplifier circuit. This video also is a good example of analyzing a first-order capacitive circuit for the different starting and ending conditions.
Mesh & Node Solutions with Resistors and Independent Sources
มุมมอง 2534 หลายเดือนก่อน
This video describes setting up and solving two linear circuits with Independent Sources using a Node and a Mesh solution.
Thevenin--Norton Circuits with Resistors & Independent Sources
มุมมอง 3004 หลายเดือนก่อน
This video discusses three circuits reduces circuits to a resulting Thevenin equivalent circuit sometimes using Thevenin-Norton transformations for this solution.
Thevenin--Norton Equivalent Circuits with Dependent Sources
มุมมอง 4854 หลายเดือนก่อน
This video shows three examples of reducing linear circuits with resistors, dependent sources, and independent sources to a Thevenin Norton normal circuit form.
A MOSFET with Resistive Biasing as a Dependent Source Linear Circuit Opportunity
มุมมอง 1.1K4 หลายเดือนก่อน
This video discusses a typical MOSFET linear amplifier that is biased using resistors, typical of a discrete circuit implementation. The transistor can be linearized to two voltage-controlled current sources and a resistor, transforming the original circuit into a linear circuit with dependent sources to solve. The intuition for both circuits, as well as one way to solve the circuit, is to tran...
Oja's Unsupervised Learning Rule
มุมมอง 1.2Kปีที่แล้ว
This video discusses Oja's unsupervised learning rule for a single node that provides a normalization from Hebb's basic learning rule such that the weight vector (w) of the single node converges to the normalized eigenvector associated with the largest eigenvalue of the input covariance matrix (x).
Basics of Unsupervised Learning from Hebb's rule
มุมมอง 329ปีที่แล้ว
This video discusses the basics of Hebb's rule, typically considered the simplest of the unsupervised learning rules. The video shows the learning rule dynamics are unstable, although a normalized form of these dynamics could result in eigen-solutions of the input covarience matrix, including Principle Component Analysis (PCA).
Adaptive Filter Basics for a Vector of Output Nodes
มุมมอง 98ปีที่แล้ว
This video works through the fundamental Adaptive Filter math for a vector output node (vector of inputs, x, and matrix of Weights, W) that includes the statistical formulation as well as the projection and analysis for the eigenvectors of the covarience matrix (of x).
Adaptive Filter Basics for a Single Output
มุมมอง 230ปีที่แล้ว
This video works through the fundamental Adaptive Filter math for a single output node (vector of inputs, x, and Weights, W) that includes the statistical formulation as well as the projection and analysis for the eigenvectors of the covarience matrix (of x).
Biological Channels and Transistor Channels
มุมมอง 155ปีที่แล้ว
Biological Channels and Transistor Channels
Electrical Fundamentals of Neuron Membrane
มุมมอง 126ปีที่แล้ว
Electrical Fundamentals of Neuron Membrane
Introducing the connections between ML / NN & Bio Neurons
มุมมอง 278ปีที่แล้ว
Introducing the connections between ML / NN & Bio Neurons
Hybrid Acoustic Noise Cancellation Introduction
มุมมอง 1812 ปีที่แล้ว
Hybrid Acoustic Noise Cancellation Introduction
Feedback Acoustic Noise Cancellation Introduction
มุมมอง 5222 ปีที่แล้ว
Feedback Acoustic Noise Cancellation Introduction
Feedforward Acoustic Noise Cancelation Introduction
มุมมอง 5152 ปีที่แล้ว
Feedforward Acoustic Noise Cancelation Introduction
Hey Watson, copyread.squarespace.colloquium
This is great
Simple and perfect!
Madam can a lqr controller track a sinusoidal signal?
An interesting overview -- thank you!
Hello Professor Hasler, At 5:10, you say "this is a rank of one". I thought that observability matrix was rank of four. What was meant by rank of one? Did you mean rank of four? Best regards, - Dominique Deveaux
Oh wow! I've got a BUNCH of YOURS to catch up on!! I'm totes familiar with these as circuit elements, i watch these to try n get my head round the Laplace stuff :)
90397 VonRueden Square
It would be amazing if you could create playlists of your videos
Esmeralda Avenue
Toy Crossing
Titus Knoll
Clovis Extension
WHY DID THE CEILING BIRD SING
Hey Watson, copyread.squarespace.colloquium
Lesch Islands
Jodie Gardens
Greenfelder Harbors
Ward Roads
I dont understand this, I thought shot noise 2qI is related to "junctions " or depletion regions. In MOSFET channel in strong inversion/saturation you dont have this?
Alessandra Shoal
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Lopez Jeffrey Williams Ronald Miller Robert
That's what I've been thinking for a while.
r u trans?
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Romaguera Street
Fantastic. Absolutely fascinating preview. Very forward looking. There is a growing need to implement analog versions of LLMs in inference mode. Useful intelligence emerges in 70 billion parameter and larger models of LLMs after weeks and months of training on thousands of GPUs. So then it remains beyond the reach of an individual to train their own LLMs. But fine-tuning is feasible. And so then I am very interested in how an LLM (with all of it's digital MLPs (multi-layer perceptrons) and attention mechanisms) can then be mapped to analog multipliers, adders, or more advanced neuromorphic solutions. I see your work as foundational in this area. My comment is wordy and rambling, but I will come back and refine it as I continue to learn more.
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Can anyone let me know where to buy this board?
can you pls share all the videos on the basics of floating gate transistor
*Chirp*
Good explanation
Thank you for this 4 minute video! I was struggling to understand what the motivation was for this topic.
sorry for the late comment, but how is the time constant calculated here to be 0.16 microseconds?
Is there anyone producing those FPAA on par with those you use at Georgia tech or would I basically have to build the chip myself if I want to learn how to use those?
Hi, Professor Hasler, thank you so much for your amazing and clear teaching on the concept of Euler formula! One thing: I think in the cos(theta)*sin(theta), as well as the sin(theta)^3 cases, the sign may have gotten flipped. I think they should be +sin(2*theta)/2 and 3sin(theta)/4 - sin(3theta)/4, respectively. Could you please double-check?
You seem very knowledgeable in the field. Cool video!
I came on a recommendation to your channel. Unfortunately, I don't see your playlists. It is not clear in what order to study the material. What hardware do you need to buy to study? Although everything looks very interesting. Thank you in advance for your feedback.