This m file models a DPSK UWB system using a delay in one leg of the mixer, correlation receiver low pass filter combination requiring no template for synching. Various waveforms are displayed throughout the system to allow the user to observe operation of the system.
he LPC932 can be used to create a Pulse Width Modulated PWM signal. That s an analog signal, with only 2 discrete levels, for example 0V and 5V and a constant period. The current value of this signal at a certain poiTnt of time is proportional to its Duty Cycle. That s the High Time during one period divided by the period. It can also be calculated as the average value during a particular period. That means after low pass filtering, (e.g. RC circuit) the signal becomes analog, with an actual value controlled by the microcontroller. The PWM functionality enables the LPC932 to control for example the speed of DC motors or the brightness of electric lighting.
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This m file analyzes a coherent binary phase shift keyed(BPSK) and a amplitude shift keyed(ASK) communication system. The receiver uses a correlator(mixer-integrator[LPF]) configuration with BER measurements comparing measured and theoretical results. The bandpass and low pass used in the receiver are constructed using z transforms.
The purpose of this lab is to introduce the concept of FSMs with a datapath, and to
study the usage of more complex test benches. Also, we enforce a rudimentary design
methodology by assuming that the students are part of a bigger project, and have no
knowledge of VHDL-implementation of the datapath (made by a hypothetical other
group) other than its predefined Entity Interface until they come to the lab.
The rest of this document is structured as follows: Section 2 describes some prelimi-
nary reading and exercises that should be done before the lab. Section 3 details the
design tasks that should be carried out to pass this lab.
How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
