this program deals with homomorphic analysis of speech. it was homework assignment in digital speech processing course. speech is synthesized using homomorphic filter.
The target of the assignment is to familiarize the student with MIMO channel modeling.
The work is based on L. Schumacher’s MIMO channel model implementation, with
added capacity analysis. First the channel model implementation is introduced, and
thereafter analysis on MIMO channel with different parameters is done. Finally a short
report on the results is written.
The task in this assignment is to implement an airline routing system. Your
system should be able to read in a
ight network as a graph from a le, where
airports are represented as vertices and
ights between airports are represented
as edges, take as input two airports and calculate the shortest route (ie path)
between them.
a non-sharing smart pointer class that can be used with STL containers such as std::map, vector, list, set, and deque. The smart pointer has an assignment operator and greater than operator that call the target object s operator.
一種基于二維鏈表的稀疏矩陣模半板類設計
A template Class of sparse matrix.
Key technology: bin,2-m linked matrix.
constructors: 1.normal constuctor 2.copy constuctor. 3.assignment constructor.
Basic operator: 1. addition(sub) of two matrix
2. inverse of a matrix.
3. multiply of two matrix.
etc.
SVMhmm: Learns a hidden Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag sequences for new sentences.
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.
This the OXO example code plus the presentation. It is intended to provide you with some clues about the structure and classes you may need for your second assignment. I have also included the jar file.
The acceptance and introduction of serial communication to more and more
applications has led to requirements that the assignment of message identifiers to
communication functions be standardized for certain applications. These applications
can be realized with CAN more comfortably, if the address range that originally has
been defined by 11 identifier bits is enlarged
