Decoding most of the infrared signals can be easily
handled by PIC16C5X microcontrollers. This application
note describes how this decoding may be done.
The only mandatory hardware for decoding IR signals
is an infrared receiver. The use of two types is
described here. Both are modular types used often by
the consumer electronics industry. The first type
responds to infrared signals modulated at about
40 kHz. The second responds to non-modulated infrared
pulses and has a restricted range. The hardware
costs of each approach will be less than two dollars.
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(shè)計(jì)
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.
his packet is a IS-95 baseband simulation for 1 data channel of 9.6 KBps rate.
The simulation is written for static channel and AWGN noise. The packet include:
1) Packet Builder (Viterbi Encoding, Interleaver, PN generation)
2) Modulator (RRC filter)
3) Demodulator (Matched Filter, RAKE receiver)
4) Receiver (HD or SD) (Deinterleaver, Viterbi Decoder).
You should run "Simulation.m" function that include all modules.
This code was used for making the practical measurements in section 2.3 of my thesis. This Matlab code allows an OFDM signal to be generated based on an input data file. The data can be random data, a grey scale image, a wave file, or any type of file. The generated OFDM signal is stored as a windows wave file, allowing it to be viewed, listened to and manipulated in other programs. The modified wave file can then be decoded by the receiver software to extract the original data. This code was developed for the experiments that I performed in my honours thesis, and thus has not been fully debugged.
This is the original code developed for the thesis and so has several problems with it. The BER performance given by the simulations is infact Symbol Error Rate.
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.
What Does the code DO? Sometimes we may desire to hide our file contents from others.One of the possible way is encrypting these files.Here a simple encryption technique is used(In VB - The same technique can be implemented in "c" also.) Program flow Explained * Open the File to be encrypted for Binary Access Read(Say Source File) * Open a temparory file where encrypted data is stored for Binary Access Write(Say Destination File) * Loop through the Source File Byte by Byte * For each byte read from the file, Complement the data. (Using Not operator (in C we have to use "~" operator) * Write Complemented Data to Destination File * Delete the Source File * Rename Destination file as Source File(Now Encryption is over)
Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
nonlinear estimation problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation problem.
Examples are given to show the use and some advantages of this scheme.
This paper investigates the design of joint frequency
offset and carrier phase estimation of a multi-frequency time division
multiple access (MF-TDMA) demodulator that is applied to
a digital video broadcasting—return channel system via satellite
(DVB-RCS). The proposed joint estimation algorithm is based on
the interpolation technique for two correlation values in the frequency
and phase domains. This simple interpolation technique
can significantly improve frequency and phase resolution capabilities
of the proposed technique without increasing the number of
the correlation values. In addition, the overall block diagram of a
digital communications receiver for DVB-RCS is presented, which
was designed using the proposed estimation algorithms.
Index Terms—Carrier phase estimation, DVB-RCS, frequency
offset estimation, interpolation, joint estimation, MF-TDMA.
