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Image Resizer Tool Online

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Instructions for Using the Image Resizer Tool

Upload an Image:
Click the Choose File button to upload the image you want to resize. Select an image file from your device. Supported formats typically include JPEG, PNG, and GIF.
Set the Desired Dimensions:
In the Width field, enter the desired width for your resized image. The default value is set to 1200 pixels. In the Height field, enter the desired height for your resized image. The default value is set to 768 pixels. You can change these values according to your needs.
Specify Image Quality:
In the Quality field, enter a value between 0.0 and 1.0 (e.g., 0.8) to specify the quality of the resized image. The default value is set to 0.8. A lower value will reduce file size but also reduce image quality.
Submit the Changes:
After setting your desired dimensions and quality, click the Submit button to process the image. The resized image will be displayed below the button once processing is complete.
Check the Image Size:
After submission, the tool will calculate and display the size of the resized image in kilobytes (KB) below the displayed image.
Download the Resized Image:
Click the Download button to save the resized image to your device. The downloaded file will have the same name as the original image, but with a .png extension.
Repeat as Necessary:
You can repeat the process with different images or different settings as needed. Simply upload a new image and adjust the dimensions and quality as desired.

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Admin & Author: Salim

  Website: www.salimwireless.com
  Interests: Signal Processing, Telecommunication, 5G Technology, Present & Future Wireless Technologies, Digital Signal Processing, Computer Networks, Millimeter Wave Band Channel, Web Development
  Seeking an opportunity in the Teaching or Electronics & Telecommunication domains.
  Possess M.Tech in Electronic Communication Systems.

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Rayleigh vs Rician Fading

In Rayleigh fading, the channel coefficients tend to have a Rayleigh distribution, which is characterized by a random phase and magnitude with an exponential distribution. This means the magnitude of the channel coefficient follows an exponential distribution with a mean of 1. In Rician fading, there is a dominant line-of-sight component in addition to the scattered components. The channel coefficients in Rician fading can indeed tend towards 1, especially when the line-of-sight component is strong. When the line-of-sight component dominates, the Rician fading channel behaves more deterministically, and the channel coefficients may tend towards the value of the line-of-sight component, which could be close to 1. MATLAB Script clc; clear all; close all; % Define parameters numSamples = 1000; % Number of samples K_factor = 5; % K-factor for Rician fading SNR_dB = 20; % Signal-to-noise ratio (in dB) % Generate complex Gaussian random variable for Rayleigh fading channel h_rayleigh = (

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ... MATLAB Script for BER vs. SNR for M-QAM, M-PSK, QPSk, BPSK %Written by Salim Wireless %Visit www.salimwireless.com for study materials on wireless communication %or, if you want to learn how to code in MATLAB clc; clear; close all; % Parameters num_symbols = 1e5; % Number of symbols snr_db = -20:2:20; % Range of SNR values in dB % PSK orders to be tested psk_orders = [2, 4, 8, 16, 32]; % QAM orders to be tested qam_orders = [4, 16, 64, 256]; % Initialize BER arrays ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); % BER calculation for each PSK order and SNR value for i = 1:length(psk_orders) psk_order = psk_orders(i); for j = 1:length(snr_db) % Generate random symbols data_symbols = randi([0, psk_order-1]

Comparisons among ASK, PSK, and FSK | And the definitions of each

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BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

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Constellation Diagrams of ASK, PSK, and FSK

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Channel Impulse Response (CIR)

Channel Impulse Response (CIR) Wireless Signal Processing CIR, Doppler Shift & Gaussian Random Variable The Channel Impulse Response (CIR) is a concept primarily used in the field of telecommunications and signal processing. It provides information about how a communication channel responds to an impulse signal. What is the Channel Impulse Response (CIR) ? It describes the behavior of a communication channel in response to an impulse signal. In signal processing, an impulse signal has zero amplitude at all other times and amplitude ∞ at time 0 for the signal. Using a Dirac Delta function, we can approximate this. ...(i) δ( t) now has a very intriguing characteristic. The answer is 1 when the Fourier Transform of δ( t) is calculated. As a result, all frequencies are responded to equally by δ (t). This is crucial since we never know which frequencies a system will affect when examining an unidentified one. Since it can test the system for all freq

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