Skip to main content

A Brief Discussion of Filters

 

Low Pass Filter

In most cases, filters extract the required frequency from a signal. Low-pass filters only permit frequencies falling below and attenuating frequencies above the cutoff frequency.

Low-pass filters generally have a form where the output decays at higher frequencies.

Low-Pass Filter Transfer Function

Let’s take the following simple transfer function:

\[ H(z) = \frac{1}{1 + 0.5z^{-1}} \]

Analysis of the Transfer Function

The denominator suggests it’s a low-pass filter because it has a single pole and the gain decreases as frequency increases (in the discrete-time case, this is for higher \(\omega\)).

At low frequencies, the magnitude will be close to 1, but as the frequency increases, the magnitude will drop.

Fig: Low Pass Filter

A low pass filter's cutoff frequency is calculated as

Cut off frequency = 1 / 2*pi*R*C


High Pass Filter

All frequencies in a signal above the high pass filter's cutoff frequency can pass through the high pass filter. High-pass filters generally have a form where the output increases at higher frequencies.

High-Pass Filter Transfer Function

Now consider the following transfer function:

\[ H(z) = \frac{z^{-1}}{1 + 0.5z^{-1}} \]

Analysis of the Transfer Function

The numerator has z^{-1}, suggesting a high-pass filter because it includes a term that shifts the response at low frequencies, while passing higher frequencies more easily.

By examining the transfer function, you can classify the filter's behavior in terms of its frequency response.

Fig: High Pass Filter

A high pass filter's cutoff frequency is calculated as

Cut off frequency = 1 / 2*pi*R1*C1


Band Pass Filter

A band pass filter is a device that permits frequencies that fall within a specific frequency range. Both frequencies inside and outside of the field are attenuated. Band-pass filters will show a peak or resonance at a specific frequency.

Bandpass Filter Transfer Function

Now consider the following transfer function for a bandpass filter:

\[ H(z) = \frac{z^{-1} - z^{-2}}{1 + 0.5z^{-1} + 0.25z^{-2}} \]

Analysis of the Transfer Function

The numerator contains the terms z^{-1} and z^{-2}, suggesting that the filter allows a band of frequencies to pass while attenuating lower and higher frequencies.

The filter achieves this by having a zero at a specific frequency, which creates a notch at the desired frequency, thus passing a band of frequencies in the middle.

By examining the transfer function, you can classify the filter's behavior in terms of its frequency response. This includes its ability to pass signals within a specific frequency band and attenuate others outside that range.

Fig: Band Pass Filter
All frequencies above (1/2*pi*R1*C1) and below (1/2*pi*R2*C2) are passed by the band pass filter shown in the figure above.

Keep in mind that a bandpass filter is a combination of a high pass and a low pass filter.


Further Reading




Contact Us

Name

Email *

Message *

Popular Posts

Hybrid Beamforming | Page 1

Beamforming Techniques Hybrid Beamforming... Page 1 | Page 2 | Hybrid Beamforming: Hybrid beam formation was developed to address some of the limitations of digital pre-coding approaches. Every antenna element is connected to an RF chain in digital pre-coding (beam forming) method. We also know that each RF chain is in charge of providing a separate data stream between the transmitter and the receiver. We know that a larger number of independent data streams leads to higher data rates. It has a spatial multiplexing feature for MIMO. As a result, we may assume that switching from MIMO to massive MIMO will benefit us more in terms of spatial multiplexing in massive MIMO, where each antenna is coupled to a single RF chain. We'll proceed with a definition of hybrid beam forming. Overview of hybrid beam forming with example: Unlike digital beam forming, more than one antenna element is connected to a single RF chain in hybr...

MATLAB Code for 8-PSK, 16-PSK, ...

📘 Overview & Theory 🧮 MATLAB Code for BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK 🧮 Simulator for m-ary PSK 📚 Further Reading   MATLAB Code for BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK clc; clear all; close all; rng(10) M = 8; % M = 2, 4, 8, 16, 32, etc. N_Bits = 2520; Phase = 0; data_info_bit = randi([0,1],N_Bits,1); data_temp = bi2de(reshape(data_info_bit,N_Bits/log2(M),log2(M))); modData = pskmod(data_temp,M,Phase); figure(1); scatterplot(modData); channelAWGN = 15; rxData2 = awgn(modData, channelAWGN); figure(2); scatterplot(rxData2); demodData = pskdemod(rxData2,M,Phase);   for BPSK, Constellation Size, M = 2 for QPSK, M = 4 for 8-PSK, M = 8, and so on    Output Figure: 8-PSK Modulation Figure: 8-PSK Demodulation after adding AWGN Noise Using the above MATLAB code you'll able be to modulate and demodulate 2-PSK, 4-PSK, 8-PSK, 16-PSK, 32-PSK and so on.  16-PSK   Fig: 16-PSK In this above code ' M ' is the number of the conste...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Home / Engineering & Other Exams / UGC NET 2026 PYQ ⬇️ Download Papers and Solutions 📋 Exam Pattern 💡 Preparation Tips ❓ FAQs 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) 📥 Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Solved Paper + Explanation ...

How Windowing Affects Your Periodogram

The windowed periodogram is a widely used technique for estimating the Power Spectral Density (PSD) of a signal. It enhances the classical periodogram by mitigating spectral leakage through the application of a windowing function. This technique is essential in signal processing for accurate frequency-domain analysis.   Power Spectral Density (PSD) The PSD characterizes how the power of a signal is distributed across different frequency components. For a discrete-time signal, the PSD is defined as the Fourier Transform of the signal’s autocorrelation function: S x (f) = FT{R x (Ï„)} Here, R x (Ï„)}is the autocorrelation function. FT : Fourier Transform   Classical Periodogram The periodogram is a non-parametric PSD estimation method based on the Discrete Fourier Transform (DFT): P x (f) = \(\frac{1}{N}\) X(f) 2 Here: X(f): DFT of the signal x(n) N: Signal length However, the classical periodogram suffers from spectral leakage due to abrupt truncation of the ...

Advanced M-ary Modulation Simulator: Constellation, min dist, Efficiency, SER, EVM (RMS)

Advanced M-ary Communication Lab Analytical & Statistical Performance of Digital Modulation Theoretical Probability of Error (\(P_s\)) \[ P_s = Q\left(\sqrt{\frac{2 E_b}{N_0}}\right) \] Modulation (M-ary) BPSK (M=2) QPSK (M=4) 8-PSK (M=8) 16-QAM (M=16) 64-QAM (M=64) 256-QAM (M=256) SNR (\(E_b/N_0\)): 12 dB Efficiency 2 bps/Hz Min Dist (\(d_{min}\)) 1.41 Symbol Error 1.2e-5 EVM (RMS) 0.0% Constellation Diagram Noise PDF & Decision Tail 1. Geometric Mapping ...

Frequency Shift Keying (FSK) Modulation & Demodulation (with Simulation)

Frequency Shift Keying (FSK) Theoretical Foundations: Frequency Shift Keying (FSK) is a discrete frequency modulation scheme wherein the digital information is encoded via instantaneous shifts in the carrier signal's frequency. The fundamental implementation is Binary FSK (BFSK), which maps binary data onto two distinct, discrete spectral states. A binary '1' (the "mark" state) is represented by a carrier frequency \( f_1 \), while a binary '0' (the "space" state) corresponds to frequency \( f_2 \). Each symbol is sustained for a bit interval denoted by \( T_b \). FSK Transmitter Characterization: The mathematical model for the modulated BFSK output \( s(t) \) is defined as: \[ s(t) = \begin{cases} A_c \cos(2\pi f_1 t), & \text{for } m = 1 \\ A_c \cos(2\pi f_2 t), & \text{for } m = 0 \end{cases} \] ...

Galois Fields: GF(2) and GF(2m) and Primitive Polynomial

Galois Fields: GF(2) and GF(2 m ) 1. What is a Galois Field (GF)? A Galois Field (GF) is a finite set of elements in which the four basic arithmetic operations—addition, subtraction, multiplication, and division (except by zero)— are all well defined and closed. GF(q) ⇒ a field with exactly q elements 2. The Simplest Field: GF(2) GF(2) is the smallest possible finite field and forms the foundation of all digital systems. GF(2) = {0, 1} Addition in GF(2) Addition is performed modulo 2 (XOR operation): + 0 1 0 0 1 1 1 0 Multiplication in GF(2) × 0 1 0 0 0 1 0 1 GF(2) is used in binary logic, XOR operations, and simple error-control codes. 3. Meaning of GF(2 m ) GF(2 m ) is a finite field containing exactly 2 m elements . Each element represents an m-bit symbol . Field Number of Elements GF(2) 2 GF(2²) 4 GF(2³) 8 GF(2⁸) 256 Important: GF(2 m ) is not integer arithmetic modulo 2 m . It is polynomial-based arithmetic. 4....