Ever wondered how apps separate overlapping voices in a recording or extract hidden signals from noisy data? That’s where Independent Component Analysis (ICA) comes in. ICA is a statistical technique for decomposing mixed signals into their independent sources. Unlike PCA, which only decorrelates data, ICA assumes independence among sources, making it ideal for real-world signals like: 🎧 Audio recordings (separating speakers) 🧠 EEG/MEG brain signals (isolating neural patterns) 📈 Financial data (finding hidden market factors) How it works (visualized below): 1️⃣ Start with mixed signals (x₁, x₂, x₃). 2️⃣ Apply ICA — the algorithm identifies independent components. 3️⃣ Result: Separated independent signals (s₁, s₂, s₃). A Sample Code (in Python) : # Import necessary libraries import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import FastICA # Step 1: Generate sample signals np.random.seed(42) n_samples = 2000 time = np.linspace(0, 8, n_samples...
1. In an era of internet vigilantism, would there be any impact on a fair trial due to interference of social media and public platforms ? Ans. It depends on many factors. Social media can create public opinion based on half truths or misinformation, which can pressurize a judge to interpret evidence especially in a 50-50% chance case, in tune with the public opinion. A wavering judge may align his/her decision in favor of public opinion, lest he/she should be adversely criticized. But a trained judicial mind will not be influenced by external factors, but will be guided by the proof appearing from the evidence adduced in the case under trial. He/she will not succumb to the pressure exerted by social media. Similar is the case of prosecutors and investigators. Social media can easily affect a layman witness. It can affect the privacy of vulnerable victims also. Thus trial by media is a social evil. 2. With the rise of digital tools, how has the use of technology like digit...