Skip to main content

Beginner’s Guide to Building a Mobile App Using React Native and Expo.dev

 

This article is about how to create your first mobile app using React Native and Expo. For the development, I used the online platform expo.dev. It accelerates your work by suggesting dependencies very well (which npm packages you need to install).

React Native is a JavaScript framework that helps you to launch your mobile app, similar to how React.js is used for building web applications. Like React.js, here also you create and call different components. Expo is basically a toolset for React Native — it provides pre-configured tools, libraries, and services so you don’t need to set up everything manually. Expo helps you make it easier and faster to build mobile apps (iOS and Android).

 

Steps

  1. Go to the expo.dev website. Opening this website you will find an interface like this, which is your first/main/directory root screen App.js.

If this interface does not open, then search for and go to the ‘Snacks’ section of expo.dev, where you can create a new Snack for your mobile app development. 

2. In the file package.json, it stores the dependencies, which include different npm packages and libraries required to run the app.

3. If you want multiple screens in your app, then create another folder like screens and under this folder create different components like Sidebar.js, Layout.js, and LandingPage.js. Call these components in the App.js file as required. Similarly, you can call different components inside another component. In our case, Sidebar.js and Layout.js are called inside LandingPage.js, and LandingPage.js is then called inside the App.js screen.

 You will see the output of your code on the right side of the expo.dev interface, where both Web and Android app previews are available. You can also scan the QR code from the device section using the Expo Go mobile app to run it directly on your device.

 

What I Learned

Building this first app taught me how simple it can be to get started with React Native when using Expo. The expo.dev platform and Expo Go app made testing very smooth, while React Native components helped me structure the app easily. This gave me the confidence to explore more features and build more complex apps in the future.

 

Visit Github

Further Reading

  1.  How to Build an Offline Mobile App with React Native, Expo, and SQLite
๐Ÿ“Š MATLAB & ONLINE SIMULATIONS (Web-Based)
๐ŸŽ“ GATE & UGC - NET SOLUTIONS
๐Ÿ“ก WIRELESS & SIGNAL PROCESSING


Contact Us

Name

Email *

Message *

Popular Posts

MIMO Channel Matrix | Rank and Condition Number

MIMO / Massive MIMO MIMO Channel Matrix | Rank and Condition...   The channel matrix in wireless communication is a matrix that describes the impact of the channel on the transmitted signal. The channel matrix can be used to model the effects of the atmospheric or underwater environment on the signal, such as the absorption, reflection or scattering of the signal by surrounding objects. When addressing multi-antenna communication, the term "channel matrix" is used. Let's assume that only one TX and one RX are in communication and there's no surrounding object. Here, in our case, we can apply the proper threshold condition to a received signal and get the original transmitted signal at the RX side. However, in real-world situations, we see signal path blockage, reflections, etc.,  (NLOS paths [↗]) more frequently. The obstruction is typically caused by building walls, etc. Multi-antenna communication was introduced to address this issue. It makes diversity app...
Read more

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

Bit Error Rate (BER) & SNR Guide Analyze communication system performance with our interactive simulators and MATLAB tools. ๐Ÿ“˜ Theory ๐Ÿงฎ Simulators ๐Ÿ’ป MATLAB Code ๐Ÿ“š Resources BER Definition SNR Formula BER Calculator MATLAB Comparison ๐Ÿ“‚ Explore M-ary QAM, PSK, and QPSK Topics ▼ ๐Ÿงฎ Constellation Simulator: M-ary QAM ๐Ÿงฎ Constellation Simulator: M-ary PSK ๐Ÿงฎ BER calculation for ASK, FSK, and PSK ๐Ÿงฎ Approaches to BER vs SNR Calculation What is Bit Error Rate (BER)? The BER indicates how many corrupted bits are received compared to the total number of bits sent. It is the primary figur...
Read more

UGC NET Electronic Science Previous Year Question Papers with Solutions

Home / Engineering & Other Exams / UGC NET 2026 PYQ ⬇️ Download Papers and Solutions ๐Ÿ“‹ Exam Pattern ๐Ÿ’ก Preparation Tips ❓ FAQs ๐Ÿ“Š 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) ๐Ÿ“ฅ Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading ๐Ÿ“‚ View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Solved Paper + Explanation ...
Read more

How to Mount Google Drive in Google Colab

How to Mount Google Drive in Google Colab Google Colab provides temporary storage during a session. Any files stored in the /content directory will be deleted when the runtime disconnects. To store datasets, trained models, and results permanently, it is recommended to mount your Google Drive in Colab. Mounting Google Drive allows your notebook to access files directly from your Drive and save outputs there so they remain available even after the Colab session ends. Step 1: Import the Drive Module First import the Google Colab drive module. from google.colab import drive Step 2: Mount Google Drive Run the following command to mount your Google Drive. from google.colab import drive drive.mount('/content/drive') After running the command: A link will appear in the output. Click the link and log in to your Google account. Copy the authentication code provided. Paste the code back into the notebook. Or, a Google authentication page will a...
Read more

MATLAB Code for OTFS (Orthogonal Time Frequency Space)

MATLAB Code for OTFS (Orthogonal Time Frequency Space) %% Clear workspace clc; clear; close all ; %% Step 1: OTFS Parameters N_delay = 4; % Number of delay bins (rows) N_doppler = 4; % Number of Doppler bins (columns) N_sym = N_delay * N_doppler; modOrder = 4; % QPSK SNR_dB = 20; % Noise level %% Step 2: Generate random data symbols data = randi([0 modOrder-1], N_sym, 1); txSymbols = pskmod(data, modOrder, pi/4); disp( 'Transmitted Delay-Doppler symbols:' ); disp(reshape(txSymbols, N_delay, N_doppler)); %% Step 3: Map Delay-Doppler → Time-Frequency (ISFFT) % ISFFT: Inverse Symplectic Finite Fourier Transform % 1. Take IDFT along Doppler (columns) % 2. Take DFT along Delay (rows) ddSymbols = reshape(txSymbols, N_delay, N_doppler); % Step 3a: IDFT along columns (Doppler) tfGrid = ifft(ddSymbols, N_doppler, 2); %IFFT (accross columns) along Doppler → spreads in time (Delay → Time) %FFT (accross rows)along Delay → spreads in frequency (Delay → Frequency) % Step 3b: DFT along ...
Read more

Wiener Filter in MATLAB

  MATLAB Code  % Wiener Filter Based on Wiener-Hopf Equation % This script demonstrates how to apply the Wiener filter to recover % a reference signal from a noisy signal using the Wiener-Hopf equation. % The filter minimizes the mean squared error between the noisy signal and the reference signal. clear; close all; clc; % Signal Parameters fs = 4000; % Sampling frequency (Hz) T = 1; % Total recording time (seconds) L = T * fs; % Signal length (samples) tt = (0:L-1) / fs; % Time vector ff = (0:L-1) * fs / L; % Frequency vector % Generate Reference Signal (a sinusoid) y = sin(2 * pi * 120 * tt); % Reference sinusoidal signal y = y(:); % Ensure column vector % Create Noisy Signal by Adding Gaussian Noise x = 0.50 * randn(L, 1) + y; % Noisy signal x = x(:); % Ensure column vector % Define Filter Order (Number of Coefficients) N = 200; % Apply Wiener Filter using custom function [xest, b, MSE] = wienerFilt(x, y, N); % Plot Results figure; subplot(411); plot(tt, x, 'k'), hold on, p...
Read more

Overmodulation & Distortion in AM

Overmodulation in AM and How It Causes Distortion 1. AM Signal Equation s(t) = A c [1 + ฮผ m(t)] cos(2ฯ€ f c t) A c = carrier amplitude m(t) = normalized modulating signal (|m(t)| ≤ 1) ฮผ = modulation index 2. Modulation Index ฮผ = A m / A c - Normal AM: 0 < ฮผ ≤ 1 → no distortion - Overmodulation: ฮผ > 1 → distortion occurs 3. Envelope and Overmodulation A(t) = A c [1 + ฮผ m(t)] - For undistorted AM: 1 + ฮผ m(t) ≥ 0 at all times - If ฮผ > 1: 1 + ฮผ m(t) < 0 at negative peaks → carrier flips Example: Let m(t) = cos(2ฯ€ f m t), A c = 1 V, ฮผ = 1.2 Minimum envelope: A min = A c [1 - 1.2] = -0.2 V Negative amplitude → envelope crosses zero → 180° phase flip 4. Mathematical Consequence -A c cos(ฮธ) = A c cos(ฮธ + ฯ€) This phase reversal is what causes distortion in the demodulated signal. 5. Instantaneous AM Signal s...
Read more

Online Simulator for ASK, FSK, and PSK Signal Generation

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. ๐Ÿ“ก ASK Simulator ๐Ÿ“ถ FSK Simulator ๐ŸŽš️ BPSK Simulator ๐Ÿ“š More Topics ASK Modulator FSK Modulator BPSK Modulator More Topics 1. ASK (Amplitude Shift Keying) Simulat...
Read more