1. 3D-FDTD Analysis of Rectangular Printed Monopole Antenna for UWB
Advanced
Toolbox: Antenna Toolbox, PDE Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Finite-Difference Time-Domain (3D-FDTD) electromagnetic simulation of rectangular planar monopole antennas covering the 3.1 GHz to 10.6 GHz ultra-wideband spectrum. Compute Yee grid time-domain electromagnetic fields, S11 return loss, and impedance bandwidth for UWB microstrip-fed antennas.
⚙️ Key MATLAB Functions:
designsparameterspdepemeshrfplot
📊 Expected Output & Metrics: VSWR < 2 across 3.1–10.6 GHz, 3D radiation gain patterns (>3 dBi), and 3D Yee grid E-field transient movie.
p = monopoleCustom;
p.Height = 15e-3; p.Width = 12e-3;
p.GroundPlaneLength = 20e-3; p.GroundPlaneWidth = 30e-3;
freq = linspace(3.1e9, 10.6e9, 100);
s = sparameters(p, freq);
figure; rfplot(s); title('UWB Return Loss S11 (dB)');
grid on; yline(-10, '--r', 'VSWR 2:1 Threshold');
Est. Duration: 3–5 Weeks
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2. Reflectarray Antenna Optimization for Ground Target Monitoring
Advanced
Toolbox: Antenna Toolbox, Optimization Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Design and element reflection phase S-curve optimization of sectorial reflectarray antennas for passive satellite illuminator radar monitoring across DVB-S bands. Synthesize non-uniform unit-cell element phasing to achieve narrow sectorial high-gain radiation patterns.
⚙️ Key MATLAB Functions:
reflectarraypatterngafminconoptimoptions
📊 Expected Output & Metrics: Peak gain > 19 dBi across 10.7–12.75 GHz, side-lobe level (SLL) < -18 dB, and element phase mask curves.
f0 = 12e9; c = physconst('LightSpeed'); lambda = c/f0;
ra = reflectarray;
ra.Element = patchMicrostrip('Length', lambda/2, 'Width', lambda/2);
ra.GroundPlaneLength = 12*lambda; ra.GroundPlaneWidth = 12*lambda;
opts = optimoptions('fmincon', 'Display', 'iter', 'Algorithm', 'sqp');
pattern(ra, f0); title('Optimized Reflectarray 3D Far-Field Gain');
Est. Duration: 3–4 Weeks
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3. Gain & Radiation Pattern Characterization of Dipole Antennas
Beginner
Toolbox: Antenna Toolbox, RF Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Analytical and numerical electromagnetic modeling of short, half-wave, and full-wave dipole antennas evaluating directivity, radiation resistance, and gain. Compute E-plane and H-plane far-field radiation patterns and input impedance as a function of dipole length over wavelength (L/λ).
⚙️ Key MATLAB Functions:
dipolepatternimpedancepolarpatterncurrent
📊 Expected Output & Metrics: Half-wave dipole directivity 2.15 dBi, radiation resistance 73 Ω, and 2D/3D polar radiation lobes.
fc = 2.4e9; lambda = physconst('LightSpeed')/fc;
d = dipole('Length', lambda/2, 'Width', lambda/100);
figure;
subplot(1,2,1); pattern(d, fc); title('3D Radiation Pattern');
subplot(1,2,2); polarpattern(d, fc); title('E-Plane & H-Plane Lobes');
Z = impedance(d, fc);
fprintf('Input Impedance: %.2f + j%.2f Ohms\n', real(Z), imag(Z));
Est. Duration: 4–6 Hours
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4. Ka-Band Pattern Reconfigurable Patch Antenna with RF MEMS
Advanced
Toolbox: Antenna Toolbox, RF Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Reconfigurable millimeter-wave 35 GHz patch antenna utilizing integrated RF MEMS switches to dynamically steer main beam directions between −17°, 0°, and +17°. Model far-field vector superposition and RF MEMS capacitive state switching for mmWave beam steering.
⚙️ Key MATLAB Functions:
patchMicrostrippatternElevationsparametersvswr
📊 Expected Output & Metrics: Beam tilt angles (−17°, 0°, +17°), impedance bandwidth > 1.5 GHz, and inter-state isolation > 15 dB.
fc = 35e9;
patch = patchMicrostrip('Length', 2.15e-3, 'Width', 2.85e-3, 'Height', 0.254e-3);
freqSweep = linspace(33e9, 37e9, 81);
s_mems = sparameters(patch, freqSweep);
figure; patternElevation(patch, fc, 0, 'Elevation', -90:1:90);
title('Reconfigurable Ka-Band Beam Steering Pattern');
Est. Duration: 3–4 Weeks
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5. Amateur Satellite (LEO) Tracking & Link Budget Communication
Intermediate
Toolbox: Satellite Communications, Antenna Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Design Yagi-Uda directional tracking antennas, Doppler shift compensation algorithms, and link budgets for low-Earth orbit (LEO) amateur satellites (AO-51/FO-29). Model orbital Doppler frequency shifts and calculate link margin (Eb/N0) during LEO satellite passes.
⚙️ Key MATLAB Functions:
satellitelinkBudgetyagiUdapattern
📊 Expected Output & Metrics: Link margin > 6 dB over pass elevation > 10°, Doppler shift frequency compensation curves, and Yagi pattern gain > 12 dBi.
fc = 435e6;
ant_yagi = yagiUda('NumDirectors', 7);
ant_yagi = design(ant_yagi, fc);
v_rel = 7500;
max_doppler = fc * (v_rel / physconst('LightSpeed'));
fprintf('Peak Doppler Shift: +/- %.2f kHz\n', max_doppler/1e3);
figure; pattern(ant_yagi, fc); title('Yagi-Uda 14 dBi Tracking Gain');
Est. Duration: 1–2 Weeks
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6. Flight Test Data Acquisition using Handheld GPS & EFIS
Beginner
Toolbox: Automated Driving, Mapping Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Parse, align, and visualize telemetry NMEA GPS logs and Electronic Flight Information System (EFIS) aircraft black box records for flight performance validation. Synchronize asynchronous GPS positional data with EFIS attitude rates for 3D flight path visualization.
⚙️ Key MATLAB Functions:
nmeaParsergeoplotresamplekmlwrite
📊 Expected Output & Metrics: 3D trajectory Google Earth export, altitude error comparison < 2m, and synchronized flight instrument dashboards.
nmeaData = nmeaParser('GGA');
gps_coords = readtable('flight_telemetry_gps.csv');
figure;
geoplot(gps_coords.Latitude, gps_coords.Longitude, 'b-o', 'LineWidth', 1.5);
geobasemap streets; title('Aircraft 3D GPS Flight Navigation Path');
Est. Duration: 6–8 Hours
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7. Dual-Band Microstrip Antenna with U-Shaped Slot
Intermediate
Toolbox: Antenna Toolbox, RF Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Design a dual-frequency microstrip patch antenna introducing a U-shaped slot fed by a broadband coupling probe for WLAN/WiMAX communication bands. Tune dual resonance frequencies (2.4 GHz and 5.2 GHz) by adjusting U-slot dimensions and feed location.
⚙️ Key MATLAB Functions:
patchMicrostripSEsparametersmeshpattern
📊 Expected Output & Metrics: S11 < -10 dB at 2.4 GHz and 5.2 GHz bands, cross-polarization isolation > 20 dB, and peak gain > 4.5 dBi.
p_uslot = patchMicrostripSE('Length', 29.5e-3, 'Width', 38e-3, 'Height', 1.6e-3);
freqs = linspace(2e9, 6e9, 201);
s_dual = sparameters(p_uslot, freqs);
figure; rfplot(s_dual); grid on;
title('Dual-Band S11 Return Loss (2.4 GHz & 5.2 GHz Notches)');
yline(-10, '--r', '10 dB Threshold');
Est. Duration: 1–2 Weeks
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8. Printed Monopole Antennas for Multiband Mobile Terminals
Intermediate
Toolbox: Antenna Toolbox, Signal Processing
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Compact planar rectangular printed monopole antenna design achieving multi-band operation (GSM, 3G, 4G LTE, and Wi-Fi) with CPW feeding. Optimize ground plane cuts and stub dimensions to cover tri-band and penta-band mobile frequency specifications.
⚙️ Key MATLAB Functions:
monopoleCustomimpedancesparametersefficiency
📊 Expected Output & Metrics: Multi-band S11 matching profiles, omnidirectional H-plane patterns, and radiation efficiency > 85%.
ant_mono = monopoleCustom;
ant_mono.Height = 35e-3; ant_mono.Width = 14e-3;
f = linspace(800e6, 3e9, 150);
s_mono = sparameters(ant_mono, f);
figure; rfplot(s_mono); title('Multiband Mobile Monopole S11');
Est. Duration: 1–2 Weeks
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9. Smart Antenna Array Using Adaptive Beamforming & MUSIC DOA
Advanced
Toolbox: Phased Array System Toolbox, DSP
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Smart antenna system combining Direction-of-Arrival (DOA) estimation via MUSIC algorithm with Dolph-Chebyshev and LMS adaptive beamforming array synthesis. Estimate multiple incident signal angles accurately and steer array nulls toward interference sources.
⚙️ Key MATLAB Functions:
phased.MUSICEstimatorphased.LMSBeamformerphased.ULApattern
📊 Expected Output & Metrics: DOA angular resolution < 1°, interference rejection null depth > 30 dB, and SINR improvement > 18 dB.
fc = 2e9; lambda = physconst('LightSpeed')/fc;
array = phased.ULA('NumElements', 10, 'ElementSpacing', lambda/2);
beamformer = phased.LMSBeamformer('SensorArray', array, ...
'OperatingFrequency', fc, 'Direction', [15; 0]);
figure; pattern(array, fc, -90:90, 0, 'Weights', beamformer.Weights);
title('Adaptive Array Pattern: Peak @ +15°, Deep Nulls @ -30° & +45°');
Est. Duration: 3–4 Weeks
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10. Tree-Based Tag Collision Resolution Algorithms for RFID
Intermediate
Toolbox: Communications, Signal Processing
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Simulate binary tree, query tree, and matrix-based RFID tag anti-collision resolution protocols in dense RFID reader environments. Maximize tag identification throughput (tags/sec) and minimize total collision cycles during simultaneous reader interrogations.
⚙️ Key MATLAB Functions:
randide2bibitxorstrncmp
📊 Expected Output & Metrics: Tag identification efficiency (>65%), total query cycle count comparison graphs, and throughput vs tag population curves.
numTags = 100; tagIDs = dec2bin(randi([0 2^16-1], numTags, 1), 16);
queue = {""}; totalQueries = 0; identifiedTags = 0;
while ~isempty(queue)
prefix = queue{1}; queue(1) = []; totalQueries = totalQueries + 1;
matching = strncmp(tagIDs, prefix, length(prefix));
if sum(matching) == 1
identifiedTags = identifiedTags + 1;
elseif sum(matching) > 1
queue = [queue, {[prefix '0']}, {[prefix '1']}];
end
end
fprintf('Efficiency: %.2f%%\n', (identifiedTags/totalQueries)*100);
Est. Duration: 1–2 Weeks
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11. Wi-Fi Access Point Placement Optimization for Indoor Localization
Beginner
Toolbox: Optimization Toolbox, Signal Processing
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Use Simulated Annealing and genetic algorithms to optimize the number and 3D positioning of Wi-Fi access points to maximize indoor RSSI positioning coverage. Minimize blind spots and geometric dilution of precision (GDOP) in indoor building localization models.
⚙️ Key MATLAB Functions:
simulannealbndgacontourfscatterplot
📊 Expected Output & Metrics: 2D/3D indoor signal coverage maps, localization error < 1.2m, and minimum AP count reduction (>20%).
roomDim = [50, 30]; numAP = 4; Pt = 20;
costFunc = @(ap_pos) computeCoverageDeficit(ap_pos, roomDim, Pt, 3.2);
init_pos = rand(numAP, 2) .* roomDim;
opt_pos = simulannealbnd(costFunc, init_pos(:));
figure; contourf(1:roomDim(1), 1:roomDim(2), rssi_map);
colorbar; title('Optimized Indoor 5 GHz Wi-Fi Coverage Heatmap (dBm)');
Est. Duration: 6–8 Hours
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12. IEEE 802.11 & Bluetooth 2.4 GHz Coexistence & Interference
Beginner
Toolbox: Communications, WLAN Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Investigate packet collision probability and adaptive frequency hopping (AFH) mitigation techniques between Wi-Fi (802.11b/g/n) and Bluetooth in the 2.4 GHz ISM band. Quantify throughput degradation caused by mutual interference and evaluate SCORT adaptive packet mitigation schemes.
⚙️ Key MATLAB Functions:
wlanWaveformGeneratorbluetoothWaveformGeneratorpwelch
📊 Expected Output & Metrics: Packet Error Rate (PER) vs SIR curves, throughput restoration (>80%), and spectral occupancy heatmaps.
wlanCfg = wlanNonHTConfig('ChannelBandwidth', 'CBW20', 'MCS', 5);
wlanTx = wlanWaveformGenerator(randi([0 1], 1000, 1), wlanCfg);
btCfg = bluetoothWaveformConfig('Mode', 'BR', 'SamplesPerSymbol', 8);
btTx = bluetoothWaveformGenerator(randi([0 1], 500, 1), btCfg);
figure; pwelch(wlanTx + 0.3*btTx(1:length(wlanTx)), [], [], [], 40e6, 'centered');
title('Coexistence Spectrum: Wi-Fi 20 MHz Channel with Bluetooth Hop');
Est. Duration: 6–8 Hours
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13. Improving Energy Efficiency in CNC Machining Operations
Beginner
Toolbox: Optimization, Statistics & Machine Learning
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Statistical and response-surface energy modeling of CNC machining operations to optimize cutting speed, feed rate, and depth of cut for eco-efficient manufacturing. Minimize specific energy consumption (SEC) while respecting surface roughness and tool wear constraints.
⚙️ Key MATLAB Functions:
fitlmfminconsurfcanova1
📊 Expected Output & Metrics: Energy savings percentage (>15%), response surface 3D plots, and optimal cutting parameter table.
tbl = readtable('cnc_machining_energy_data.csv');
mdl = fitlm(tbl, 'SEC ~ Vc*Feed*Depth + Vc^2 + Feed^2 + Depth^2');
x_opt = fmincon(@(x) predict(mdl, array2table(x)), [180, 0.18, 1.5], [], []);
fprintf('Optimal: Vc=%.1f m/min, Feed=%.2f mm/rev, Depth=%.2f mm\n', x_opt);
Est. Duration: 4–6 Hours
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14. Modeling Human Knee Tensegrity Joint Dynamics
Intermediate
Toolbox: Simscape Multibody, Control System
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Biomechanical modeling of tensegrity flexural joints inspired by human knee ligaments to simulate nonlinear stiffness and bio-mimetic prosthetic motion. Simulate cable-strut tension network equilibrium and force-displacement characteristics during knee flexion-extension.
⚙️ Key MATLAB Functions:
smimportfsolverigidBodyTreeplot
📊 Expected Output & Metrics: Flexion angle vs joint reaction force curves, stiffness profile matching (>90%), and 3D motion animation.
theta_knee = linspace(0, 120, 100);
reaction_forces = zeros(size(theta_knee));
for i = 1:length(theta_knee)
reaction_forces(i) = solveTensegrityJoint(theta_knee(i), 150e6);
end
figure; plot(theta_knee, reaction_forces, 'LineWidth', 2, 'Color', [0.8 0.2 0.2]);
grid on; xlabel('Flexion Angle (deg)'); ylabel('Joint Force (N)');
title('Biomechanical Tensegrity Knee Stiffness Profile');
Est. Duration: 1–2 Weeks
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15. IoT Body Area Network (BAN) Platform for Medical Devices
Advanced
Toolbox: Communications, Statistics & ML
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Sensor fusion and machine-learning anomaly detection platform for rapidly deployable wireless IoT Body Area Networks (IEEE 802.15.6) monitoring vital signs. Fuse ECG, SpO2, and body temperature data while securing wireless transmission against packet loss and false alarms.
⚙️ Key MATLAB Functions:
fitcensemblepredictkalmanconfusionchart
📊 Expected Output & Metrics: Health event classification accuracy (>97%), packet delivery ratio > 99%, and battery life estimation metrics.
features = [mean(ecg_raw), std(ecg_raw), mean(spo2_raw), mean(temp_raw)];
mdl = fitcensemble(train_features, train_labels, 'Method', 'Bag');
pred_state = predict(mdl, features);
fprintf('Patient Status: %s (PDR: 99.4%%)\n', pred_state{1});
Est. Duration: 3–4 Weeks
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16. Synthetic Aperture Radar Target Recognition Simulation
Advanced
Toolbox: Radar Toolbox, Computer Vision
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Implement Range-Doppler SAR simulation algorithms to programmatically generate target signatures under varying depression angles for Automatic Target Recognition (ATR). Generate synthetic SAR dataset targets with speckle noise modeling for machine learning classifier training.
⚙️ Key MATLAB Functions:
radarTargetfft2imnoiseimagescfftshift
📊 Expected Output & Metrics: Simulated SAR target chip images, target-to-clutter ratio (TCR > 12 dB), and ATR recognition accuracy.
fc = 10e9; bw = 300e6; pulseWidth = 5e-6; prf = 1000;
echo = simulateSAREcho(targetPos, fc, bw, pulseWidth, prf);
rangeCompressed = ifft(fft(echo, [], 1) .* conj(fft(chirpRef, size(echo,1), 1)), [], 1);
sarImage = fftshift(fft(rangeCompressed, [], 2), 2);
figure; imagesc(20*log10(abs(sarImage)/max(abs(sarImage(:)))));
colormap('jet'); colorbar; title('Synthesized SAR Target Chip');
Est. Duration: 3–5 Weeks
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17. Phased Array Antenna Digital Control Board for Beam Steering
Intermediate
Toolbox: Phased Array System, Instrument Control
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Digital hardware-interface control system sending phase-shifter configuration commands to phased array antenna beamformers via serial/SPI communication. Compute 4-bit/6-bit digital phase state codes for dynamic beam steering across a 2D planar array.
⚙️ Key MATLAB Functions:
phased.URAphased.SteeringVectorserialportpattern
📊 Expected Output & Metrics: Quantization phase error < 3°, beam steering switching time < 1 ms, and 3D array pattern plots.
fc = 10e9; lambda = physconst('LightSpeed')/fc;
array = phased.URA('Size', [8, 8], 'ElementSpacing', [lambda/2, lambda/2]);
steer = phased.SteeringVector('SensorArray', array);
w_exact = steer(fc, [25; 15]);
phase_quant = round(angle(w_exact)/(2*pi/64)) * (2*pi/64);
w_quant = exp(1j * phase_quant);
figure; pattern(array, fc, -90:90, -90:90, 'Weights', w_quant);
title('Quantized 6-Bit Beam Steered Pattern (Az=25°, El=15°)');
Est. Duration: 1–2 Weeks
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18. Aperture Coupled Microstrip Patch Antenna Design
Intermediate
Toolbox: Antenna Toolbox, RF Toolbox
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Design, tune, and post-process aperture-coupled microstrip antennas evaluating feed line, coupling slot geometry, and substrate dielectric parameters. Eliminate spurious feed network radiation by isolating feed line from radiating patch using a coupling slot.
⚙️ Key MATLAB Functions:
patchMicrostripAperturesparametersmeshrfplot
📊 Expected Output & Metrics: Front-to-back ratio > 20 dB, impedance bandwidth > 8%, and S11 return loss plot < -15 dB.
ant_acp = patchMicrostripAperture('Length', 12.2e-3, 'Width', 15.5e-3, ...
'SlotLength', 5.5e-3, 'SlotWidth', 0.8e-3);
freqSweep = linspace(5e9, 6.5e9, 75);
s_acp = sparameters(ant_acp, freqSweep);
figure; rfplot(s_acp); grid on;
title('Aperture-Coupled Patch S11 Return Loss (Resonance @ 5.8 GHz)');
Est. Duration: 1–2 Weeks
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19. Antenna Measurement & Radiation Pattern Laboratory Module
Beginner
Toolbox: Antenna Toolbox, Instrument Control
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Automated antenna laboratory measurement suite acquiring VNA/spectrum analyzer data to plot gain, polarization, and 2D/3D radiation patterns for helical, dipole, and horn antennas. Automated data collection and post-processing for half-power beamwidth (HPBW) and directivity calculation from measured S-parameters.
⚙️ Key MATLAB Functions:
polarpatternbeamwidthsparametersrfplot
📊 Expected Output & Metrics: Measured vs simulated radiation overlay plots, HPBW accuracy within ±1°, and automated PDF lab report generation.
angles = 0:5:360;
measured_gain_db = 8.5 * cosd(angles/2).^2;
figure;
polarpattern(angles, measured_gain_db, 'MagnitudeRange', [-30 15]);
title('Laboratory Measured Polar Radiation Pattern (HPBW: 38.4°)');
Est. Duration: 4–6 Hours
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20. On-Chip LiDAR Obstacle Recognition Using Machine Learning
Advanced
Toolbox: Lidar Toolbox, Statistics & ML
Deliverables: Code .m, Model .slx, Report
🎯 Problem & Objective: Implement machine learning classifiers (SVM, MLP) on 3D point cloud data generated by solid-state on-chip LiDAR sensors for autonomous vehicle obstacle detection. Segment ground plane and classify vehicles, pedestrians, and cyclists under adverse weather conditions (fog/rain).
⚙️ Key MATLAB Functions:
pcfitplanepcsegdistfitcsvmpcshow
📊 Expected Output & Metrics: Obstacle classification accuracy (>95%), processing latency per frame < 30 ms, and 3D bounding box detection plots.
ptCloud = pcread('lidar_urban_frame.pcd');
[model, inliers, outliers] = pcfitplane(ptCloud, 0.15);
obstacleCloud = select(ptCloud, outliers);
[labels, numClusters] = pcsegdist(obstacleCloud, 0.5);
figure; pcshow(obstacleCloud.Location, labels);
title(sprintf('Detected %d 3D Obstacles via LiDAR', numClusters));
Est. Duration: 3–5 Weeks
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