What?

• • Data center cooling consumes nearly 50% of total energy, making efficiency critical.
• • Traditional air cooling struggles with hotspots and thermal stratification in high-density racks.
• • Liquid cooling offers the potential to remove heat at the source, reducing energy use and improving temperature uniformity.
• • The project aimed to compare air vs. liquid cooling to identify scalable, cost-effective thermal strategies.
• • Real-world conditions were simulated to benchmark methods for modern data center loads (~10 kW per rack).

Current Energy Usage Challenge

How?

• • Developed a detailed 3D model of a data center rack and environment in Fusion 360, including modular air and liquid cooling features.
• • Ran CFD simulations in SimScale to analyze heat transfer and airflow under realistic load conditions.
• • Implemented hot aisle containment and adaptive fan control to enhance air cooling performance.
• • Analyzed simulation results in Python, computing metrics like rack temperatures and cooling energy needs.
• • Visualized outcomes via an interactive Streamlit dashboard for clear comparison of cooling methods.

Airflow Architecture

Liquid Cooling Loop

Results?

• • Improved cooling performance by 27% and reduced hotspots by 32% with optimized design.
• • Liquid cooling kept inlet temperatures up to 15°C cooler than air cooling at the same load.
• • Hot aisle containment lowered upper-rack temperatures and enhanced airflow efficiency.
• • Projected PUE improved from ~1.5 (air-cooled) to ~1.1 (liquid-cooled hybrid system).
• • Demonstrated a scalable, low-cost approach to guide future smart thermal management strategies.

Cooling Performance Comparison

System Integration & Future Outlook

Hybrid System Integration

What?

• • Conventional data centers face high failure rates and energy costs due to thermal cycling and corrosion.
• • Project Natick showed that sealed nitrogen pods underwater cut failure rates by 8× and achieved PUE ~1.07.
• • The project explored adapting Natick’s sealed, nitrogen-filled pod concept for land-based data centers.
• • The goal was to quantify reliability, thermal stability, and energy efficiency gains.
• • Aimed to provide a practical design blueprint for land deployment.

How?

• • Designed two CAD models: a standard open rack and a sealed nitrogen pod with integrated cooling.
• • Conducted CFD simulations in SimScale to compare thermal profiles and cooling demands.
• • Tested variations in nitrogen concentration, insulation, and coolant temperature for optimization.
• • Applied species transport models to track nitrogen levels and oxygen exclusion in the sealed pod.
• • Used corrosion and thermal cycling models to project reliability benefits.

Nitrogen Pod Life-Cycle State Diagram

Figure 2. Nitrogen pod life-cycle state diagram.

Sankey Energy Flow: Pod vs Air Cooling

Figure 3. Sankey energy flow: pod vs air cooling.

Result?

• Temperature swings were dramatically reduced in the sealed pod (see Figure 4), with daily fluctuations dropping from ±6°C in open racks to just ±1°C, helping to extend hardware lifespan.
• Thermal reliability improved by 25% and projected failure rates dropped by 35–40% (see Figure 5), as shown by the reliability gauge and KPI table.
• Energy flow analysis (Figure 3) shows that the pod design reduces cooling energy demand by ~10%, with more power delivered to IT load and less lost to inefficiencies.
• The state diagram (Figure 2) illustrates the robust operational cycle of the nitrogen pod, emphasizing routine inspection and nitrogen replenishment for long-term reliability.

Daily Temperature Swing: Open Rack vs Pod

Figure 4. Daily temperature swing: open rack vs pod.

Reliability Gauge: Projected Failure-Rate Drop

Figure 5. Reliability gauge: projected failure-rate drop.

What?

• • Investigation into the potential of thin-film solar photovoltaic technologies to achieve over 30% cell efficiency and large-scale manufacturing by 2035.
• • Focus on advances in semiconductor materials, device engineering, and fabrication processes to enable scaled manufacturing of high-tech solar devices.
• • The 30% efficiency target is significant as it doubles the efficiency of current commercial panels and approaches the theoretical limits for single-junction solar cells.
• • Global scope aligns with energy agencies’ 2035 renewable energy cost reduction milestones.

How?

• • Multidisciplinary systems modeling combining engineering assessments and market trends.
• • Engineering models estimate efficiency potential via quantum dot configurations, defect suppression, and bandgap optimizations.
• • Economic models forecast manufacturing expansion and cost learning under various policy scenarios.
• • Techno-economic simulations project adoption rates and R&D sensitivity.

Results?

• • Provides probability distributions for thin-film solar cells surpassing 30% efficiency and production forecasts by 2035.
• • Projects major impact on solar PV capacity expansion and fossil fuel displacement.
• • Offers insights for solar firms, policymakers, and research priorities in manufacturing and semiconductor synthesis.
• • Contributes to global decarbonization trajectories.

What?

• • Manual job outreach is slow, repetitive, and expensive. Traditional cold outreach requires hours of manual email writing, tracking, and follow-up—often costing $200–$500/month for recruiter services or SaaS tools.
• • Direct, personalized outreach is crucial for standing out, but doing it at scale is nearly impossible by hand.
• • After being laid off, I saw firsthand how important automation is in today’s competitive job market and the new age of outreach. I wanted a smarter way to market myself and manage the repetitive side of job searching.
• • Building this bot let me reclaim my time for what matters most—applying on career pages, preparing for interviews, and growing my skills—while the automation handled the busywork. It’s about working smarter, not just harder.

How?

• • v1.0: OCR tools (Tesseract, Apple Vision, Microsoft OCR) for resume/contact extraction—quickly hit accuracy and formatting limits.
• • v2.0: DOM scraping and bookmarklets for LinkedIn/Apollo—HTML changes and anti-bot measures broke reliability.
• • v3.0: Apollo API access—limited to 10 contacts/page, not scalable for bulk outreach.
• • v3.5: CSV bulk export automation: Python parses Apollo CSVs, Gmail API + OAuth 2.0 for secure, multi-account draft creation, async drafting for speed, CLI interface prompts for company, position, template, and Gmail account, personalized drafts with resume/cover letter attachments, multi-account support and job-title input, file export for tracking outreach per company.

Results?

• • The bot drafts 108 personalized emails in just over 1 minute (1:03), making high-volume outreach practical and efficient for job seekers.
• • To avoid spam risks and ensure deliverability, the safest workflow is to generate drafts and send them manually—this keeps control in your hands and prevents bulk-sending flags.
• • This approach saves significant time and effort compared to manual drafting, and can easily replace $400–$800/month SaaS or recruiter tools for similar outreach volume.
• • The dashboard and logs provide clear visibility into campaign progress, helping you iterate on templates and targeting for better results over time.
  🔗 View Dashboard Demo
• • While automation speeds up the process, it’s designed to support—not replace—thoughtful, targeted outreach and follow-up.

What?

• • Semiconductor deposition rate optimization directly impacts manufacturing throughput, cost per wafer, and device performance.
• • Baseline process had low deposition rates and high variability, causing production bottlenecks and degraded yield.
• • Complex parameter interdependencies (temperature, pressure, gas flow, plasma power) make manual optimization impractical.
• • Goal: Increase deposition rate by 40% while maintaining film quality standards and reducing process variability.
• • Implemented two-phase strategy: Design of Experiments (DOE) followed by Bayesian optimization for efficient parameter space exploration.

How?

• • Phase 1: DOE systematically explored major parameters (temperature: 200-500°C, pressure: 0.5-5 Torr, plasma power: 50-150W) using factorial designs.
• • DOE revealed key trends: power increases rate with diminishing returns, temperature has optimal range around 300°C.
• • Built preliminary empirical model using DOE results to predict deposition rate for untested parameter combinations.
• • Phase 2: Bayesian optimization using Gaussian Process models for intelligent parameter space exploration.
• • Expected Improvement acquisition function balanced exploration of uncertain regions with exploitation of promising areas.
• • Python automation integrated with lab equipment for streamlined parameter setting and data collection.

Results?

• • Achieved 40% improvement in deposition rate, significantly increasing production throughput.
• • Reduced process variability by 25% and defect density by 20%, improving yield and consistency.
• • Developed predictive model with 90% accuracy for deposition rate forecasting.
• • Realized 15% reduction in manufacturing cost per wafer through improved efficiency.
• • Bayesian optimization converged in only 15-20 iterations, far fewer than traditional methods.
• • Validated results through comprehensive metrology (ellipsometry, SEM, XRD, defect inspection).
• • Maintained all quality specifications while achieving speed improvements.

What?

• • Simulated 3D external flow around a flying saucer (UFO shape) inside a virtual wind tunnel to study aerodynamic behavior at high altitude.
• • Investigated how varying the tilt angle (0°, 22.5°, 45°) affects lift and drag forces.
• • Visualized velocity field distributions at different tilt configurations to assess stability and wake patterns.

Mesh along plane of symmetry

How?

X-velocity contour at 0° tilt

• • Modeled the saucer by revolving a 2D profile and set it inside a cylindrical wind tunnel geometry.
• • Applied parabolic velocity profile at the inlet (max 100 m/s at center) and pressure outlet at the exit; used k-omega turbulence model for steady-state solutions.
• • Ran separate cases for each tilt angle and analyzed flow with x-velocity contour plots at key planes.

X-velocity contour at 22.5° tilt

Results?

• • Lift and drag forces increased with tilt angle; maximum lift recorded at 22.5° tilt.
• • X-velocity contours confirmed accurate boundary conditions and showed stronger flow separation at higher tilt angles.
• • Outlet flow revealed clear velocity gradient consistent with increased wake disturbance at 45° tilt.

X-velocity contour at 45° tilt

X-velocity over outlet at 45° tilt

Lift and drag force summary chart

🔗 View Full Report (PDF)

Buoyancy-Driven Internal Flow for Earth vs. Moon Gravity

• • WHAT? Simulated thermal convection in a water tank to study how Earth and Moon gravity impact internal mixing and heat distribution.
• • HOW? Used Boussinesq approximation and transient ANSYS Fluent simulations with velocity inlet, outflow outlet, and thermal boundary at tank bottom.
• • RESULT? Visualized stratification and mixing; Earth gravity showed stronger downward jet and mixing compared to Moon gravity.

Y-velocity contour at t = 5 min (Earth)

Temperature contour at t = 5 min (Earth)

Temperature contour at t = 1 min (Moon)

Avg. temperature vs. time plot

Internal Flow with Heated Wall

• • WHAT? Simulated water flow through a coiled pipe heated externally to assess how inlet velocity affects temperature gain.
• • HOW? Used steady-state CFD with uniform wall heating (1000 W/m²) and varied inlet velocity to analyze outlet temperature rise.
• • RESULT? ΔT decreased as inlet velocity increased due to reduced residence time; hand calculation provided reference comparison.

Helical Spring/Bar CAD Model

Outlet temperature contour (0.01 m/s)

Skin temperature of helical pipe

Compressible Jet and Nozzle Flow

• • WHAT? Simulated air jet discharge and nozzle flows to study compressible flow behavior, Mach number rise, and adiabatic cooling.
• • HOW? Used transient and steady-state CFD, density-based solver, and ideal gas law with varying initial pressures and outlet pressures.
• • RESULT? Identified stronger cooling and higher velocity in cases with larger adiabatic expansion; Mach number increased along nozzle length.

Mach number along jet / nozzle axis

Velocity along jet / nozzle axis

Nozzle static temperature contour

Nozzle density contour

🔗 View Full Report (PDF)

What?

• • Simulated heat transfer and fluid flow within a half-pipe geometry using Computational Fluid Dynamics (CFD).
• • Analyzed temperature distribution, velocity profiles, and heat transfer rates under different inlet conditions.
• • Studied how inlet size affects thermal performance and flow behavior.

How?

• • Created half-pipe model and boundary conditions in ANSYS Fluent (or equivalent CFD tool).
• • Simulated large and small inlet configurations and analyzed pressure outlet conditions.
• • Used custom field functions and surface integrals to compute heat transfer rates at key surfaces.
• • Generated contour plots for velocity, static temperature, and Y velocity to study flow and thermal fields.

Figure. Static temperature profile of the simulation domain.

Results?

• • Heat transfer rate for large inlet: 183,215 W
• • Heat transfer rate for small inlet: 152,271 W
• • Heat transfer rate at pressure outlet: 335,470 W (matches sum of inlet rates)
• • Results showed clear thermal and flow differences with varying inlet sizes.

🔗 View Full Report (PDF)

Precision Engineering Showcase

Explore a collection of meticulously crafted 3D CAD models demonstrating expertise in mechanical design, assembly engineering, and surface modeling. Each model represents real-world engineering challenges and solutions, from industrial components to consumer product design.

• • Industrial-grade mechanical assemblies and components
• • Detailed exploded views and technical drawings
• • Advanced surface modeling applications
• • Design for manufacturing principles

Pump Motor Assembly

Pump Housing Component

Pump Assembly - Exploded View

Pipe Manifold System

Pipe Manifold - Alternate Design

Screw Jack - Exploded View

Screw Jack Assembly

Mechanical Clamp

Clamp - Exploded View

V-Block Fixture

Vane Rotor Assembly

Toggle Clamp Mechanism

Toggle Clamp - Exploded View

Mount Bracket

Housing Cover

Bearing Block Assembly

Bearing Block - Exploded View

Hair Dryer Handle (Surface Modeling)

Piston Head Assembly

Water Jug (Surface Modeling)

Water Jug - Alternate View

Machining Block - Front View

Machining Block - Isometric View

Machining Block - Top View

Drainer Sink (Surface Modeling)

Bearing Cap

Click any image to open, zoom, and browse the full CAD gallery.

Engineering Excellence in Action

Each model in this collection showcases specific engineering principles and design considerations:

• • Mechanical Assemblies: Screw Jack, Bearing Blocks, Pipe Manifold
• • Manufacturing Fixtures: V Block, Clamps, Toggle Clamps
• • Pump Systems: Complete Pump Assembly with Housing and Rotor
• • Surface Modeling: Consumer Products and Ergonomic Designs
• • Technical Documentation: Exploded Views and Assembly Guides

Technical Highlights

Key features and achievements demonstrated in this collection:

• • Complex Assemblies: Multi-component systems with proper constraints and relationships
• • Design Intent: Models created with manufacturing and assembly in mind
• • Technical Documentation: Clear visualization of components and assemblies
• • Surface Modeling: Complex organic shapes and ergonomic designs
• • Precision Engineering: Accurate dimensions and tolerances for real-world applications

What?

• • For this Diploma major project, we wanted to develop something which is sustainable for small manufacturers.
• • Small manufacturers used conventional processes which had drawbacks such as it required more time for production, a greater number of workers, and hence more space for production.
• • The “Brick Making Machine” (Sold as “Paving Block Making Machine”) was made for small-scale industries to manufacture bricks and paving blocks at a lesser time with qualitative production in less effort.
• • The goal was to make Brick Making Machine in a minimum cost so that it can be afforded to small-scale industries.
• • The design of each part was also especially important, the design should be such that the machine can withstand continuous operations. We used CATIA V5-R2019 for part designing purposes.
• • Knowledge of the Theory of Machines, Heat and Mass transfer, testing and measurements, and strength of the material was used while developing ‘Brick Making Machine’.

How?

• • While selecting the material we considered the following factors: Availability of Material, Suitability of Material for working conditions, and the cost of the material.
• • After considering all the factors we decided to use Mild Steel as it is a type of steel alloy that contains a high amount of carbon.
• • For the manufacturing process we used Lathe Machine, Grinding Machine, Drilling Machine, and Welding Machine, etc.
• • The Brick making machine has four main parts: hopper, slider box, mold box, and pressing mechanism.
• • The hopper is designed for easy pouring of raw material into the slider box and to prevent any residue from remaining in the hopper.
• • The slider box collects the raw material from the hopper and transfers it to the mold box.
• • The pressing mechanism, consisting of the mold box, long lever, and sliding mechanism, applies pressure to the raw material in the mold box to shape it.
• • Pulling back the lever and pressing down again removes the paving block from the mold box.

Results?

• • The main important thing is that the number of skilled workers required for the production of brick and paving blocks was less than the number of semiskilled and unskilled workers required to use this machine.
• • In the conventional method six skilled workers were required for production of brick and paving blocks whereas in this machine maximum of 2 workers, efforts required by the worker are also reduced.
• • The time required for a single brick in the conventional method is about 15-18 minutes which is reduced to less than 5 minutes per brick.
• • The initial investment of the machine is slightly higher than the conventional method, but this investment is a one-time investment, not paid monthly or yearly.
• • Machine saves the monthly worker expenses at the same output. The only thing required is the small maintenance of the machine.
• • The Brick Making Machine (Sold as: ‘Paving Block Making Machine’) was sold to the same industry for 11,540Rs (INR).

What?

• • Environmental pollution was recognized as one of the major problems of the modern world.
• • After visiting small-scale industries such as foundry, jaggery industries, and textile industries we found that the small black color particles are present everywhere near these industries. These are the solid pollutants emitted from the chimneys of small-scale industries.
• • Air pollution can cause long-term damage to people’s nerves, brain, kidneys, liver, and other organs.
• • Considering all these issues we can use air filtration technology in these industries to reduce the emission of solid pollutants and achieve a sustainable and healthy environment.

Design Features

• • We used Mild Steel because it has a melting point of 1350 to 1500 degrees Celsius.
• • We provided a door on one side of the air filter for maintenance purposes to easily access over the replacement of the HEPA/ MANN filter and clean the filter from the inside.
• • Packing material on joints and doors of the air filter to minimize air leakages.
• • We used high-temperature packing material (Champion Sheet). A high-temperature packing sheet is used because in case the temperature of flue gas is high it should be sustained at that temperature for a longer period.
• • Zinc chromate yellow primer coating is used. It is an odorless chemical compound primarily used as an industrial paint coating. The compound is a beneficial coating because it is an anti-corrosive and an anti-rust primer.
• • HEPA filter is used which stands for high-efficiency particulate air. A HEPA filter is a type of mechanical air filter; it works by forcing air through a fine mesh that traps harmful particles such as pollen, pet dander, dust mites, and tobacco smoke.
• • Maximum continuous operating temperature 350°C, efficiency 99.99% at 0.3 μm.

How?

• • Firstly, we are done with all the dimensions on the M.S sheet and cut down all required sides.
• • Then we formed a structure by using an L angle & welded all the sides on it.
• • Thereafter we made the inlet portion by using a 5-inch M.S pipe mounted on an M.S sheet.
• • Then we built the first chamber and placed the 1st HEPA/ MANN air Filter with a range of 10 microns.
• • In the Second chamber we arranged 12 MS pipes with a diameter of 1 inch each.
• • In the third chamber, we have arranged HEPA air Filter with a range of 2.5 microns on its outlet.
• • After that we set the outlet portion on it then we provide a door fully sealed to prevent air leakages by using a high-temperature packing sheet.

Result?

• • After doing an Experiment for 8 hours per day we trapped soot, dusty particles, and PM (particulate matter) which will expose us to air and harm the environment around us.
• • The amount of collected solid pollutants on 1st day is 382.5 gm. On the 2nd day, the amount is 370.29 gm and on the 3rd day, the amount is 389.43 gm.
• • The PM concentration on the 1st day was 20 mg/m3, on the 2nd day it was 19.3 mg/m3 and on the 3rd day it was 20.36 mg/m3. Thus, the average PM concentration is 19.88 mg/m3.
• • The Project was designed and analyzed and satisfies all the necessary conditions for an efficient, economical air filter. The different layers of filters used are quite simple and easily available materials (filtrates). The three-layer filtration makes an effective filtration process.

What?

• • A hydraulic ram pump is a water pump powered by water with a height difference.
• • In areas where natural flows exist with a height difference of the water over a small distance, hydraulic ram pumps can be used to transport water to higher grounds without using electricity or fuel in places without a constant electricity supply (Rural areas/Villages).
• • The goal was to provide a sustainable solution for water pumping and transportation, especially in areas with natural water flows with a height difference.

How?

• • To make this project, we conducted extensive research on the concept and design of hydraulic ram pumps.
• • We studied the various components and materials that would be suitable for the construction of the pump.
• • After considering the customer's requirements, we decided to use lightweight materials such as aluminum, PVC, and stainless steel, to keep the pump light, in weight and low in cost.
• • To make the pump easy to use, we designed it to have a simple mechanism with only a few moving parts.
• • The pump was designed with a closed-loop system that would prevent water from entering the pump's internal components, making it durable and long-lasting.

Result?

• • The results of this project were successful. We were able to design and develop a hydraulic ram pump that met the customer's requirements.
• • The pump was simple in mechanism, light in weight, low in cost, easy to use, and strong enough to withstand the demands of pumping water in rural areas and villages.
• • The pump was assessed in different environments and performed well, proving to be an effective and sustainable solution for water pumping and transportation in areas without a constant electricity supply.

What?

• • The automatic packaging machine project was initiated to address the pressing needs in the packaging industry for improved efficiency, safety, and cost savings.
• • The primary aim was to reduce the manual labor requirements in the packaging process and minimize the risk of injury to employees.
• • The increasing demand for faster and more efficient packaging methods also contributed to the development of this project.

How?

• • The project was executed with the guidance of a team of engineers and technicians who have experience in the packaging industry.
• • Designed with SolidWorks and developed three prototypes of cutting-edge automatic packaging machine with a PLC system that is equipped to manage a wide range of goods, including fragile and heavy items.
• • The machine is designed to ensure maximum load containment and consistency in wrapping, which leads to cost savings in the use of stretch film.
• • (Prototypes of the project for easy understanding of workers, cannot display the SolidWorks model because of the contract with the industry)

Results?

• • The automatic packaging machine was a highly efficient tool in the packaging process, as it can wrap pallets much faster than manual methods which reduced manual labor by 70%.
• • The machine has also improved safety conditions in the workplace by reducing the risk of manual handling injuries. Moreover, the machine has enabled labor reallocation, freeing up employees to be more productive in other areas.
• • The film cost savings was significant, as the consistency of wrapping and the ability to use less stretch film has resulted in 60% cost reductions and waste reduction by 75%.
• • In conclusion, the automatic packaging machine project has successfully addressed the challenges faced by the packaging industry and provided a highly efficient, safe, and cost-effective solution.
• • The project was sold to the industry for 18,000Rs (INR) and it became a valuable tool for businesses in the packaging industry.

What?

• • The performance of the central AC system will decrease when the outdoor temperature increases.
• • This is the case in Arizona where the ambient temperature increases up to 41°C with low humidity.
• • The chart below shows how the AC cooling capacity gets derated when the outdoor temperature gets high, which is the case of the weather in Arizona State during the summer season.

How?

• • The table shows the operation pressure of R-410A at different ambient temperatures.
• • It is clear that the AC compressor needs to work harder and longer to achieve higher operating pressure which derates the cooling capacity of the system.
• • Adding a mist system in front of the condenser will decrease the ambient temperature around the condenser and improve its performance.
• • The psychrometric chart below shows how adding water to the air can reduce its temperature.

Results?

• • Based on United States Census there are 3,138,871 houses in Arizona as of July 1st, 2021.
• • We considered only 70% of that falls in our assumption of house size of 1900 SQFT and AC size of 4 Ton unit. We have 2,197,209.70 houses.
• • The energy saving is 540,290,302 KWh per month which is a cost saving of $69,319,245.77 per month.

What?

• • Coffee is, and remains, one of the world’s most popular beverages with an estimated 66% of Americans enjoying the drink daily.
• • There is then a need to understand what makes good coffee to best understand how to leverage both the size of the market and the potential revenue one would enjoy by perfecting the process. It is also important to note the consumer benefits of optimizing the coffee brewing process.
• • Thus, the aims of this experiment were to determine which factors and their levels in the brewing of pour-over coffee lead to both greater satisfaction from consumers and lie closest to an optimal pH value.

Table 1. Design Factors for Coffee Optimization Experiment

How?

• • Jars of coffee grounds were pre-weighed (10 g) for each of the three grind-time settings defined in the design, ensuring every treatment started with the correct grind size and dose.
• • A calibrated scale and timers governed all water/coffee ratios, grind times, bloom intervals, and total brew times; all containers were washed between trials to prevent cross-contamination.
• • After brewing, each cup cooled exactly five minutes before tasting; pH was measured after tasting to reflect coffee at a consistent, drinkable temperature.
• • Testers received equal portions, rated each sample privately with a six-question scoring tool, and force-ranked the day’s 11 treatments from best to worst.
• • All scores, ranks, and pH readings were logged immediately in both the JMP experiment file and a backup Excel sheet for later DOE analysis.

Figure 4. Experiment Data Table

Results?

• • Running an analysis considering both the average Forced Rank data and the standard deviation for Forced Rank data, a predicted optimal cup of coffee is found.
• • The treatment is as follows: Grind the beans for 16 seconds, use the low-level Water-to-Coffee Ratio of 16:1, brew for three minutes at the high-level temperature of 205 degrees Fahrenheit, and make sure to let the coffee brew bloom for 30 seconds.
• • This prediction not only provides a desirable coffee, but a coffee that is robust to the subjectivity of the consumers.

What?

• • This project explores image compression using Singular Value Decomposition (SVD) in MATLAB.
• • SVD is a mathematical technique that decomposes an image matrix into three simpler matrices, allowing for efficient data reduction.
• • The goal is to significantly reduce image file size while preserving as much visual quality as possible.
• • Applications include storage optimization, faster image transmission, and foundational understanding for advanced image processing tasks.

How?

• • The original image is loaded into MATLAB and converted to a grayscale matrix for processing.
• • Singular Value Decomposition (SVD) is applied to decompose the image matrix into U, S, and V matrices.
• • By retaining only the largest singular values, a compressed approximation of the original image is reconstructed.
• • The number of singular values kept determines the balance between compression ratio and image quality.
• • Both the original and compressed images are displayed for visual comparison and analysis.

Figure 1. Original grayscale image loaded in MATLAB

Figure 2. SVD decomposition and image compression process

Results?

• • The compressed image retains most visual features of the original, despite using far fewer singular values.
• • File size is reduced substantially, demonstrating the effectiveness of SVD-based compression.
• • Visual comparison shows minor loss of detail, which can be adjusted by varying the number of singular values retained.
• • This approach provides a practical introduction to matrix-based image compression and its trade-offs.

Figure 3. Compressed image reconstructed from limited singular values

Figure 4. Side-by-side comparison of original and compressed images

What?

• • In today's digital age, having a strong and secure password is more important than ever. However, remembering a strong and unique password for each of your online accounts can be a challenge.
• • That is where a password generator comes in handy. This project aims to provide a simple and efficient solution to generate random and secure passwords that can be used for various online accounts.

How?

• • The import random and import string statements import the necessary modules required for the password generator.
• • The password_generator function takes an input length and generates a random password of the desired length.
• • The chars variable holds a combination of ASCII letters, digits, and punctuation characters.
• • The return statement uses the join function to concatenate the randomly generated characters from the chars variable to form a password.
• • The length variable is initialized by taking the input from the user.
• • The generated password is printed using the print function.

Result?

• • The password generator is a simple and efficient solution for generating random and secure passwords.
• • It can generate passwords of varying lengths and complexity, depending on the input provided by the user.

• • The combination of letters, digits, and punctuation characters ensures that the generated passwords are strong and secure.
• • The code can be easily modified to include additional characters or constraints, such as excluding similar characters or