Project

# Title Team Members TA Documents Sponsor
28 Saxophone Effects Pedal
Eliseo Navarrete
Peter Hevrdejs
Sean McGee
Prashant Shankar appendix1.zip
design_document3.pdf
final_paper1.pdf
photo1.jpg
presentation1.pptx
proposal1.pdf
**Members:** Peter Hevrdejs [pdh2 – In Person], Eliseo Navarrete [eliseon2 – Online], Sean McGee [seantm2 – In Person]

# **PROBLEM:**

There are currently no quality effect pedals on the market specifically designed for horn players. The market is currently dominated in catering towards electric guitar / bass players - with the simplicity of using unbalanced inputs. There are pedals with balanced inputs made for vocals, but the problem is the circuits are designed for emphasizing typical vocal frequencies, not brass. Running a brass instrument through these often results in a "tinny" or some may say even comedic sound unfit for live performance.

# **SOLUTION:**

We propose building a multi effects pedal designed specifically for saxophone. Since the majority of microphones use a balanced output, we will need to implement circuit designs not only to that type of input, but also the frequency range of a saxophone. In our pedal, we will implement a preamp and equalizer that will solve the aforementioned “tinniness”. The user can elect whether or not to utilize practical performance effects - delay and reverb. The delay and reverb will be implemented on a digital signal processor while the preamp remains based in analog due to the time, cost and complexity to model distortion. The EQ will be implemented in either analog or on chip after comparing what works best for our application. All circuit components will be housed under an enclosure, durable enough for touring.

# **SOLUTION COMPONENTS:**

**ANALOG PREAMP [FIRST SUBSYSTEM]**

The preamp section will have the audio from the microphone go into 2 or 3 gain stages (this will be decided based upon the specs of our design). The preamp will be transistor based for a transparent, clear sound. This decision will help achieve the desired tonal quality for sharp attack instruments. The circuit will also be transformer-coupled at either the input/output of the preamp (This attribute gives the signal more "color" or distortion to the tone but will take up more physical space).

**EQUALIZER CIRCUIT [SECOND SUBSYSTEM]**

The EQ section of the circuit will be dispersed across 5 potentiometers focusing on 5 different bands of frequency to allow for minute adjustments from the instrument preset. Since analog and digital EQs will affect the signal differently, we need to evaluate whether this system will be implemented on the DSP or as an analog circuit.

**DIGITAL EFFECTS [THIRD SUBSYSTEM]**

The digital processing section will utilize a processor designed for handling audio applications. After the EQ, the delay and reverb can be turned on and off through latching foot switches. The delay will feature 3 potentiometers that affect the blend (wet vs dry signal), time (how long the delay trails), rate (how fast the note repeats). The reverb will feature 2 potentiometers that affect blend, decay (how long the reverb lasts).

**MULTI-INSTRUMENT ACCESSIBILITY [STRETCH GOAL]**

We can expand the capability of this pedal by making EQ presets to the desired frequency ranges of several instruments. If we have the time to do this, this will feature an interface that could easily select different options, or even user presets.

# **CRITERION FOR SUCCESS:**

- Preamp functionality - amplifies instrument signal to audible amplitude
- EQ correctly sculpts (filters) frequencies
- DSP successfully converts audio signal from the first module into useable data for the second module
- Delay effect repeats input signal at given time / periodicity
- Reverb effect trails by specified time

Decentralized Systems for Ground & Arial Vehicles (DSGAV)

Mingda Ma, Alvin Sun, Jialiang Zhang

Featured Project

# Team Members

* Yixiao Sun (yixiaos3)

* Mingda Ma (mingdam2)

* Jialiang Zhang (jz23)

# Problem Statement

Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.

# Solution

We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.

### The hardware components include:

Provided by Research Platforms

* A drone

* A ground vehicle

* A camera

Developed by our team

* An LED based docking indicator

* RF communication modules (xbee)

* Onboard compute and communication microprocessor (STM32F4)

* Standalone power source for RF module and processor

# Required Circuit Design

We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.

# Criterion for Success -- Stages

1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.

2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)

3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line

4. Drone can dock autonomously onto the ground vehicle that is slowly turning

5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking

6. Drone can pick up packages while flying synchronously to the ground vehicle

We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.

Project Videos