Power Management System

Our company introduced the first-generation power management system during our involvement in the MARI-Sense project. The target applications are Unmanned Surface Vehicles (USVs) and Buoys. All the boards produced under the project are utilized in buoys and USVs in Cyprus.

Power Management Testing

We collected important feedback from the use of our systems in the field and we are already working on providing the second generation of this product soon.

Power Management Quick Summary

  1. Multiple Power Outputs
  2. Remote activation of outputs
  3. Standard battery input
  4. Serial Communication with the host controller
  5. Applications areas: USVs, Buoys

Contact us for more information and discuss how our products can solve your problems.

Building the FPGA Board

The second element of the TileSystem useful for edge processing is the FPGA board. This board interfaces tightly with the microcontroller of the system using a bus and also with the potential frontends for input or output.

As FPGAs are also hit by the component shortages in the semiconductor market, the design was based on AMD/Xilinx Spartan 6LX9 device, as this was in stock. The fact that the device is in a QFP package (as opposed to BGA) allows a less expensive PCB design with easier debugging, as all pins can be probed.

TileFPGA Board

The board was designed in less than 2 weeks and it came into our lab for verification. In order to test the board, we must connect it to our TileMCU through a backplane, and run the appropriate firmware that will load a valid configuration to the chip. A suitable test design with proper pin constraints was created.

After the assembly of the missing parts, the TileCUBE assembled with the microcontroller and the FPGA.

TileFPGA in Unconfigured State

The initial power-up lit our default LEDs (orange LED lit, means that the FPGA is unconfigured). We had to compile a test design with the updated pin-out from the Perseus CFE board to the TileFPGA. The design was placed at the SDCard. The firmware was adjusted to load this FPGA bitstream, so we can test the MCU and SDRAM interfaces.

FPGA in Configured State

After the FPGA was configured, we tested the Mini-FlexBus interface. We used the debugger to inspect the register area and confirmed the visibility (read operations). Registers were also written to verify that the interface is working as expected. We tested the LED state change (green color seen in the above photo) by changing the relevant bit in a register.

The next phase was to test the internal Block-RAMs. Initially the internal logic did not route the memories to the FlexBus interface (this was a design feature). the values seen in the memory space are 0xFFFF (due to the pull-ups inside the FPGA logic).

Block-RAM Access Enable and Test

Setting the respective enable bit in the control register the memory space can be written and the values are retained. Note that the default memory values seen by the Bus are 0xFFFF, but as soon as we step an instruction after the memory enable bit is set, the debugger refreshes the memory contents that are now zero.

These tests concluded the basic Mini-FlexBus interface and the internal Block-RAM interfaces. In the next post, we will test the SDRAM.

TileMCU FirmWare

The TileMCU Coldfire version completed its first set of tests, completing the integration of the USB stack from Freescale/NXP to the RTOS.

Using POSIX-style drivers (or any standard way of interfacing) we were able to replace the Serial input of the Command Line Interpreter (CLI) with the USB one.

The code change was a very simple one, replacing just the name of the driver.

Code Excerpt of Selectable Driver

So essentially we define a preprocessor macro with the name of the required driver. As both the USB-CDC and the Serial driver have the same API, they can be interchanged at will.

Here we do this on compile time. Another option would be to mirror both interfaces and have at the same time either USB or Serial I/O. For now, we decided to keep it simple as we have to test the rest of the boards to complete our hydrophone system.

USB Enumeration

At first, the USB enumeration was checked to see if the basics worked. Then we opened with our terminal the respective virtual serial port and start testing the CLI.

CLI Test Example

The CLI was tested to ensure that there were no problems using it and testing some other functions like the SD Card mounting and FAT access.

As we have completed the first phase of testing we can move on to the next boards for testing. We will return back to this board to test the Mini-FlexBus interface with the FPGA.

Building the MCU Board

The concept of a flexible and scalable system is a usual requirement for many applications. We initially started this flexibility by integrating MCUs with FPGAs on the same board. These created the initial Perseus Family Platform boards that we used for the development of applications.

Later on, during the MARI-Sense project, it was evident that the form factor should be more compact while features should be more selectable. In that spirit, we created the TileCubeTM System. Toward implementation of the platform, we designed boards for a complete hydrophone system, including microcontroller boards, FPGAs, and analog front-ends.

Due to component shortages, we were forced to design and use existing parts that we had in stock for the boards. Some boards are already designed with higher-performance microcontrollers waiting to have the new parts in hand. The flexibility of the system allows using different MCU boards in a backward-compatible manner.

The first board of the Tile System is the microcontroller. This version is based on Coldfire MCF52258.

CAD Model of TileMCU_CF

Soon the boards arrived to our lab and we started the board bring-up process and testing.

Actual TileMCU_CF Sample

We confirmed the firmware configuration changes to support the new I/O assignment and installed COFILOS to test the system. Soon we had a system working with serial communications, command line interface (CLI), etc.

First Light (Blinking) of Firmware

Next steps were to verify the SDCard interface and we tried the first time the Digilent Digital Discovery tool for capturing SPI data.

SDCard SPI Checks

Finally, we confirmed the proper operation of the SDCard and through the CLI we sneaked into the first sector of the device.

SDCard First Sector

Initially, the same board was designed with another microcontroller in mind, but due to component shortages, we had to re-spin the board with Coldfire. It was a fast race to quickly redesign the board, build it and make it work, within our deadlines. This gave us the motivation to complete the rest of the boards with the same speed, so we can see the full system ready and explore the potential of the system.

If you are interested in this platform feel free to contact us.

Edge Computing for Maritime IoT @BTS

“Breaking The Surface 2021” event was very successful and interesting. Many scientists of all over thw world presented the latest developments in archeology, marine robotics, sensing and other applications.

We were honored to present our maritime IoT platforms and share our knowledge on embedded system design and hardware acceleration.

BTS Tutorial

We discussed things like power management, showcasing the power management board for the MARI-Sense ASV, hardware acceleration, and versatile heterogeneous platforms.

Ready for the Tutorial with all Hardware
Power Management GUI
Power Management GUI

For more details see the following link.

Tutorial 2 Intro – AI Zerocaliber: Edge Computing for Maritime IoT – Breaking the Surface (


For anyone attending the multi-disciplinary conference of “Breaking The Surface 2021” event, I will be giving a tutorial on multiple topics on using embedded computing in maritime applications.

Hydrophone Processing System
Hydrophone Processing System

For more details see the following link.

Tutorial 2 Intro – AI Zerocaliber: Edge Computing for Maritime IoT – Breaking the Surface (

EMD2021: Smart-Float

European Maritime Day in Cyprus 2021

In Larnaca, 17-21 June 2021, there was this great event, where we had the opportunity to show our latest developments for the MARI-Sense project in collaboration with CMMI.

On Sunday 21 of June, at Europe Square CMMI and MARI-Sense exhibited various vehicles for maritime applications.

The underwater remote controlled robotic vehicle (ROV) has the capability to provide underwater images or use manipulators to perform delicate actions as required for research or other applications.

EMD2021: CMMI ROV Exhibit
Front view of the CMMI’s Robotic Underwater Vehicle

A smart-boat is also developed named “Kerkouros” from the ancient Cyprus ship with oars. “Kerkouros” is under development with the ultimate goal to be an autonomous surface vehicle; a challenging task for the development team.

EMD2021: Smart-Boat "Kerkouros" Exhibit
Prototype of Autonomous Smart Boat Developed at CMMI

For helping development efforts for the MARI-Sense project another platform was exhibited, the Smart-Float. The Smart Float will be used as a technology evaluation platform, where different sensors will be mounted and tested along with radio links, like WiFi, LoRa, thrusters, maneuverability, or other technologies and embedded systems. This platform will help improve the know-how of the team and will lead to new improved models. The main advantage of this platform is its low-cost and large transport capacity (more than 20Kgr net for payload). The team is already thinking of other potential applications where this platform can be used effectively. AI Zerocaliber Ltd is involved with this platform from the concept phases till now, helping with the hull and system design, and specific embedded components.

EMD2021: Smart-Float
Smart-Float Platform Shown During EMD-2021 Event

For more information on the MARI-Sense project you may check the project’s website.

MARI-Sens Logo

Running Training on Raspberry Pi

Creating a Smart Hydrophone Processing System- Software

This article is a continuation of the previous article on Creating a Smart Hydrophone Processing System – Hardware

It is assumed that you have already setup the Raspberry Pi to have remote desktop and SSH agents active. I also have setup my network to assign a specific IP to this device both for wired or wireless operation. It is also assumed that you already installed git or any other tools you require for development. We used the default Raspbian Linux. Login in with SSH should present something similar as the next picture. Of course, you may use a remote desktop environment; select which best fits your taste.

Raspberry Pi Login Screen
Raspberry Pi Login Screen

Notice that this is a Linux 5 kernel. This is important mostly for driver’s compatibility or support with the DAC+ADC Pro board. However, we did not face any real issues with our setup.

Our development plan was based on using python tools and machine learning for fast prototyping.  We based our classification code on this GitHub repo: seth814/Audio-Classification: Code for YouTube series: Deep Learning for Audio Classification (

Python can get messy with packages and proper system configurations. Note that the Raspberry Pi image comes with both Python2 and Python3 interpreters. Writing simple python runs the 2.7 version while running python3 calls the Python 3.x interpreter. Keep this in mind.

In our case, we used the Python3 setup. First, we created a virtual environment to install packages. There are many ways to do the setup. You may try the anaconda system (although the link below for TensorFlow says that this will not work), which will take care of any dependencies and install all the packages, or you can follow on here and see the more tedious manual approach. The idea is to have a system set up in our Raspberry that matches the one we use in our desktop environments to ensure maximum compatibility and help us with testing.

Download the virtualenv package:

$ sudo pip3 install virtualenv

Then we can select a directory to do our development and create the virtual environment there:

 $ cd UnderwaterSoundProcessing
 $ virtualenv audioml
 $ cd audioml
 $ source bin/activate
 … do something…
 $ Deactivate 

If you check the requirements.txt in the github repo, you will notice a long list of items that must be installed along with their version numbers. If you try the simple command:

$ pip3 install -r requirements.txt

You will fail miserably. The reason is simple. Kapre requires TensorFlow version at least 1.15, but default TensorFlow for Raspberry is 1.14.

TensorFlow 2 cannot be installed by default on RPB Pi4. We followed the instructions from this link:

The above link states two things:

  1. The script used is on a google drive (check contents, security and stability issue)
  2. TFLite can provide significant improvements in prediction. You may convert your normal models to Tflite for faster processing. Need to test on each application if accuracy is maintained.
  3. When installing in venv do not use sudo. In case you did (as I did), reinstall (last step)  tensorflow without the sudo. Test that Tensorflow is installed from python before continuing.
  4. If in python interpreter you are able to import the tensorflow package as stated in the link you are good to go.

For Tensorlow and Numpy packages we install the ATLAS library.

$ sudo apt install libatlas-base-dev

Another issue we faced was the LLVM library. Numba 0.48 requires v7 of LLVM and not v9.

 $ sudo apt install llvm-7 
 $ LLVM_CONFIG=llvm-config-7 pip3 install llvmlite 

Now running the dependencies command should work:

$ pip3 install -r requirements.txt
Installing packages for ML
Installing packages for ML

We run our predict module to see it working. We had already trained previously on a desktop PC the machine learning network to classify a mixture of sea sounds taken in a previous experiment and from the web.

 $ python 
Running Prediction on Raspberry Pi
Running Training on Raspberry Pi

All these are nice, but still where is the hydrophone? Next steps describe the process to properly interface the DAC+ADC Pro module.

We followed the procedures from the manufacturer: Configuring Linux 4.x or higher | HiFiBerry

Although the kernel is 5, we needn’t to do the work-around presented for the EEPROM. First, we tested the playback. Please note that the device is not the default, but rather the sysdefault as seen after the next command.

Sound Devices List
Sound Devices List

The following commands will output sound from the left and right channel respectively.

 $ speaker-test -D sysdefault -t sine -f 440 -c 2 -s 1
 $ speaker-test -D sysdefault -t sine -f 440 -c 2 -s  2 

Now to test record try the following command for 3 seconds recording, Signed 16, wav format, and show Vumeter.

$ arecord -D sysdefault -r 44100 -d 3 -f S16 -t wav -V mono test.wav

You will notice that the hydrophone signal is very low. This is expected and we need to boost the signal with the following command:

$ amixer -D sysdefault cset name='ADC Capture Volume' 96,96

The values of this command are steps between 0 and 104 and will set ADC volume 0.5db/step. So 96 is about 48dB. You may adjust this value to a lower level depending on the sensitivity. A 33dB gain should work just fine. You may retry to record again and observe the VU meter levels.

For python to access the audio device, we installed the ALSA audio:

 $ sudo apt-get install libasound2-dev
 $ pip install pyalsaaudio 

To ensure that the same exact code is used either for off-line prediction and on-line prediction, we used a file wrapper in python. The sampling function performs a sampling for a duration (like 1 second) and stores the result in a file. Then the main loop acquires this file and processes it like it does off-line content.

def readdaq(self):
     loops = int(self.sample_dur_seconds * round( (self.fs_Hz / self.chunk) ) )
     frames = []
     sample = np.array(frames)
     while loops > 0:
         loops -= 1
         # Read data from device
         l, data =
         if l>0:
             if l!= self.chunk:
                 print("Sampling Error ", l)
     wf ='/mnt/tmpfs/sample.wav', 'wb')

     batch = self.readfile('/mnt/tmpfs/sample.wav')
     return batch

Note that the file is saved on a temporary location. This is a ram drive created as follows:

 $ sudo mkdir /mnt/tmpfs
 $ sudo mount -o size=1M -t tmpfs none /mnt/tmpfs 

The reason of using a ram drive is that we do not want to wear out our SDcard, or have slow-downs due to file system activity. We use this scratchpad area to write the samples and use them for processing. Then the sample is overwritten by the next sample.

This way we  streamline the testing process and are able to run the same code with or without hydrophone, either on desktop, or on Raspberry Pi.


Using open-source software and off-the-shelf hardware we are able to have a platform for sound classification using machine learning. We created a uniform testbed that can be used to test either on-line or off-line the methods employed for evaluation purposes.


The SMART Cable was developed through the SMART Cable Project. This project
is part of the Research & Innovation Foundation Framework Programme RESTART
2016-2020 for Research, Technological Development and Innovation and co-funded
by the Republic of Cyprus and the European Regional Development Fund with grant
number ENTERPRISES/0916/0066.

For more information you may visit: SMART Cable :
Cyprus Subsea Consulting & Services (

This project is also part of the MARI-Sense Project INTEGRATED/0918/0032. The MARI-Sense project develops intelligent systems that allow human operators to make sense of the complex maritime environment for applications including transport and shipping, coastal tourism, search and rescue, and maritime spatial planning.


H2a Hydrophone Connected

Creating a Smart Hydrophone Processing System- Hardware

In scientific projects, it is often needed to sample sounds from remote locations, for classification or other purposes. As data link rates may be low or unreliable, transmitting raw samples to inland processing centers may not be an option. An alternative is to do off-line processing in batches. This means that raw data are stored in non-volatile memory untill a physical visit replaces the memory media and uses the first batch for processing after any events occurred. It is obvious that a module that performs real-time classification will have to send a very small amount of data to, possibly cloud-based data centers.

Towards this direction, we will show how to build the basic elements needed for such systems using simple parts and open-source software packages, to provide a platform to build classification systems for hydrophone sensors.


The hardware we are going to use for this example is based on Raspberry Pi 4.

Raspberry Pi 4 module
Raspberry Pi module

To interface we use a hydrophone an analog to-digital converter is needed. For our experiments we used the DAC+ADC Pro from HiFiBerry (HiFiBerry DAC+ ADC Pro | HiFiBerry). This module offers both audio input and output. In our case, we are only interested for the audio.

HiFiBerry DAC+ ADC Pro
HiFiBerry DAC+ ADC Pro

This HAT is placed on top of the Raspberry PI. To mount properly the module, we used the following items as seen in the next picture.

ADC and Raspberry Pi side to side
ADC and Raspberry Pi side to side

We used two of the nylon stands to hold the front side of the HAT. We skipped the back nylon stands as they wouldn’t allow to fully insert the connector. In the picture above we have assembled the front stands in the Raspberry Pi 4 and we are ready to connect the HAT. Also note the small nylon bolts for securing the HAT and two jumpers we will need for the hydrophone.

Next, we connect the HAT on top of the Raspberry PI module. We fully insert the connector; we should not see any naked pins protruding from the bottom as seen in the picture.

ADC on top of Raspberry Pi, connector side
ADC on top of Raspberry Pi

We then place the bolts to be ready for tightening the HAT. The HAT will have a slight inclination as the front stands will keep it slightly upwards, in respect with the fully inserted connector.

ADC front side mount
ADC front side mount

After completing the mechanical fit, comes the jumper settings. The two jumpers J1 and J3 must be placed to provide power to the hydrophone. This is because our hydrophones are behaving like condenser microphones and they do not generate their own voltage. Make sure you check which type of hydrophone you use as depending of the hydrophone technology you may not need this power; Failing to properly identify the type of hydrophone may damage either your equipment.

ADC Jumper Settings
ADC Jumper Settings

Our hydrophone is the H2a Aquarian Audio from Aquarian Hydrophones.  Next pictures show the hydrophone and its connector ready to be connected on the platform. H2a Hydrophone (

H2a Hydrophone
H2a Hydrophone Just Before Connection

Next, we connect the hydrophone to the 3.5mm Jack input of the DAC+ADC Pro HAT and we are ready to go from the hardware standpoint.

H2a Hydrophone Connected
H2a Hydrophone Connected


This ends the first part of the smart Hydrophone, concluding the Hardware. A setup showing how to integrate off the shelf components to support Hydrophone sound sampling from the sea was presented.


The SMART Cable was developed through the SMART Cable Project. This project is part of the Research & Innovation Foundation Framework Programme RESTART 2016-2020 for Research, Technological Development and Innovation and co-funded by the Republic of Cyprus and the European Regional Development Fund with grant number ENTERPRISES/0916/0066.

For more information you may visit: SMART Cable : Cyprus Subsea Consulting & Services (

This project is also part of the MARI-Sense Project INTEGRATED/0918/0032. The MARI-Sense project develops intelligent systems that allow human operators to make sense of the complex maritime environment for applications including transport and shipping, coastal tourism, search and rescue, and maritime spatial planning.

MARISENSE Experiment #1

Last May we conducted the first experiment of the program. Purpose was to create a dataset of underwater sounds and respective surface images, that would be used to develop and test our algorithms. Various kinds of equipment were used, from RIBs, Sailboats up to underwater ROVs and gliders. You can take a taste of the process on how the experiment was conducted and at the same time hear the respective underwater sounds.