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The goal is to determine the real-time position and velocity of various moving objects based on Doppler travel time and displacement measurements provided by a passive radar.

**Problem Analysis**

The project solution requires analysis of the information received from the passive radar and exclusion of false and lost measurements, as well as analysis of the unknown association between measurements and objects the measurement from multiple (multi-static) sources.

**Method of Intervention and Solution**

We proposed a solution that combines optimization techniques for trace initialization with advanced multi-target tracking and sensor fusion techniques.

**Results and Benefits**

The methods that were used allow to obtain the effective identification of objects.

Object tracking in real time: find out how Data Fusion techniques allow you to identify and track objects using radar data analysis!

Real-Time Object Tracking

Object tracking using passive radar

Real Time Object Tracking

Aerospace & Defense

The objective of this research project, conducted in collaboration with the Policlinico Gemelli, is the development of an automatic system for the analysis of EEG (ElectroEncephaloGram) signals in order to predict seizures in patients resistant to drug treatment.

**The Challenge**

The solution requires the capture and recognition of EEG signal changes typical of the onset of a pre-critical phase. This allows the necessary precautions to be activated to avoid or manage the actual seizure. One of the difficulties to be faced in the development of an automated solution is the strong noise of the EEG signals.

**The Solution**

We filter the EEG signals managing to extract the most relevant features through autoregressive models (AR). Based on the features thus highlighted we build classification models of type Support Vector Machines (SVM), which we train to distinguish between a normal phase and a pre-critical phase.

**Results and Benefits**

The predictive model helps to identify the pre-critical phase of a seizure.

Find out how a mathematical model uses electroencephalogram data to predict seizures onset and other adverse events: visit the website!

Forecasts of Adverse Events from Medical Data

Identify pre-critical phase from EEG data

Adverse Events Prediction

Healthcare & Pharma

The goal is to develop a software able to support the operator in the ordinary maintenance operations by automatically identifying the engines with an higher risk and therefore higher priority.

**The Challenge**

In order to classify the risk category of the device we use the available data from the analysis of chemical and physical properties of the oil performed on samples taken at different stages of the engine life. The main difficulty is due to an incomplete and inconsistent data history.

**The solution**

We developed a classification algorithm on Support Vector Machine (SVM). To handle the missing data we built an ad-hoc modification of the SVM kernel was used to handle the missing data in order to maximize the separation between classes.

**Results and Benefits**

Our classifier is able to correctly recognize the risk category of the engine with more than 85% accuracy. In addition, the model is interpretable, thus allowing the identification of the chemical and physical parameters considered most relevant for classification.

Find out how to build a classification algorithm for predictive maintenance even in the absence of data. Trust the experience of Deix!

Classification Algorithms in Predictive Maintenance

Predictive Maintenance using engine oil measurements

Engine Classification

The goal is to design the double hull of a catamaran in order to reduce as much as possible the expected value of the average resistance in the field of irregular waves increasing, at the same time, the maneuverability of the boat.

**The Challenge**

We want to take advantage of the HF-URANS (High-Fidelity Unsteady Reynolds-Averaged Navier-Stokes) simulator to get very accurate results of the boat design performance. The challenge is to insert the simulation model into an automatic optimization procedure while keeping computational costs and time low.

**The Solution**

By training a network of radial basis functions, we build a surrogate model (called SRBF) of the simulator. This model has very low computation times but accuracies comparable to those of the high-fidelity model. We can thus exploit it within a global and multi-objective optimization method that, properly hybridized with local techniques, quickly produced an optimal solution.

**Results and Benefits**

The new simulator allows to support the design through the use of a virtual prototype for rapid simulation and an optimizer to identify the optimal parameters.

How to integrate a high-fidelity simulator and an optimizer to support virtual prototyping design? Find out more on our website!

Virtual Prototyping and Simulation

Design of the hull of an open-sea racing catamaran

Virtual Prototyping

We want to improve the reaction time (delay) to the on/off command of AMOLED displays.

**The Challenge**

A number of design variables (PMOS and NMOS transistor characteristics) can be acted upon to minimize the reaction time. Circuit performance, however, also depends on temperature, operating voltage, and process variables subject to uncertainty and statistical fluctuations related to the electronic component manufacturing process.

**The Solution**

Using machine learning techniques, a "yield simulator" has been defined, that is a software tool that allows to accurately predict the percentage of integrated circuits "in full working order". The simulator is then used to calculate the objective function to be minimized and, in order to take into account the variability of the parameters, the algorithm uses robust optimization techniques, which can take into account the variance of the process and manage the "worst case scenario".

**Results and Benefits**

Leveraging robust optimization techniques we identify the optimal configuration without the need to produce prototypes on which to perform tests. This significantly reduces design time and costs.

As part of robust optimization, discover how uncertainty quantification techniques enable optimization of variable parameters.

Robust Optimization - AMOLED Display Design

Robust optimization of a DC-DC converter for AMOLED displays

AMOLED Display Design

The goal is to optimize the design of an industrial three-phase induction motor (7.5 KW) used for industrial lifts and hoists.

**The Challenge**

The objective is to improve the performance of the motor by acting on a number of variables that define the shape and size of the rotor and stator and the windings in the stator slots, while simultaneously taking into account several factors: cost of construction, energy efficiency, power factor, and start-up current.

**The Solution**

Given a configuration, we calculate the performance of the motor using finite element simulation software. Through a purpose-developed interface, the simulator is embedded within a multi-objective optimization procedure. The optimizer takes into account all performances simultaneously arriving at a set of optimal solutions.

**Results and Benefits**

Multi-objective optimization allows to propose different engine designs that represent the best tradeoffs between constraints and performance.

Find out how to manage multiple factors in the design of an electric motor. Visit our page dedicated to multi-objective optimization!

Multi-Objective Optimization - Electric Motor Design

Multi-objective design of an electric motor

Electric Motor Design

Manufacturing

We want to analyze the weather forecasts to make an estimate of the wind energy offers that the various operators make to the "next day market", in Italy.

**The Challenge**

Predict the offer strategies of the operators and plan an offer taking into account bonuses and penalties that the market regulation foresees according to the demand/supply ratio.

**The Solution**

We use clustering techniques to reduce the dimensionality of the problem by capturing useful information from weather forecasts and train artificial intelligence models to model operator behavior based on "next day market" historical data.

**Results and Benefits** 

The analysis techniques adopted enable the planning of an "intelligent" bidding strategy.

It is possible to use weather forecast to anticipate energy market trends thanks to artificial intelligence: visit the website and find out how!

Renewable Energy Market Forecast

Forecasting fluctuations in the renewable energy market

Telco & Utilities

We operate in different industries collaborating with our clients to leverage their domain knowledge and find effective customized solutions.

Case Studies and Successful Applications

We work in different sectors collaborating with our clients to take advantage of their domain knowledge. Using mathematical modeling we can abstract the problem and find the most suitable solution for each context.