The evolution of the new generations of Internet of Things (iot) has opened the door to an ever wider use of Artificial Intelligence (AI) in the context of the optimization of resources and the creation of more modern solutions close to contemporary needs. However, balancing the application of AI in this context becomes crucial to ensure a green and sustainable approach.

The adoption of Machine Learning (ML) models is the cornerstone of this new era. This stochastic and heuristic architecture allows you to collect structured information, optimize resource management, adapt in real time and provide high quality services.

The current challenge is to effectively integrate AI in the new generations of IoT, in order to maximize the efficiency and sustainability of applications:

What solutions have we made in Pragma Etimos?

Biometric Neural Intelligence Integration

The adoption of Artificial Intelligence (AI) models is the basis of a new paradigm for the design and optimization of high efficiency solutions.

An architecture based on the AI in fact favors the collection of information, the strategic management of the resources, the automatic adaptation Real Time and the supply of relative services.

The new frontier of research is aimed at replacing the traditional physical components inside the iot with digital analogues based on specially trained neuronal models. The adoption of such solutions will bring significant advantages in terms of flexibility, performance and resilience of new technologies, essential conditions in a path of growth towards a sustainable Green Data future.

At Pragma Etimos, we continue to develop pioneering, innovative and functional audio recognition solutions. Voice biometrics is the new technological frontier that allows identification and authentication by voice in a much safer way than traditional systems.

The developed platforms, the result of years of research, called “Polyphonic” and “Polyphonic Forensic” are composed of multi-level tools that define the “vocal footprints” and recognize voices extrapolating them from audio files, regardless of quality and source.

Very often, those dealing with vocal recordings have great difficulty deciphering sounds in noisy and/or disturbed contexts, making the work complicated or even impossible. One of the tools of Polyphonic is the cleaning of the audio, so as to eliminate the background noise or other elements, such as wheezing and breathing etc…

Thanks to Machine Learning algorithms you can get to have a new file where the voice is extremely clear and sharp. In fact, the integration of Biometric Intelligence solutions based on neural models inside iot allows you to perform Quality Check and Audio Cleansing activities while recording an audio, obtaining biometric recognition and sharing the functionality of several iot inside a single device thanks to the substitution of physical elements with Neural components.

Artificial Intelligence techniques also have the ability to optimize the energy management processes of iot, intelligently managing charging times and consumption, thus increasing the time of use and efficiency.

The technical integration of neural networks in Polyphonic Forensic

The integration of the technology, described above, in the modern solutions used for the recognition of sounds and people, is proving successful in terms of performance and accuracy of results. In particular, the possibility of splitting the task into different components/modules, allows to apply the parallelism of calculation necessary to allow a real-time analysis of audio streams.

Fundamental aspect of this computing architecture is the integrated use of CUDA libraries, able to optimize the capabilities of Nvidia cards.

With the exponential increase of communications, also due to the remarkable improvement of the TLC infrastructures and the lowering of the costs of connectivity of the various communication players, the activity of recognition of speakers linked to the world of Forensics has significantly increased.

This phenomenon, together with the notorious scarcity of resources available to the PG, is making necessary the implementation of a new generation of algorithms in the approach to audio analysis, able to comply with internationally recognized procedures in the scientific world of forensics, with an approach based on techniques of AI and neuronal models.

In particular, it is necessary to provide the operator with a semi-automatic system able to speed up the main operations (easily subject to the factor of “human error”) and to report to the expert its role of synthesis of the analysis.


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