CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Exploitable results
The principal feature of the tool is the capability of early detection of crossing an interface between layers of different tissues and automatically stopping the feed, according to the specification of the operator.
The demonstrator has the features listed below:
Effectiveness of penetration control: the maximum protrusion is well below the specified limits in all the modes, thus a great improvement has been obtained with respect to the existing drills;
Friendliness: since the surgeon interacts with the tool exclusively by the footswitch he must concentrate only on positioning the drill bit, maintaining the correct orientation and exerting a sufficient amount of thrust force. Some failures have been observed only when the surgeon does not exert a sufficient amount of thrust force on the drilling tool. For this reason, a short training for unexperienced operators is required prior to use of the tool for the first time;
Patient safety: the system is intrinsically safe because the drilling tool is handled directly by the surgeon who can interrupt the operation at any time. Moreover, in case of failure both the surgeon or the assistant can stop the tool by means of an emergency button.In the worst case, the tool behaves like a traditional one with no automatic penetration control capability, so the surgeon is the sole person responsible for the execution of the procedure;
Medical staff safety: the system gives information in real time regarding the drill bit position inside the bone by means of a graphical interface without the need of stopping the procedure and without using X-rays. This significantly reduces the professional risk of the members of the medical staff;
Modularity of control software: the control software has been realised in C++ language using a library of purposely written real time classes where each class executes a particular control function. In fact, each class might be considered as a hardware block and the main code as the link of these blocks.The core of the control algorithm is also seen as a block that might be changed if a better algorithm is available.In this manner, the control software becomes extremely manageable and so its updating is very easy.
We have developed a novel and mathematically sound computational framework, namely fuzzy lattice learning framework (FLL) or FL-framework, within which we have devised various intelligent schemes for learning and decision making, especially for clustering and classification. One of them is the FLL scheme whose neural implementation, namely fuzzy lattice neural network (FLN), enhances the scope of Grossberg's adaptive resonance theory (ART).
The main advantages of the FLL/FLN are:
it learns fast, requires no tuning, and can deal jointly with disparate data such as numbers, waveforms, propositional variables, fuzzy sets, images, etc;
it can handle input-intervals instead of handling solely individual input-points;
it is easily implementable in hardware, while its neural implementation allows for fast parallel processing;
it is reliable and noise resistant.
Application of the FLL/FLN to different benchmark data clustering and classification problems showed its outstanding performance compared to various other schemes.
The predictive modular decision systems (PREMODS) is a class of algorithms applicable to time series classification and prediction and nonlinear systems identification. PREMODS consists of a number of user-defined predictive modules (linear, neural, etc) and a decision module which compares and combines the predictive module outputs using Bayesian, fuzzy, learning automata and other algorithms.
PREMODS is characterized by:
modularity (ie its components can be removed and independently retrained);
recursive, on-line operation;
high robustness to noise;
good scaling properties (the complexity of the algorithm scales as a linear function of the size of the model);
it can be handled by non-technical people.
PREMODS has been applied with good results to phoneme and enzyme classification, forecasting of electric loads and alternating current (AC) motor parameter estimation. Other potential applications include industrial fault diagnosis, medical diagnosis, prediction of financial data, structural identification, etc.
Searching for OpenAIRE data...
There was an error trying to search data from OpenAIRE
No results available