A Preliminary Study on SVM based Analysis of Underwater Magnetic Signals for Port Protection

People who attend to the problem of underwater port protection usually use sonar based systems. Recently it has been shown that integrating a sonar system with an auxiliary array of magnetic sensors can improve the effectiveness of the intruder detection system. One of the major issues that arise from the integrated magnetic and acoustic system is the interpretation of the magnetic signals coming from the sensors. In this paper a machine learning approach is proposed for the detection of divers or, in general, of underwater magnetic sources. The research proposed here, by means of a windowing of the signals, uses Support Vector Machines for classification, as tool for the detection problem. Empirical results show the effectiveness of the method

Fingerprint-enhanced capacitive-piezoelectric flexible sensing skin to discriminate static and dynamic tactile stimuli

nspired by the structure and functions of the human skin, a highly sensitive capacitive‐piezoelectric flexible sensing skin with fingerprint‐like patterns to detect and discriminate between spatiotemporal tactile stimuli including static and dynamic pressures and textures is presented. The capacitive‐piezoelectric tandem sensing structure is embedded in the phalange of a 3D‐printed robotic hand, and a tempotron classifier system is used for tactile exploration. The dynamic tactile sensor, interfaced with an extended gate configuration to a common source metal oxide semiconductor field effect transistor (MOSFET), exhibits a sensitivity of 2.28 kPa−1. The capacitive sensing structure has nonlinear characteristics with sensitivity varying from 0.25 kPa−1 in the low‐pressure range (<100 Pa) to 0.002 kPa−1 in high pressure (≈2.5 kPa). The output from the presented sensor under a closed‐loop tactile scan, carried out with an industrial robotic arm, is used as latency‐coded spike trains in a spiking neural network (SNN) tempotron classifier system. With the capability of performing a real‐time binary naturalistic texture classification with a maximum accuracy of 99.45%, the presented bioinspired skin finds applications in robotics, prosthesis, wearable sensors, and medical devices

Fast and Memory-Efficient Import Vector Domain Description

One-class learning is a classical and hard computational intelligence task. In the literature, there are some effective and powerful solutions to address the problem. There are examples in the kernel machines realm, Support Vector Domain Description, and the recently proposed Import Vector Domain Description (IVDD), which directly delivers the sample probability of belonging to the class. Here, we propose and discuss two optimization techniques for IVDD to significantly improve the memory footprint and consequently to scale to datasets that are larger than the original formulation. We propose two strategies. First, we propose using random features to approximate the gaussian kernel together with a primal optimization algorithm. Second, we propose a Nystr\uf6m-like approximation of the functional together with a fast converging and accurate self-consistent algorithm. In particular, we replace the a posteriori sparsity of the original optimization method of IVDD by randomly selecting a priori landmark samples in the dataset. We find this second approximation to be superior. Compared to the original IVDD with the RBF kernel, it achieves high accuracy, is much faster, and grants huge memory savings

An Ab Initio Local Principal Path Algorithm

We introduce an improved version of the principal path method, an algorithm conceived to find smooth paths between objects in space. Some key steps of the algorithm have been changed, making the solution intrinsically local and preventing it from being attracted by a global manifold. Judiciously performing the initialization step with the Dijkstra algorithm and a proper metric, the functional now only performs a final refinement of the initial solution. Hence the algorithm is stabler as the space of possible solutions has been considerably reduced with respect to the original method. We tested the proposed algorithm in 2D toy data sets (to understand the behaviour) and in high-dimensional data sets. Compared to the previous version of the algorithm, we obtained significantly stabler and more realistic generated samples

Efficient Approximate Regularized Least Squares by Toeplitz Matrix

Machine Learning based on the Regularized Least Squares (RLS) model requires one to solve a system of linear equations. Direct-solution methods exhibit predictable complexity and storage, but often prove impractical for large-scale problems; iterative methods attain approximate solutions at lower complexities, but heavily depend on learning parameters. The paper shows that applying the properties of Toeplitz matrixes to RLS yields two benefits: first, both the computational cost and the memory space required to train an RLS-based machine reduce dramatically; secondly, timing and storage requirements are defined analytically. The paper proves this result formally for the one-dimensional case, and gives an analytical criterion for an effective approximation in multidimensional domains. The approach validity is demonstrated in several real-world problems involving huge data sets with highly dimensional dat

K-means clustering for content-based document management in intelligence

Text-mining methods have become a key feature for homeland-security technologies, as they can help explore effectively increasing masses of digital documents in the search for relevant information. This chapter presents a model for document clustering that arranges unstructured documents into content-based homogeneous groups. The overall paradigm is hybrid because it combines pattern-recognition grouping algorithms with semantic-driven processing. First, a semantic-based metric measures distances between documents, by combining a content-based with a behavioral analysis; the metric considers both lexical properties and the structure and styles that characterize the processed documents. Secondly, the model relies on a Radial Basis Function (RBF) kernel-based mapping for clustering. As a result, the major novelty aspect of the proposed approach is to exploit the implicit mapping of RBF kernel functions to tackle the crucial task of normalizing similarities while embedding semantic information in the whole mechanism. In addition, the present work exploits a real-world benchmark to compare the performance of the conventional k-means algorithm and recent k-means clustering schemes, which apply Johnson-Lindenstrauss-type random projections for a reduction in dimensionality before clustering. Experimental results show that the document clustering framework based on kernel k-means provide an effective tool to generate consistent structures for information access and retrieval

Efficient implementation of SVM training on embedded electronic systems

The implementation of training algorithms for SVMs on embedded architectures differs significantly from the electronic support of trained SVM systems. This mostly depends on the complexity and the computational intricacies brought about by the optimization process, which implies a Quadratic-Programming prob-lem and usually involves large data sets. This work presents a general approach to the efficient implementation of SVM training on Digital Signal Processor (DSP) devices. The methodology optimizes efficiency by suitably adjusting the established, effective Keerthi\u2019s optimization algorithm for large data sets. Besides, the algorithm is reformulated to best exploit the computational features of DSP devices and boost efficiency accordingly. Experimental results tackle the training problem of SVMs by involving real-world benchmarks, and confirm both the computational efficiency of the approach

Solubility Advantage of Amorphous Ketoprofen. Thermodynamic and Kinetic Aspects by Molecular Dynamics and Free Energy Approaches

Thermodynamic and kinetic aspects of crystalline (c-KTP) and amorphous (a-KTP) ketoprofen dissolution in water have been investigated by molecular dynamics simulation focusing on free energy properties. Absolute free energies of all relevant species and phases have been determined by thermodynamic integration on a novel path, first connecting the harmonic to the anharmonic system Hamiltonian at low T and then extending the result to the temperature of interest. The free energy required to transfer one ketoprofen molecule from the crystal to the solution is in fair agreement with the experimental value. The absolute free energy of the amorphous form is 19.58 kJ/mol higher than for the crystal, greatly enhancing the ketoprofen concentration in water, although as a metastable species in supersaturated solution. The kinetics of the dissolution process has been analyzed by computing the free energy profile along a reaction coordinate bringing one ketoprofen molecule from the crystal or amorphous phase to the solvated state. This computation confirms that, compared to the crystal form, the dissolution rate is nearly 7 orders of magnitude faster for the amorphous form, providing one further advantage to the latter in terms of bioavailability. The problem of drug solubility, of great practical importance, is used here as a test bed for a refined method to compute absolute free energies, which could be of great interest in biophysics and drug discovery in particular