Alessandro Massaro

Bio: Alessandro Massaro is an academic researcher from Istituto Italiano di Tecnologia. The author has contributed to research in topics: Antenna (radio) & Dielectric. The author has an hindex of 19, co-authored 199 publications receiving 1178 citations. Previous affiliations of Alessandro Massaro include National Research Council & Polytechnic University of Bari.

Design and Characterization of a Nanocomposite Pressure Sensor Implemented in a Tactile Robotic System

Abstract: In this paper, we present the implementation of a new class of optical pressure sensors in a robotic tactile-sensing system based on polydimethylsiloxane (PDMS). The sensor consists of a tapered optical fiber, where an optical signal goes across, embedded into a PDMS-gold nanocomposite material (GNM). By applying different pressure forces onto the PDMS-based nanocomposite, changes in the optical transmittivity of the fiber can be detected in real time due to the coupling between the GNM and the tapered fiber region. The intensity reduction of a transmitted light is correlated to the pressure force magnitude. Light intensity is converted into an electric signal by a system suitable for robotic implementation. High sensitivity using forces by applying weights of a few grams is proved. Sensitivity on the order of 5 g is checked. A detailed algorithm for the detection of roughness and shapes by means of a robotic finger is proposed.

In situ formation and size control of gold nanoparticles into chitosan for nanocomposite surfaces with tailored wettability.

Abstract: The in situ formation of gold nanoparticles into the natural polymer chitosan is described upon pulsed laser irradiation. In particular, hydrogel-type films of chitosan get loaded with the gold precursor, chloroauric acid salt (HAuCl4), by immersion in its aqueous solution. After the irradiation of this system with increasing number of ultraviolet laser pulses, we observe the formation of gold nanoparticles with increasing density and decreasing size. Analytical studies using absorption measurements, atomic force microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy of the nanocomposite samples throughout the irradiation procedure reveal that under the specific irradiation conditions there are two competing mechanisms responsible for the nanoparticles production: the photoreduction of the precursor responsible for the rising growth of gold particles with increasing size and the subsequent photofragmentation of these particles into smaller ones. The described method allows the loca...

A Portable Optical Sensor for Sea Quality Monitoring

Abstract: In this paper, we propose the modeling, the design, and the development of a high sensitivity cheap optical sensor for chlorophyll a and water transparency based on chlorophyll fluorescence and turbidity due to scattering for in situ monitoring of trophic status of seawater. The sensor is designed in order to detect very low chlorophyll concentration and low level turbidity by means of a numeric lock-in amplifier technique, with a common resonant input stage. The sensor is designed and implemented by considering a proper layout suitable for a good light source coupling. The presented prototype integrates on a board two LED sources emitting red and blue light to measure both chlorophyll-a and turbidity. The system assures a chlorophyll sensitivity of 1 mV/2.5 μg/l, a detectivity of 0.2 μg/l, with a 40-dB minimum attenuation of all the other light sources. It is attractive with respect to commercial systems because it guarantees considerable reliability with low manufacturing costs.

Accurate Time-Domain Modeling of Reconfigurable Antenna Sensors for Non-Invasive Melanoma Skin Cancer Detection

Abstract: The full-wave electromagnetic characterization of reconfigurable antenna sensors for non-invasive detection of melanoma-related anomalies of the skin is presented. To this end, an enhanced locally conformal finite-difference time-domain procedure, based on the definition of effective material parameters and a suitable normalization of the electromagnetic field-related quantities, is adopted. In this way, an insightful understanding of the physical processes responsible for the performance of considered class of devices is achieved. This in turn is important in order to enhance the structure reliability, optimizing the design cycle. A suitable microelectromechanical-system-based sensor layout is finally discussed in details.

LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction

Abstract: The paper is focused on the application of Long Short-Term Memory (LSTM) neural network enabling patient health status prediction focusing the attention on diabetes. The proposed topic is an upgrade of a Multi-Layer Perceptron (MLP) algorithm that can be fully embedded into an Enterprise Resource Planning (ERP) platform. The LSTM approach is applied for multi-attribute data processing and it is integrated into an information system based on patient management. To validate the proposed model, we have adopted a typical dataset used in the literature for data mining model testing. The study is focused on the procedure to follow for a correct LSTM data analysis by using artificial records (LSTM-AR-), improving the training dataset stability and test accuracy if compared with traditional MLP and LSTM approaches. The increase of the artificial data is important for all cases where only a few data of the training dataset are available, as for more practical cases. The paper represents a practical application about the LSTM approach into the decision support systems (DSSs) suitable for homecare assistance and for de-hospitalization processes. The paper goal is mainly to provide guidelines for the application of LSTM neural network in type I and II diabetes prediction adopting automatic procedures. A percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach, comparing results with up-to-date MLP works. The LSTM-AR- neural network can be applied as an alternative approach for all homecare platforms where not enough training sequential dataset is available.

Photonic crystals

Abstract: The term photonic crystals appears because of the analogy between electron waves in crystals and the light waves in artificial periodic dielectric structures. During the recent years the investigation of one-, two-and three-dimensional periodic structures has attracted a widespread attention of the world optics community because of great potentiality of such structures in advanced applied optical fields. The interest in periodic structures has been stimulated by the fast development of semiconductor technology that now allows the fabrication of artificial structures, whose period is comparable with the wavelength of light in the visible and infrared ranges.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Soft Robotic Grippers.

Abstract: Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

Laser Synthesis and Processing of Colloids: Fundamentals and Applications

Abstract: Driven by functionality and purity demand for applications of inorganic nanoparticle colloids in optics, biology, and energy, their surface chemistry has become a topic of intensive research interest. Consequently, ligand-free colloids are ideal reference materials for evaluating the effects of surface adsorbates from the initial state for application-oriented nanointegration purposes. After two decades of development, laser synthesis and processing of colloids (LSPC) has emerged as a convenient and scalable technique for the synthesis of ligand-free nanomaterials in sealed environments. In addition to the high-purity surface of LSPC-generated nanoparticles, other strengths of LSPC include its high throughput, convenience for preparing alloys or series of doped nanomaterials, and its continuous operation mode, suitable for downstream processing. Unscreened surface charge of LSPC-synthesized colloids is the key to achieving colloidal stability and high affinity to biomolecules as well as support materials,...