The classification of ceramic archaeological fragments is based on shape, dimensions, decorations, technological elements, color and material. Nowadays, all of these features are still recognized and analyzed by a skilled operator. It follows that the resulting characterization of shape and sizes of archaeological fragments is poorly reproducible and repeatable. With a view to overcome these limitations, a computer-based methodology, able to extract automatically several quantitative information from high-density discrete geometric models acquired by the laser scanning of archaeological fragments, was proposed. In this paper, the set of quantitative information obtainable is furtherly broadened, by including the segmentation of some types of morphological features, the identification of the fragment shape type, the evaluation of the longitudinal profile and the estimation of a larger set of dimensional features. Finally, a new 3D information framework is proposed to store the large variety of quantitative information extracted.

A flash Analog to Digital Converter (ADC) at 3 Giga samples per second (GS/s) was developed using QUBIC4X which is a 0.25 µm SiGeC process from NXP Semiconductors. The ADC has a bandwidth close to 1.2 GHz with a resolution of 6-bit. The full design employs a differential structure. The ADC uses a parallel architecture consisting of the following components: track and hold, comparators, and a fat tree encoder. An embedded test system will help us to validate the data transmission, and it is made by a Linear Feedback Shift Register (LFSR). An additive scrambler block allowed us to transmit the data without an accompanying clock signal. The core of the digital circuit is in Emitter-Coupled Logic (ECL). The input is adapted to 100 Ω differential, and the outputs use standard Low Voltage Differential Signaling (LVDS). The input voltage range is about 0.5 Volts. The complete system has a power consumption of 2.6 Watts and the Effective Number of Bits (ENOB) is higher than 4.4 at 1490 MHz.

In practice, the degradation process of electronic products is usually accompanied with singularities caused by intermittent and transient interference, improper conduct on singularity processing would inevitably and seriously affect the accuracy of products’ life-time prediction. Taking advantage of rapid development of AI technology recent year, a new surrogate approach based on spline function and online sequential extreme learning machine (OS-ELM) is developed in this paper, to address these issue. This fusion approach takes the cubic non - polynomial spline function as the prediction cell of the output from OS-ELM, the second derivative of the spline model can be adopted and calculated to form a series of observation frames, meanwhile, an improved particle swarm optimization (PSO) is used to optimize the parameters of ELM hidden network to help with forecasting the observation sequence and rebuild the spline function. In the verification stage, two numerical simulation examples and a practical application involving typical time series data with singularities demonstrate the effectiveness of this proposed fusion method, respectively.

A prototype of a low-cost balance board for exergaming-based tele-rehabilitation systems is introduced. The objective is to extend at patient’s home the interactive nature of the individualized rehabilitation therapies traditionally carried out at clinic. Preliminary results on posturographic tests showed encouraging performance.

The paper presents a virtual system for locating and measuring near-field electromagnetic emissions in the frequency bandwidth from 100 kHz to 500 MHz. The implemented software controls the test equipment and performs both E and H-field strength measurements. The virtual system is not intended to verify compliance with international emission regulations. However, it is helpful in characterizing the abnormal electromagnetic fields and in extracting frequency and time domain information.