This paper introduces the Human Action Multi-Modal Monitoring in Manufacturing (HA4M) dataset, a collection of multi-modal data relative to actions performed by different subjects building an Epicyclic Gear Train (EGT). In particular, 41 subjects executed several trials of the assembly task, which consists of 12 actions. Data were collected in a laboratory scenario using a Microsoft® Azure Kinect which integrates a depth camera, an RGB camera, and InfraRed (IR) emitters. To the best of authors’ knowledge, the HA4M dataset is the first multi-modal dataset about an assembly task containing six types of data: RGB images, Depth maps, IR images, RGB-to-Depth-Aligned images, Point Clouds and Skeleton data. These data represent a good foundation to develop and test advanced action recognition systems in several fields, including Computer Vision and Machine Learning, and application domains such as smart manufacturing and human-robot collaboration.

We present a compact, semiempirical model of carbon nanotube field effect transistor easily implementable in simulation SPICE software to design analog and digital circuits. The model is based on analytical approximations and parameters extracted from quantum mechanical simulations, having compared the results with those of the numerical model online available and of experimental data.

We present a compact, semi-empirical model of Carbon Nanotube Field Effect Transistors (CNTFETs) directly and easily implementable in simulation software. A new procedure, based on a best-fitting between the measured and simulated values of output device characteristics, is proposed in order to extract the optimal values of the CNTFET equivalent circuit elements. To verify the versatility of the proposed model, we use it in circuit simulators to design some electronic circuits. In particular we investigate about the effects of the CNT quantum resistances and inductances, then demonstrating their role for both analog and digital applications at frequencies over about ten THz.

A direct current model of carbon nanotube field effect transistors for computer-aided design applications is proposed. The main objective is to obtain a very good agreement between measured and simulated I–V curves through a best-fitting procedure, particularly in the knee and saturation regions. To verify the accuracy of the model, the results have been compared with those of experimental data and of a numerical model available online, obtaining a negligible relative error in both the cases. The proposed model can be easily implemented in an electrical simulator, and the computational time is very short.

In this paper we have implemented the semi-empirical compact model for CNTFETs already proposed by us both in SPICE, using ABM library, and in Verilog-A in order to compare them. Typical analogue circuits and logic blocks have been simulated and results have been presented to validate the implementation of the proposed CNTFET model both in Verilog-A and in SPICE. The obtained results have been the same in static simulations and comparable in dynamic simulations, in which the differences are due to the better implementation of the capacitance model in Verilog-A. We have found many advantages using Verilog-A: the development time in writing the model is shorter, the simulation run time much shorter and the software is much more concise and clear than schemes using ABM blocks in SPICE.