Polytechnic University of Milan

About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Finite element method & Population. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.

Progress in Silicon Single-Photon Avalanche Diodes

Abstract: Silicon single-photon avalanche diodes (SPADs) are nowadays a solid-state alternative to photomultiplier tubes (PMTs) in single-photon counting (SPC) and time-correlated single-photon counting (TCSPC) over the visible spectral range up to 1-mum wavelength. SPADs implemented in planar technology compatible with CMOS circuits offer typical advantages of microelectronic devices (small size, ruggedness, low voltage, low power, etc.). Furthermore, they have inherently higher photon detection efficiency, since they do not rely on electron emission in vacuum from a photocathode as do PMTs, but instead on the internal photoelectric effect. However, PMTs offer much wider sensitive area, which greatly simplifies the design of optical systems; they also attain remarkable performance at high counting rate, and offer picosecond timing resolution with microchannel plate models. In order to make SPAD detectors more competitive in a broader range of SPC and TCSPC applications, it is necessary to face several issues in the semiconductor device design and technology. Such issues will be discussed in the context of the two possible approaches to such a challenge: employing a standard industrial high-voltage CMOS technology or developing a dedicated CMOS-compatible technology. Advances recently attained in the development of SPAD detectors will be outlined and discussed with reference to both single-element detectors and integrated detector arrays.

A novel approach for barriers to industrial energy efficiency

Abstract: A critical review of the literature highlighted the need for a new taxonomy encompassing the most relevant barriers stemmed from previous studies, and accounting for interactions and independences of the barriers to avoid overlaps and implicit interactions. Based on an extensive literature review the paper provides a novel approach for barriers to the adoption of industrial energy-efficient technologies, coping with the issues risen by the review of the literature. We developed a taxonomy adaptable to empirical research, and able to evaluate the differences between perceived and real barriers, the effect of the barriers on decision-making processes, and the interactions among barriers. We modeled three types of interactions, i.e., causal relationship, composite effect and hidden effect, in order to start analyzing the dynamics among barriers, and tested the taxonomy in a preliminary investigation. The study proposes a useful instrument both to enterprises and policy-makers to identify critical factors to improve industrial energy efficiency and to open the research to further investigation in this topic.

Phase change memories: State-of-the-art, challenges and perspectives

Abstract: Among the emerging non-volatile technologies, phase change memories (PCM) are the most attractive in terms of both performance and scalability perspectives. The paper reviews the physics underlying PCM operation, the scaling potentials of these devices and some options recently proposed for the cell structure. The paper also addresses the main challenges for the PCM to become fully competitive with standard Flash technology.

Unsupervised learning techniques for an intrusion detection system

Abstract: With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are increasingly limited by their need of an up-to-date and comprehensive knowledge base. Data mining techniques have been successfully applied in host-based intrusion detection. Applying data mining techniques on raw network data, however, is made difficult by the sheer size of the input; this is usually avoided by discarding the network packet contents.In this paper, we introduce a two-tier architecture to overcome this problem: the first tier is an unsupervised clustering algorithm which reduces the network packets payload to a tractable size. The second tier is a traditional anomaly detection algorithm, whose efficiency is improved by the availability of data on the packet payload content.