Pseudo-random Aloha for Enhanced Collision-recovery in RFID

In this letter we motivate the need to revisit the MAC protocol used in Gen2 RFID system in order to leverage receiver structures with Collision Recovery capabilities at the PHY layer. To this end we propose to consider a simple variant of the Framed Slotted Aloha with pseudo-random (deterministic) slot selection as opposite to the classical random selection. Pseudo-random access allows naturally to implement Inter-frame Successive Interference Cancellation (ISIC) without changing the PHY modulation and coding format of legacy RFID standard. By means of simulations we show that ISIC can bring 20-25% gain in throughput with respect to traditional intra-frame SIC. Besides that, we elaborate on the potential of leveraging pseudo-random access protocols in combination with advanced PHY techniques in the context of RFID applications.Comment: This manuscript has been submitted to IEEE on the 19th September 201

Reduce to the Max: A Simple Approach for Massive-Scale Privacy-Preserving Collaborative Network Measurements (Extended Version)

Privacy-preserving techniques for distributed computation have been proposed recently as a promising framework in collaborative inter-domain network monitoring. Several different approaches exist to solve such class of problems, e.g., Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC) based on Shamir's Secret Sharing algorithm (SSS). Such techniques are complete from a computation-theoretic perspective: given a set of private inputs, it is possible to perform arbitrary computation tasks without revealing any of the intermediate results. In fact, HE and SSS can operate also on secret inputs and/or provide secret outputs. However, they are computationally expensive and do not scale well in the number of players and/or in the rate of computation tasks. In this paper we advocate the use of "elementary" (as opposite to "complete") Secure Multiparty Computation (E-SMC) procedures for traffic monitoring. E-SMC supports only simple computations with private input and public output, i.e., it can not handle secret input nor secret (intermediate) output. Such a simplification brings a dramatic reduction in complexity and enables massive-scale implementation with acceptable delay and overhead. Notwithstanding its simplicity, we claim that an E-SMC scheme is sufficient to perform a great variety of computation tasks of practical relevance to collaborative network monitoring, including, e.g., anonymous publishing and set operations. This is achieved by combining a E-SMC scheme with data structures like Bloom Filters and bitmap strings.Comment: This is an extended version of the paper presented at the Third International Workshop on Traffic Monitoring and Analysis (TMA'11), Vienna, 27 April 201

Explicit recognition of emotional facial expressions is shaped by expertise: evidence from professional actors

Can reading others' emotional states be shaped by expertise? We assessed processing of emotional facial expressions in professional actors trained either to voluntary activate mimicry to reproduce character's emotions (as foreseen by the “Mimic Method”), or to infer others' inner states from reading the emotional context (as foreseen by “Stanislavski Method”). In explicit recognition of facial expressions (Experiment 1), the two experimental groups differed from each other and from a control group with no acting experience: the Mimic group was more accurate, whereas the Stanislavski group was slower. Neither acting experience, instead, influenced implicit processing of emotional faces (Experiment 2). We argue that expertise can selectively influence explicit recognition of others' facial expressions, depending on the kind of “emotional expertise”

Report from the 6th PhD School on Traffic Monitoring and Analysis (TMA)

This is a summary report by the organizers of the 6th TMA PhD school held in Louvain-la-Neuve on 5-6 April 2016. The insight and feedback received about the event might turn useful for the organization of future editions and similar events targeting students and young researchers

Estimating population density distribution from network-based mobile phone data

In this study we address the problem of leveraging mobile phone network-based data for the task of estimating population density distribution at pan-European level. The primary goal is to develop a methodological framework for the collection and processing of network-based data that can be plausibly applied across multiple MNOs. The proposed method exploits more extensive network topology information than is considered in most state-of-the-art literature, i.e., (approximate) knowledge of cell coverage areas is assumed instead of merely cell tower locations. A distinguishing feature of the proposed methodology is the capability of taking in input a combination of cell-level and Location Area-level data, thus enabling the integration of data from Call Detail Records (CDR) with other network-based data sources, e.g., Visitor Location Register (VLR). Different scenarios are considered in terms of input data availability at individual MNOs (CDR only, VLR only, combinations of CDR and VLR) and for multi-MNO data fusion, and the relevant tradeoff dimensions are discussed. At the core of the proposed method lies a novel formulation of the population distribution estimation as a Maximum Likelihood estimation problem. The proposed estimation method is validated for consistency with synthetically generated data in a simplified simulation scenario.JRC.H.6-Digital Earth and Reference Dat

On integrating a proprietary and a commercial architecture for optimal BIST performances in SoCs

This paper presents the integration of a proprietary hierarchical and distributed test access mechanism called HD2BIST and a BIST insertion commercial tool. The paper briefly describes the architecture and the features of both the environments and it presents some experimental results obtained on an industrial So