A security approach for off-chip memory in embedded microprocessor systems

This paper describes a complete off-chip memory security solution for embedded systems. Our security core is based on a one-time pad (OTP) encryption circuit and a CRC-based integrity checking module. These modules safeguard external memory used by embedded processors against a series of well-known attacks, including replay attacks, spoofing attacks and relocation attacks. Our implementation limits on-chip memory space overhead to less than 33% versus memory used by a standard microprocessor and reduces memory latency achieved by previous approaches by at least half. The performance loss for software execution with our solution is only 10% compared with a non-protected implementation. An FPGA prototype of our security core has been completed to validate our findings.     

A comparative study of existing metrics for 3D-mesh segmentation evaluation

In this paper, we present an extensive experimental comparison of existing similarity metrics addressing the quality assessment problem of mesh segmentation. We introduce a new metric, named the 3D Normalized Probabilistic Rand Index (3D-NPRI), which outperforms the others in terms of properties and discriminative power. This comparative study includes a subjective experiment with human observers and is based on a corpus of manually segmented models. This corpus is an improved version of our previous one (Benhabiles et al. in IEEE International Conference on Shape Modeling and Application (SMI), 2009). It is composed of a set of 3D-mesh models grouped in different classes associated with several manual ground-truth segmentations. Finally the 3D-NPRI is applied to evaluate six recent segmentation algorithms using our corpus and the Chen et al.’s (ACM Trans. Graph. (SIGGRAPH), 28(3), 2009) corpus.     

A framework for regional association rule mining and scoping in spatial datasets

The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists.     

High accuracy and visibility-consistent dense multiview stereo

Since the initial comparison of Seitz et al., the accuracy of dense multiview stereovision methods has been increasing steadily. A number of limitations, however, make most of these methods not suitable to outdoor scenes taken under uncontrolled imaging conditions. The present work consists of a complete dense multiview stereo pipeline which circumvents these limitations, being able to handle large-scale scenes without sacrificing accuracy. Highly detailed reconstructions are produced within very reasonable time thanks to two key stages in our pipeline: a minimum s-t cut optimization over an adaptive domain that robustly and efficiently filters a quasidense point cloud from outliers and reconstructs an initial surface by integrating visibility constraints, followed by a mesh-based variational refinement that captures small details, smartly handling photo-consistency, regularization, and adaptive resolution. The pipeline has been tested over a wide range of scenes: from classic compact objects taken in a laboratory setting, to outdoor architectural scenes, landscapes, and cultural heritage sites. The accuracy of its reconstructions has also been measured on the dense multiview benchmark proposed by Strecha et al., showing the results to compare more than favorably with the current state-of-the-art methods.     

Dual path communications over multiple spanning trees for networked control systems

The switched Ethernet networks are more and more deployed in industry. The Spanning Tree Protocol implemented in the switches enables management of the link connectivity. But the reconfiguration time of the Spanning Tree Protocol (STP) when link failure occurs is not adapted to satisfy industrial constraints. The objective of this paper is to propose a method based only on standard, mitigating the probability of disconnection between nodes having hard real-time properties. The approach developed in this paper consists of duplicating frames and of forwarding them on different paths. These paths are optimized and specified by using genetic algorithms. OPNET simulations show the interest of this proposal on a particular Networked Control System.     

Segmentation-based multi-class semantic object detection

In this paper we study the problem of the detection of semantic objects from known categories in images. Unlike existing techniques which operate at the pixel or at a patch level for recognition, we propose to rely on the categorization of image segments. Recent work has highlighted that image segments provide a sound support for visual object class recognition. In this work, we use image segments as primitives to extract robust features and train detection models for a predefined set of categories. Several segmentation algorithms are benchmarked and their performances for segment recognition are compared. We then propose two methods for enhancing the segments classification, one based on the fusion of the classification results obtained with the different segmentations, the other one based on the optimization of the global labelling by correcting local ambiguities between neighbor segments. We use as a benchmark the Microsoft MSRC-21 image database and show that our method competes with the current state-of-the-art.     

All-Electrical Detection of the Spin-Charge Conversion in Nanodevices Based on SrTiO3 2-D Electron Gases

The Magnetoelectric Spin-Orbit (MESO) technology aims to bring logic into memory by combining a ferromagnet with a magnetoelectric (ME) element for information writing, and a spin-orbit (SO) element for information read-out through spin-charge conversion. Among candidate SO materials to achieve a large MESO output signal, oxide Rashba two-dimensional electron gases (2DEGs) have shown very large spin-charge conversion efficiencies, albeit mostly in spin-pumping experiments. Here, all-electrical spin-injection and spin-charge conversion experiments in nanoscale devices harnessing the inverse Edelstein effect of SrTiO₃ 2DEGs are reported. Nanodevices aredesigned, patterned, and fabricated in which a spin current injected from a cobalt layer into the 2DEG is converted into a charge current. The spin-charge conversion signal is optimized by applying back-gate voltages and studied its temperature evolution. It further disentangles the inverse Edelstein contribution from spurious effects such as the planar Hall effect, the anomalous Hall effect, or the anisotropic magnetoresistance. The combination of non-volatility and high energy efficiency of these devices can potentially lead to new technology paradigms for beyond-CMOS computing architectures.     

Integrated synthetic, pharmacological, and computational investigation of cis-2-(3,5-dichlorophenylcarbamoyl)cyclohexanecarboxylic acid enantiomers as positive allosteric modulators of metabotropic glutamate receptor subtype 4

2‐(3,5‐Dichlorophenylcarbamoyl)cyclohexanecarboxylic acid ( 1 ) is a potent and selective positive allosteric modulator of metabotropic glutamate receptor subtype 4 (mGluR4). The activity of 1 was reported to reside in the cis diastereomer with equal potency between its enantiomeric forms (Niswender et al., Mol. Pharmacol. 2008 , 74, 1345–1358). In the present study, the asymmetric synthesis of each of the cis enantiomers was performed, and their activities were compared with that of the racemic trans. In our assays, the cis enantiomers differ in potency, with one of them (1R,2S) higher and the other (1S,2R) lower than the racemic trans. High‐level quantum chemical calculations were carried out to characterize the structures of minimum energy in all‐isomer conformational space as well as particular intermediates between conformational transitions. Computational analysis identified structural features of 1 that can play a role in mGluR4 functionality and establish the basis for subsequent work, in which molecular chirality constructed on conformations derived from those found for the active (1R,2S) enantiomer can provide new ideas for drug discovery. Comparison between experimental and theoretical circular dichroism spectra confirmed both the absolute configuration of the (1R,2S) compound and its calculated most stable conformation, thereby supporting experimental and theoretical work.     

The formation of halogen-specific TOX from chlorination and chloramination of natural organic matter isolates

The formation of disinfection by-products (DBPs) is a public health concern. An important way to evaluate the presence of DBPs is in terms of the total organic halogen (TOX), which can be further specified into total organic chlorine (TOCl), bromine (TOBr), and iodine (TOI). The formation and distribution of halogen-specific TOX during chlorination and chloramination of natural organic matter (NOM) isolates in the presence of bromide and iodide ions were studied. As expected, chloramination produced significantly less TOX than chlorination. TOCl was the dominant species formed in both chlorination and chloramination. TOI was always produced in chloramination, but not in chlorination when high chlorine dose was used, due to the limited presence of HOI in chlorination as a result of the oxidation of iodide to iodate in the presence of excess chlorine. The formation of TOI during chloramination increased as the initial iodide ion concentration increased, with a maximum of ∼60% of the initial iodide ion becoming incorporated into NOM. Iodine incorporation in NOM was consistently higher than bromine incorporation, demonstrating that the competitive reactions between bromine and iodine species in chloramination favoured the formation of HOI and thus TOI, rather than TOBr. Correlations between the aromatic character of the NOM isolates (SUVA₂₅₄ and % aromatic C) and the concentrations of overall TOX and halogen-specific TOX in chloramination were observed. This indicates that the aromatic moieties in NOM, as indicated by SUVA₂₅₄ and % aromatic C, play an important role in the formation of overall TOX and halogen-specific TOX in chloramination. THMs comprised only a fraction of TOX, up to 7% in chloramination and up to 47% in chlorination. Although chloramine produces less TOX than chlorine, it formed proportionally more non-THM DBPs than chlorine. These non-THM DBPs are mostly unknown, corresponding to unknown health risks. Considering the higher potential for formation of iodinated DBPs and unknown DBPs associated with the use of chloramine, water utilities need to carefully balance the risks and benefits of using chloramine as an alternative disinfectant to chlorine in order to satisfy guideline values for THMs.