Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution

Wireless Body Area Sensor Networks (WBASN) are an emerging technology enabling the design of natural human–computer interfaces (HCI). Automatic recognition of human motion, gestures, and activities is studied in several contexts. For example, mobile computing technology is being considered as a replacement of traditional input systems. Moreover, body posture and activity monitoring can be used for entertainment and health-care applications. However, until now, little work has been done to develop flexible and efficient WBASN solutions suitable for a wide range of applications. Their requirements pose new challenges for sensor network designs, such as optimizing traditional solutions for use as environmental monitoring-like applications and developing on-the-field stress tests. In this paper, we demonstrate the flexibility of a custom-designed WBASN called WiMoCA with respect to a wide range of posture and activity recognition applications by means of practical implementation and on-the-field testing. Nodes of the network mounted on different parts of the human body exploit tri-axial accelerometers to detect its movements. The advanced digital Micro-electro-mechanical system (MEMS) based inertial sensor has been chosen for WiMoCA because it demonstrated high flexibility of use in many different situations, providing the chance to exploit both static and dynamic acceleration components for different purposes. Furthermore, the sensibility and accuracy of the sensing element is perfectly adequate for monitoring human movement, while keeping cost low and size compact, thus meeting our requirements. We implemented three types of applications, stressing the WBASN in many aspects. In fact, they are characterized by different requirements in terms of accuracy, timeliness, and computation distributed on sensing nodes. For each application, we describe its implementation, and we discuss results about performance and power consumption.

Store-and-Forward Multicast Routing on the Mesh

We study the complexity of routing a set of messages with multiple destinations (multicast routing) on an n-node square mesh under the store-and-forward model. A standard argument proves that time is required to route n messages, where each message is generated by a distinct node and at most c messages are to be delivered to any individual node. The obvious approach of simply replicating each message into the appropriate number of unicast (single-destination) messages and routing these independently does not yield an optimal algorithm. We provide both randomized and deterministic algorithms for multicast routing, which use constant-size buffers at each node. The randomized algorithm attains optimal performance, while the deterministic algorithm is slower by a factor of O( log ² n). We also describe an optimal deterministic algorithm that, however, requires large buffers of size O(c). A preliminary version of this paper appeared in Proceedings of the 13th Annual ACM Symposium on Parallel Algorithms and Architectures, Crete, Greece, 2001. This work was supported, in part, by MIUR under project ALGO-NEXT.     

Molecular dynamics at the receptor level of immunodominant myelin basic protein epitope 87-99 implicated in multiple sclerosis and its antagonists altered peptide ligands: triggering of immune response

This work reports molecular dynamics studies at the receptor level of the immunodominant myelin basic protein (MBP) epitope 87-99 implicated in multiple sclerosis, and its antagonists altered peptide ligands (APLs), namely [Arg91, Ala96] MBP87-99 and [Ala91,96] MBP87-99. The interaction of each peptide ligand with the receptor human leukocyte antigen HLA-DR2b was studied, starting from X-ray structure with pdb code: 1ymm. This is the first such study of APL-HLA-DR2b complexes, and hence the first attempt to gain a better understanding of the molecular recognition mechanisms that underlie TCR antagonism by these APLs. The amino acids His88 and Phe89 serve as T-cell receptor (TCR) anchors in the formation of the trimolecular complex TCR-peptide-HLA-DR2b, where the TCR binds in a diagonal, off-centered mode to the peptide-HLA complex. The present findings indicate that these two amino acids have a different orientation in the APLs [Arg91, Ala96] MBP87-99 and [Ala91,96] MBP87-99: His88 and Phe89 remain buried in HLA grooves and are not available for interaction with the TCR. We propose that this different topology could provide a possible mechanism of action for TCR antagonism.     

ReSpecT Nets: Towards an Analysis Methodology for ReSpecT Specifications

A key feature for infrastructures providing coordination services is the ability to define the behaviour of coordination abstractions according to the requirements identified at design-time. We take as a representative for this scenario the logic-based language ReSpecT (Reaction Specification Tuples), used to program the reactive behaviour of tuple centres. ReSpecT specifications are at the core of the engineering methodology underlying the TuCSoN infrastructure, and are therefore the “conceptual place” where formal methods can be fruitfully applied to guarantee relevant system properties.In this paper we introduce ReSpecT nets, a formalism that can be used to describe reactive behaviours that can succeed and fail, and that allows for an encoding to Petri nets with inhibitor arcs. ReSpecT nets are introduced to give a core model to a fragment of the ReSpecT language, and to pave the way for devising an analysis methodology including formal verification of safety and liveness properties. In particular, we provide a semantics to ReSpecT specifications through a mapping to ReSpecT nets. The potential of this approach for the analysis of ReSpecT specifications is discussed, presenting initial results for the analysis of safety properties.     

A low-power wireless video sensor node for distributed object detection

In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.

Chemical cross-linking and mass spectrometry to map three-dimensional protein structures and protein-protein interactions

Closely related to studying the function of a protein is the analysis of its three-dimensional structure and the identification of interaction sites with its binding partners. An alternative approach to the high-resolution methods for three-dimensional protein structure analysis, such as X-ray crystallography and NMR spectroscopy, consists of covalently connecting two functional groups of the protein(s) under investigation. The location of the created cross-links imposes a distance constraint on the location of the respective side chains and allows one to draw conclusions on the three-dimensional structure of the protein or a protein complex. Recently, chemical cross-linking of proteins has been combined with a mass spectrometric analysis of the created cross-linked products. This review article describes the most popular cross-linking reagents for protein structure analysis and gives an overview of the different available strategies that employ chemical cross-linking and different mass spectrometric techniques. The challenges for mass spectrometry caused by the enormous complexity of the cross-linking reaction mixtures are emphasized. The various approaches described in the literature to facilitate the mass spectrometric detection of cross-linked products as well as computer software for data analyses are reviewed.     

Efficient additive kernels via explicit feature maps

Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ2 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels; 2) derive corresponding approximate finite-dimensional feature maps based on a spectral analysis; and 3) quantify the error of the approximation, showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as χ2. We demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. We also compare with two other approximation methods: Nystrom's approximation of Perronnin et al., which is data dependent, and the explicit map of Maji and Berg for the intersection kernel, which, as in the case of our approximations, is data independent. The approximations are evaluated on a number of standard data sets, including Caltech-101, Daimler-Chrysler pedestrians, and INRIA pedestrians.     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

Ontological query answering under expressive Entity-Relationship schemata

The Entity-Relationship (ER) model is a fundamental tool for database design, recently extended and employed in knowledge representation and reasoning due to its expressiveness and comprehensibility. We address the problem of answering conjunctive queries under constraints representing schemata expressed in an extended version of the Entity-Relationship model. This extended model, called ER⁺, comprises is-a constraints among entities and relationships, plus functional and mandatory participation constraints. In particular, it allows for arbitrary permutations of the roles in is-a among relationships. A key notion that ensures high tractability in ER⁺ schemata is separability, i.e., the absence of interaction between the functional participation constraints and the other constructs of ER⁺. We provide a precise syntactic characterization of separable ER⁺ schemata by means of a necessary and sufficient condition. We present a complete complexity analysis of the conjunctive query answering problem under separable ER⁺ schemata, and also under several sublanguages of ER⁺. We show that the addition of so-called negative constraints does not increase the complexity of query answering. With such constraints, our model properly generalizes the most widely adopted tractable ontology languages, including those in the DL-Lite family.