7,557 research outputs found

    A single-photon sampling architecture for solid-state imaging

    Full text link
    Advances in solid-state technology have enabled the development of silicon photomultiplier sensor arrays capable of sensing individual photons. Combined with high-frequency time-to-digital converters (TDCs), this technology opens up the prospect of sensors capable of recording with high accuracy both the time and location of each detected photon. Such a capability could lead to significant improvements in imaging accuracy, especially for applications operating with low photon fluxes such as LiDAR and positron emission tomography. The demands placed on on-chip readout circuitry imposes stringent trade-offs between fill factor and spatio-temporal resolution, causing many contemporary designs to severely underutilize the technology's full potential. Concentrating on the low photon flux setting, this paper leverages results from group testing and proposes an architecture for a highly efficient readout of pixels using only a small number of TDCs, thereby also reducing both cost and power consumption. The design relies on a multiplexing technique based on binary interconnection matrices. We provide optimized instances of these matrices for various sensor parameters and give explicit upper and lower bounds on the number of TDCs required to uniquely decode a given maximum number of simultaneous photon arrivals. To illustrate the strength of the proposed architecture, we note a typical digitization result of a 120x120 photodiode sensor on a 30um x 30um pitch with a 40ps time resolution and an estimated fill factor of approximately 70%, using only 161 TDCs. The design guarantees registration and unique recovery of up to 4 simultaneous photon arrivals using a fast decoding algorithm. In a series of realistic simulations of scintillation events in clinical positron emission tomography the design was able to recover the spatio-temporal location of 98.6% of all photons that caused pixel firings.Comment: 24 pages, 3 figures, 5 table

    Electroencephalography (EEG)-Derived Markers to Measure Components of Attention Processing

    Get PDF
    Although extensively studied for decades, attention system remains an interesting challenge in neuroscience field. The Attention Network Task (ANT) has been developed to provide a measure of the efficiency for the three attention components identified in the Posner’s theoretical model: alerting, orienting and executive control. Here we propose a study on 15 healthy subjects who performed the ANT. We combined advanced methods for connectivity estimation on electroencephalographic (EEG) signals and graph theory with the aim to identify neuro-physiological indices describing the most important features of the three networks correlated with behavioral performances. Our results provided a set of band-specific connectivity indices able to follow the behavioral task performances among subjects for each attention component as defined in the ANT paradigm. Extracted EEG-based indices could be employed in future clinical applications to support the behavioral assessment or to evaluate the influence of specific attention deficits on Brain Computer Interface (BCI) performance and/or the effects of BCI training in cognitive rehabilitation applications

    VINEA: a policy-based virtual network embedding architecture

    Full text link
    Network virtualization has enabled new business models by allowing infrastructure providers to lease or share their physical network. To concurrently run multiple customized virtual network services, such infrastructure providers need to run a virtual network embedding protocol. The virtual network embedding is the (NP-hard) problem of matching constrained virtual networks onto the physical network. We present the design and implementation of a policy-based architecture for the virtual network embedding problem. By policy, we mean a variant aspect of any of the (invariant) embedding mechanisms: resource discovery, virtual network mapping, and allocation on the physical infrastructure. Our architecture adapts to different scenarios by instantiating appropriate policies, and has bounds on embedding efficiency and on convergence embedding time, over a single provider, or across multiple federated providers. The performance of representative novel policy configurations are compared over a prototype implementation. We also present an object model as a foundation for a protocol specification, and we release a testbed to enable users to test their own embedding policies, and to run applications within their virtual networks. The testbed uses a Linux system architecture to reserve virtual node and link capacities.National Science Foundation (CNS-0963974

    Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems

    Get PDF
    Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ). This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)

    A Survey of Parallel Data Mining

    Get PDF
    With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms
    • …
    corecore