On Complexity, Energy- and Implementation-Efficiency of Channel Decoders

Future wireless communication systems require efficient and flexible baseband receivers. Meaningful efficiency metrics are key for design space exploration to quantify the algorithmic and the implementation complexity of a receiver. Most of the current established efficiency metrics are based on counting operations, thus neglecting important issues like data and storage complexity. In this paper we introduce suitable energy and area efficiency metrics which resolve the afore-mentioned disadvantages. These are decoded information bit per energy and throughput per area unit. Efficiency metrics are assessed by various implementations of turbo decoders, LDPC decoders and convolutional decoders. New exploration methodologies are presented, which permit an appropriate benchmarking of implementation efficiency, communications performance, and flexibility trade-offs. These exploration methodologies are based on efficiency trajectories rather than a single snapshot metric as done in state-of-the-art approaches.Comment: Submitted to IEEE Transactions on Communication

Age estimates of isochronous reflection horizons by combining ice core, survey, and synthetic radar data.

Ice core records and ice-penetrating radar data contain complementary information on glacial subsurface structure and composition, providing various opportunities for interpreting past and present environmental conditions. To exploit the full range of possible applications, accurate dating of internal radar reflection horizons and knowledge about their constituting features is required. On the basis of three ice core records from Dronning Maud Land, Antarctica, and surface-based radar profiles connecting the drilling locations, we investigate the accuracies involved in transferring age-depth relationships obtained from the ice cores to continuous radar reflections. Two methods are used to date five internal reflection horizons: (1) conventional dating is carried out by converting the travel time of the tracked reflection to a single depth, which is then associated with an age at each core location, and (2) forward modeling of electromagnetic wave propagation is based on dielectric profiling of ice cores and performed to identify the depth ranges from which tracked reflections originate, yielding an age range at each drill site. Statistical analysis of all age estimates results in age uncertainties of 5 10 years for conventional dating and an error range of 1 16 years for forward modeling. For our radar operations at 200 and 250 MHz in the upper 100 m of the ice sheet, comprising some 1000 1500 years of deposition history, final age uncertainties are 8 years in favorable cases and 21 years at the limit of feasibility. About one third of the uncertainty is associated with the initial ice core dating; the remaining part is associated with radar data quality and analysis

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201

Computing Discrepancies Related to Spaces of Smooth Periodic Functions

A notion of discrepancy is introduced, which represents the integration error on spaces of $r$-smooth periodic functions. It generalizes the diaphony and constitutes a periodic counterpart to the classical $L_2$-discrepancy as weil as $r$-smooth versions of it introduced recently by Paskov [Pas93]. Based on previous work [FH96], we develop an efficient algorithm for computing periodic discrepancies for quadrature formulas possessing certain tensor product structures, in particular, for Smolyak quadrature rules (also called sparse grid methods). Furthermore, fast algorithms of computing periodic discrepancies for lattice rules can easily be derived from well-known properties of lattices. On this basis we carry out numerical comparisons of discrepancies between Smolyak and lattice rules

Towards Improving Phenotype Representation in OWL

BACKGROUND: Phenotype ontologies are used in species-specific databases for the annotation of mutagenesis experiments and to characterize human diseases. The Entity-Quality (EQ) formalism is a means to describe complex phenotypes based on one or more affected entities and a quality. EQ-based definitions have been developed for many phenotype ontologies, including the Human and Mammalian Phenotype ontologies. METHODS: We analyze formalizations of complex phenotype descriptions in the Web Ontology Language (OWL) that are based on the EQ model, identify several representational challenges and analyze potential solutions to address these challenges. RESULTS: In particular, we suggest a novel, role-based approach to represent relational qualities such as concentration of iron in spleen, discuss its ontological foundation in the General Formal Ontology (GFO) and evaluate its representation in OWL and the benefits it can bring to the representation of phenotype annotations. CONCLUSION: Our analysis of OWL-based representations of phenotypes can contribute to improving consistency and expressiveness of formal phenotype descriptions