In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.

Millimeter wave (mm-wave) design has become the forefront for enabling multi-Gb/s wireless communications due to the abundance of available bandwidth at frequencies above 24 GHz. At these frequencies, phased arrays are used to meet link budgets by combining phase-adjusted responses of multiple antennas to form a high-gain, directive beam which is electrically steerable. Current requirements point to array sizes ranging from 8-32 elements, each of which must be measured and calibrated in terms of RF output power and phase to obtain the desired array performance. This paper will first review phased-array topologies and calibration requirements. We will then present a code modulated technique for manufacturing test of the array which uses only digital code modulators per element and a single global mm-wave squaring circuit in the form of a power detector. This approach allows measurement of full array performance with a single detector using minimum additional built-in-test hardware. Behavioral models indicate that this method can estimate the phase response within 1 degree and an output power within 0.2 dB for each individual element using global array measurements.

Of considerable interest in recent years has been the problem of exchanging correlated data with minimum communication. We thus consider the problem of exchanging two similar strings held by different hosts. Our approach involves transforming a string into a multiset of substrings that are reconciled efficiently using known multiset reconciliation algorithms, and then put back together on a remote host using tools from graph theory. We present analyses, experiments, and results to show that the communication complexity of our approach for high-entropy data compares favorably to existing algorithms including rsync, a widely-used string reconciliation engine. We also quantify the trade-off between communication and the computation complexity of our approach

We consider the problem of exchanging two similar strings held by different hosts with a minimum amount of communication. We reduce the problem of string reconciliation to a problem of multi-set reconciliation, for which nearly optimal solutions exist. Our approach involves transforming a string into a multi-set of substrings, which are reconciled efficiently and then put back together on a remote host using recent graph-theoretic results. We present an analysis of our algorithm to show that its communication complexity compares favorably to two existing methods for string reconciliation.