This paper presents observations of SiO maser emission from 161 Mira variables distributed over a wide range of intrinsic parameters like spectral type, bolometric magnitude and amplitude of pulsation. The observations were made at 86.243 GHz, using the 10•4 m millimeter-wave telescope of the Raman Research Institute at Bangalore, India. These are the first observations made using this telescope. From these observations, we have established that the maser emission is restricted to Miras having mean spectral types between M6 and M10. The infrared period-luminosity relation for Mira variables is used to calculate their distances and hence estimate their maser luminosities from the observed fluxes. The maser luminosity is found to be correlated with the bolometric magnitude of the Mira variable. On an H-R diagram, the masing Mira variables are shown to lie in a region distinct from that for the non-masing ones.

The One Square Metre Array (OSMA) is the second of three stages leading to the Square Kilometre Array (SKA). The SKA telescope will be sought after by astronomers around the globe, as its sensitivity to astronomical sources will be greater than two orders of magnitude better than current telescopes.The OSMA system consists of two beamforming stages -RF and Digital. In this paper the RF beamformer is discussed in detail and recent findings presented. One of the major features of the RF beamformer is its large instantaneous bandwidth which is due to the fact that time delays are used to form beams. I OSMA -THE ONE SQUARE METRE ARRAY As a lead up to the astronomical telescope SKA [1], several developments have been devised to prove technology, algorithms and feasibility of such a large telescope. These developments include the Adaptive Antenna Demonstrator (AAD) [2, 3], the current development of OSMA, and finally the Thousand Element Array (THEA) [4] to be completed in the year 2000.(a) OSMA Bow-tie ArrayThe OSMA system, which is pictured in Figure 1 (a), is a phased-array receive-only antenna with a mixed RF and digital adaptive beamforming architecture operating in the frequency range of 1.5 GHz to 3.5 GHz. The linearly polarized antenna consists of an 8 by 8 element active centre region surrounded by two rows of passive elements (totallying 80 elements). The array is built up of broadband bow-tie antenna elements with an integrated balun [5] and the distance between adjacent elements is 75mm in both directions. The array is backed by a ground plane which is rounded at the array edges to reduce diffraction effects.The beamforming hierarchy is illustrated in Figure l(b) where the active elements are connected to 16 RF beamformer units (RFBF I). Each RFBF I unit receives signals from four bow-tie elements, producing two identical beam outputs. The outputs of the RFBF I units can be connected to both a 16-channel adap tive digital beamforming (ADBFS) unit or to a second stage 16-channel RF beamformer unit (RFBF II). The receivers perform frequency down conversion to an intermediate frequency of 70 MHz. OSMA will be used in two different modes; a RF beamforming mode, or a mixed RF/digital adaptive beamforming mode. (b) OSMA Beamforming Hierarchy Fig. 1: (a) The OSMA array inside the NFRA test facility. (b) The beamforming hierarchy of OSMA is divided into several stages -allowing many different beamforming configurations. 111 29th European Microwave Conference -Munich 1999

Results from an experimental antenna array that is processed adaptively to cancel external radio-frequency interference are presented. The eight-element array has been tested in a far-field anechoic chamber. The adaptive beamformer rejects sidelobe and main-lobe interference by up to 37 dB. A conventional Fourier beamformer rejects interference only in the sidelobes by between 13 and 30 dB depending on the location of the interference within the array's sidelobes. The adaptive beamformer does not need any a priori knowledge about the interfering signals such as the number of interferers or their direction of arrival. The adaptive beamformer also has superior angular resolution to the Fourier beamformer. With an aperture of 4.4 wavelengths and an input signal-to-noise ratio of 37 dB, the adaptive beamformer's resolution is as low as 0.28" while the Fourier beamformer's resolution is 8.8".

A: The India based Neutrino Observatory (INO) is a proposed particle physics research project to study the atmospheric neutrinos. INO-Iron Calorimeter (ICAL) will consist of 28,800 detectors having 3.6 million electronic channels expected to activate with 100 Hz single rate, producing data at a rate of 3 GBps. Data collected contains a few real hits generated by muon tracks and the remaining noise-induced spurious hits. Estimated reduction factor after filtering out data of interest from generated data is of the order of 10 3 . This makes trigger generation critical for efficient data collection and storage. Trigger is generated by detecting coincidence across multiple channels satisfying trigger criteria, within a small window of 200 ns in the trigger region. As the probability of neutrino interaction is very low, track detection algorithm has to be efficient and fast enough to process 5 × 10 6 events-candidates/s without introducing significant dead time, so that not even a single neutrino event is missed out.A hardware based trigger system is presently proposed for on-line track detection considering stringent timing requirements. Though the trigger system can be designed with scalability, a lot of hardware devices and interconnections make it a complex and expensive solution with limited flexibility. A software based track detection approach working on the hit information offers an elegant solution with possibility of varying trigger criteria for selecting various potentially interesting physics events. An event selection approach for an alternative triggerless readout scheme has been developed. The algorithm is mathematically simple, robust and parallelizable. It has been validated by detecting simulated muon events for energies of the range of 1 GeV-10 GeV with 100% efficiency at a processing rate of 60 µs/event on a 16 core machine. The algorithm and result of a proof-of-concept for its faster implementation over multiple cores is presented. The paper 1Corresponding author.

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