PONDER - A Real time software backend for pulsar and IPS observations at the Ooty Radio Telescope

This paper describes a new real-time versatile backend, the Pulsar Ooty Radio Telescope New Digital Efficient Receiver (PONDER), which has been designed to operate along with the legacy analog system of the Ooty Radio Telescope (ORT). PONDER makes use of the current state of the art computing hardware, a Graphical Processing Unit (GPU) and sufficiently large disk storage to support high time resolution real-time data of pulsar observations, obtained by coherent dedispersion over a bandpass of 16 MHz. Four different modes for pulsar observations are implemented in PONDER to provide standard reduced data products, such as time-stamped integrated profiles and dedispersed time series, allowing faster avenues to scientific results for a variety of pulsar studies. Additionally, PONDER also supports general modes of interplanetary scintillation (IPS) measurements and very long baseline interferometry data recording. The IPS mode yields a single polarisation correlated time series of solar wind scintillation over a bandwidth of about four times larger (16 MHz) than that of the legacy system as well as its fluctuation spectrum with high temporal and frequency resolutions. The key point is that all the above modes operate in real time. This paper presents the design aspects of PONDER and outlines the design methodology for future similar backends. It also explains the principal operations of PONDER, illustrates its capabilities for a variety of pulsar and IPS observations and demonstrates its usefulness for a variety of astrophysical studies using the high sensitivity of the ORT.Comment: 25 pages, 14 figures, Accepted by Experimental Astronom

Simultaneous multi-frequency single pulse observations of pulsars

We performed simultaneous observations at 326.5 MHz with the Ooty Radio Telescope and at 326, 610 and 1308 MHz with the Giant Meterwave Radio Telescope for a sample of 12 pulsars, where frequency dependent single pulse behaviour was reported. The single pulse sequences were analysed with fluctuation analysis, sensitive to both the average fluctuation properties (using longitude resolved fluctuation spectrum and two-dimensional fluctuation spectrum) as well as temporal changes in these (using sliding two-dimensional fluctuation spectrum ) to establish concurrent changes in subpulse drifting over the multiple frequencies employed. We report subpulse drifting in PSR J0934$-$5249 for the first time. We also report pulse nulling measurements in PSRs J0934$-$5249, B1508+55, J1822$-$2256, B1845$-$19 and J1901$-$0906 for the first time. Our measurements of subpulse drifting and pulse nulling for the rest of the pulsars are consistent with previously reported values. Contrary to previous belief, we find no evidence for a frequency dependent drift pattern in PSR B2016+28 implied by non-simultaneous observations by Oster et al. (1977). In PSRs B1237+25, J1822$-$2256, J1901$-$0906 and B2045$-$16, our longer and more sensitive observations reveal multiple drift rates with distinct P3. We increase the sample of pulsars showing concurrent nulling across multiple frequencies by more than 100 percent, adding 4 more pulsars to this sample. Our results confirm and further strengthen the understanding that the subpulse drifting and pulse nulling are broadband consistent with previous studies (Gajjar et al. 2014a; Rankin 1986; Weltevrede et al. 2007) and are closely tied to physics of polar gap.Comment: 22 pages, 44 figures, Single pulse studies of pulsars, accepted by A&

Detection of long nulls in PSR B1706−-16, a pulsar with large timing irregularities

Single pulse observations, characterizing in detail, the nulling behaviour of PSR B1706$-$16 are being reported for the first time in this paper. Our regular long duration monitoring of this pulsar reveals long nulls of 2 to 5 hours with an overall nulling fraction of 31$\pm$2\%. The pulsar shows two distinct phases of emission. It is usually in an active phase, characterized by pulsations interspersed with shorter nulls, with a nulling fraction of about 15 \%, but it also rarely switches to an inactive phase, consisting of long nulls. The nulls in this pulsar are concurrent between 326.5 and 610 MHz. Profile mode changes accompanied by changes in fluctuation properties are seen in this pulsar, which switches from mode A before a null to mode B after the null. The distribution of null durations in this pulsar is bimodal. With its occasional long nulls, PSR B1706$-$16 joins the small group of intermediate nullers, which lie between the classical nullers and the intermittent pulsars. Similar to other intermediate nullers, PSR B1706$-$16 shows high timing noise, which could be due to its rare long nulls if one assumes that the slowdown rate during such nulls is different from that during the bursts.Comment: Accepted for publication in MNRA

Scatter broadening measurements of 124 pulsars at 327 MHz

We present the measurements of scatter broadening time-scales ($\tau_{sc}$) for 124 pulsars at 327 MHz, using the upgraded Ooty Radio Telescope (ORT). These pulsars lie in the dispersion measure range of 37 $-$ 503 pc cm$^{-3}$ and declination ($\delta$) range of $-$57$^{\circ} < \delta< 60^{\circ}$. New $\tau_{sc}$ estimates for 58 pulsars are presented, increasing the sample of all such measurements by about 40% at 327 MHz. Using all available $\tau_{sc}$ measurements in the literature, we investigate the dependence of $\tau_{sc}$ on dispersion measure. Our measurements, together with previously reported values for $\tau_{sc}$, affirm that the ionized interstellar medium upto 3 kpc is consistent with Kolmogorov spectrum, while it deviates significantly beyond this distance.Comment: 19 pages, 4 figures, accepted for publication in Ap

Maximum Matching via Maximal Matching Queries

We study approximation algorithms for Maximum Matching that are given access to the input graph solely via an edge-query maximal matching oracle. More specifically, in each round, an algorithm queries a set of potential edges and the oracle returns a maximal matching in the subgraph spanned by the query edges that are also contained in the input graph. This model is more general than the vertex-query model introduced by binti Khalil and Konrad [FSTTCS\u2720], where each query consists of a subset of vertices and the oracle returns a maximal matching in the subgraph of the input graph induced by the queried vertices. In this paper, we give tight bounds for deterministic edge-query algorithms for up to three rounds. In more detail: 1) As our main result, we give a deterministic 3-round edge-query algorithm with approximation factor 0.625 on bipartite graphs. This result establishes a separation between the edge-query and the vertex-query models since every deterministic 3-round vertex-query algorithm has an approximation factor of at most 0.6 [binti Khalil, Konrad, FSTTCS\u2720], even on bipartite graphs. Our algorithm can also be implemented in the semi-streaming model of computation in a straightforward manner and improves upon the state-of-the-art 3-pass 0.6111-approximation algorithm by Feldman and Szarf [APPROX\u2722] for bipartite graphs. 2) We show that the aforementioned algorithm is optimal in that every deterministic 3-round edge-query algorithm has an approximation factor of at most 0.625, even on bipartite graphs. 3) Last, we also give optimal bounds for one and two query rounds, where the best approximation factors achievable are 1/2 and 1/2 + ?(1/n), respectively, where n is the number of vertices in the input graph

A pattern recognition approach for identification of transducer-structure debonding using Lamb waves

In structural health monitoring, using piezoelectric transducers to generate high frequency elastic waves like Lamb waves in the structures is eminent. In general, piezoelectric transducers are assumed to be perfectly bonded with the host structure; however, in practical environment, there are possibilities for them to have faults. Since detecting, locating and assessing damages in a structure depend solely on transducer responses, transducer fault identification is vital. By using electrical admittance, axial strain and shear stress as function of frequencies or in analog interface circuits to identify faulty transducers, lead to demand of circuitry and processes, consequently increasing the implementation complexity. Hence, we propose a pattern recognition system that can identify transducers that are partially bonded to host structure. This pattern recognition system employs classification for features extracted from instantaneous Lamb wave signals with no need of baseline data

Detection of radio emission from the gamma-ray pulsar J1732-3131 at 327 MHz

Although originally discovered as a radio-quiet gamma-ray pulsar, J1732-3131 has exhibited intriguing detections at decameter wavelengths. We report an extensive follow-up of the pulsar at 327 MHz with the Ooty radio telescope. Using the previously observed radio characteristics, and with an effective integration time of 60 hrs, we present a detection of the pulsar at a confidence level of 99.82%. The 327 MHz mean flux density is estimated to be 0.5-0.8 mJy, which establishes the pulsar to be a steep spectrum source and one of the least-luminous pulsars known to date. We also phase-aligned the radio and gamma-ray profiles of the pulsar, and measured the phase-offset between the main peaks in the two profiles to be 0.24$\pm$0.06. We discuss the observed phase-offset in the context of various trends exhibited by the radio-loud gamma-ray pulsar population, and suggest that the gamma-ray emission from J1732-3131 is best explained by outer magnetosphere models. Details of our analysis leading to the pulsar detection, and measurements of various parameters and their implications relevant to the pulsar's emission mechanism are presented.Comment: 8 pages, 6 figures; Accepted for publication in MNRA

Detection of 15 bursts from FRB 180916.J0158+65 with the uGMRT

We report the findings of a uGMRT observing campaign on FRB 180916.J0158+65, discovered recently to show a 16.35-day periodicity of its active cycle. We observed the source at 550-750 MHz for $\sim 2$ hours each during three successive cycles at the peak of its expected active period. We find 0, 12, and 3 bursts respectively, implying a highly variable bursting rate even within the active phase. We consistently detect faint bursts with spectral energies only an order of magnitude higher than the Galactic burst source SGR~1935+2154. The times of arrival of the detected bursts rule out many possible aliased solutions, strengthening the findings of the 16.35-day periodicity. A short-timescale periodicity search returned no highly significant candidates. Two of the beamformer-detected bursts were bright enough to be clearly detected in the imaging data, achieving sub-arcsecond localization, and proving as a proof-of-concept for FRB imaging with the GMRT. We provide a $3\sigma$ upper limit of the persistent radio flux density at 650 MHz of $66~\mu{\rm Jy}$ which, combined with the EVN and VLA limits at 1.6~GHz, further constrains any potential radio counterpart. These results demonstrate the power of uGMRT for targeted observations to detect and localize known repeating FRBs.Comment: 5 pages, 4 figures, accepted for publication in MNRAS Letter

Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities

© 2024 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.Peer reviewe