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ABSTRACT We present extensive proper motion measurements of the Crab Nebula made from Canada–France–Hawaii Telescope MegaPrime/MegaCam images taken in 2007, 2016, and 2019. A total of 19974 proper motion vectors with uncertainty $${<} 10$$ mas yr$$^{-1}$$ located over the majority of the Crab Nebula are used to map the supernova remnant’s two-dimensional expansion properties that reflect the dynamics of the original explosion, acceleration of ejecta imparted by spin-down energy from the pulsar, and interaction between the ejecta and surrounding cicumstellar material (CSM). The average convergence date we derive is 1105.5$$\pm$$0.5 CE, which is 15–35 yr earlier compared to most previous estimates. We find that it varies as a function of position angle around the nebula, with the earliest date and smallest proper motions measured along the equator defined by the east and west bays. The lower acceleration of material along the equatorial plane may be indicative of the supernova’s interaction with a disc-like CSM geometry. Comparing our measurements to previous analytical solutions of the Crab’s expansion and our own numerical simulation using the moving mesh hydrodynamics code sprout, we conclude that the ejecta have relaxed closer to homologous expansion than expected for the commonly adopted pulsar spin-down age of $$\tau \sim 700$$ yr and a pulsar wind nebula (PWN) still evolving inside the flat part of the ejecta density profile. These findings provide further evidence that the PWN has reached the outer steep part of the supernova ejecta density profile and escaped the confines of the ejecta shell in some regions. 

We present extensive proper motion measurements of the Crab Nebula made from Canada-France-Hawaii Telescope MegaPrime/MegaCam images taken in 2007, 2016, and 2019. A total of 19974 proper motion vectors with uncertainty < 10 mas yr−1 located over the majority of the Crab Nebula are used to map the supernova remnant’s two-dimensional expansion properties that reflect the dynamics of the original explosion, acceleration of ejecta imparted by spin-down energy from the pulsar, and interaction between the ejecta and surrounding circumstellar material (CSM). The average convergence date we derive is 1105.5 ± 0.5 CE, which is 15-35 years earlier compared to most previous estimates. We find that it varies as a function of position angle around the nebula, with the earliest date and smallest proper motions measured along the equator defined by the east and west bays. The lower acceleration of material along the equatorial plane may be indicative of the supernova’s interaction with a disk-like CSM geometry. Comparing our measurements to previous analytical solutions of the Crab’s expansion and our own numerical simulation using the moving mesh hydrodynamics code Sprout, we conclude that the ejecta have relaxed closer to homologous expansion than expected for the commonly adopted pulsar spindown age of τ ∼ 700 yr and a pulsar wind nebula (PWN) still evolving inside the flat part of the ejecta density profile. These findings provide further evidence that the PWN has broken out of the inner flat part of the supernova ejecta density profile and has experienced “blowout”. 

Usage of drones has increased substantially in both recreation and commercial applications and is projected to proliferate in the near future. As this demand rises, the threat they pose to both privacy and safety also increases. Delivering contraband and unauthorized surveillance are new risks that accompany the growth in this technology. Prisons and other commercial settings where venue managers are concerned about public safety need cost-effective detection solutions in light of their increasingly strained budgets. Hence, there arises a need to design a drone detection system that is low cost, easy to maintain, and without the need for expensive real-time human monitoring and supervision. To this end, this paper presents a low-cost drone detection system, which employs a Convolutional Neural Network (CNN) algorithm, making use of acoustic features. The Mel Frequency Cepstral Coefficients (MFCC) derived from audio signatures are fed as features to the CNN, which then predicts the presence of a drone. We compare field test results with an earlier Support Vector Machine (SVM) detection algorithm. Using the CNN yielded a decrease in the false positives and an increase in the correct detection rate.Previous tests showed that the SVM was particularly susceptible to false alarms for lawn equipment and helicopters, which were significantly improved when using the CNN. Also,in order to determine how well such a system compared to human performance and also explore including the end-user in the detection loop, a human performance experiment was conducted.With a sample of 35 participants, the human classification accuracy was 92.47%. These preliminary results clearly indicate that humans are very good at identifying drone’s acoustic signatures from other sounds and can augment the CNN’s performance. 

MEMS resonators integrated with CMOS feedback networks have a potentially wide field of applications as oscillator circuits in communications and sensor systems. However, considerable advancements to this nascent technology are required to realize such a vision. We present a configurable CMOS chip which facilitates the development of MEMS-referenced oscillators, especially for timing and sensing applications in harsh environments. The chip has been designed in the OnSemi 3M2P 0.5 um process. It supports MEMS resonators with various frequencies (10–120 kHz), resonant modes, and impedance levels, thus allowing interfacing to a wide range of devices. This paper describes analysis, design, and simulation results.