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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. 

Measurements of the production of electrons from heavy-flavour hadron decays in pp collisions at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV at midrapidity with the ALICE detector are presented down to a transverse momentum (p_T) of 0.2 GeV/cand up top_T= 35 GeV/c, which is the largest momentum range probed for inclusive electron measurements in ALICE. In p-Pb collisions, the production cross section and the nuclear modification factor of electrons from heavy-flavour hadron decays are measured in thep_Trange 0.5< p_T<26 GeV/cat$$ \sqrt{s_{\textrm{NN}}} $$$\sqrt{s_{\mathrm{NN}}}$= 8.16 TeV. The nuclear modification factor is found to be consistent with unity within the statistical and systematic uncertainties. In both collision systems, first measurements of the yields of electrons from heavy-flavour hadron decays in different multiplicity intervals normalised to the multiplicity-integrated yield (self-normalised yield) at midrapidity are reported as a function of the self-normalised charged-particle multiplicity estimated at midrapidity. The self-normalised yields in pp and p-Pb collisions grow faster than linear with the self-normalised multiplicity. A strongp_Tdependence is observed in pp collisions, where the yield of high-p_Telectrons increases faster as a function of multiplicity than the one of low-p_Telectrons. The measurement in p-Pb collisions shows nop_Tdependence within uncertainties. The self-normalised yields in pp and p-Pb collisions are compared with measurements of other heavy-flavour, light-flavour, and strange particles, and with Monte Carlo simulations.

 

The azimuthal ($$\Delta \varphi $$$\Delta \phi$) correlation distributions between heavy-flavor decay electrons and associated charged particles are measured in pp and p–Pb collisions at$$\sqrt{s_{\mathrm{{NN}}}} = 5.02$$$\sqrt{s_{\mathrm{NN}}}=5.02$TeV. Results are reported for electrons with transverse momentum$$4$4<p_{\text{T}}<16$$$\textrm{GeV}/c$$$\text{GeV}/c$ and pseudorapidity$$|\eta |<0.6$$$\left|\eta \right|<0.6$. The associated charged particles are selected with transverse momentum$$1$1<p_{\text{T}}<7$$$\textrm{GeV}/c$$$\text{GeV}/c$, and relative pseudorapidity separation with the leading electron$$|\Delta \eta | < 1$$$\left|\Delta \eta \right|<1$. The correlation measurements are performed to study and characterize the fragmentation and hadronization of heavy quarks. The correlation structures are fitted with a constant and two von Mises functions to obtain the baseline and the near- and away-side peaks, respectively. The results from p–Pb collisions are compared with those from pp collisions to study the effects of cold nuclear matter. In the measured trigger electron and associated particle kinematic regions, the two collision systems give consistent results. The$$\Delta \varphi $$$\Delta \phi$distribution and the peak observables in pp and p–Pb collisions are compared with calculations from various Monte Carlo event generators.

 

A study of multiplicity and pseudorapidity distributions of inclusive photons measured in pp and p–Pb collisions at a center-of-mass energy per nucleon–nucleon collision of$$\sqrt{s_{\textrm{NN}}}~=~5.02$$$\sqrt{s_{\text{NN}}}=5.02$ TeV using the ALICE detector in the forward pseudorapidity region 2.3 $$<~\eta _\textrm{lab} ~<$$$<{\eta }_{\text{lab}}<$ 3.9 is presented. Measurements in p–Pb collisions are reported for two beam configurations in which the directions of the proton and lead ion beam were reversed. The pseudorapidity distributions in p–Pb collisions are obtained for seven centrality classes which are defined based on different event activity estimators, i.e., the charged-particle multiplicity measured at midrapidity as well as the energy deposited in a calorimeter at beam rapidity. The inclusive photon multiplicity distributions for both pp and p–Pb collisions are described by double negative binomial distributions. The pseudorapidity distributions of inclusive photons are compared to those of charged particles at midrapidity in pp collisions and for different centrality classes in p–Pb collisions. The results are compared to predictions from various Monte Carlo event generators. None of the generators considered in this paper reproduces the inclusive photon multiplicity distributions in the reported multiplicity range. The pseudorapidity distributions are, however, better described by the same generators.

 

Abstract A newly developed observable for correlations between symmetry planes, which characterize the direction of the anisotropic emission of produced particles, is measured in Pb–Pb collisions at $$\sqrt{s_\text {NN}}$$ s NN  = 2.76 TeV with ALICE. This so-called Gaussian Estimator allows for the first time the study of these quantities without the influence of correlations between different flow amplitudes. The centrality dependence of various correlations between two, three and four symmetry planes is presented. The ordering of magnitude between these symmetry plane correlations is discussed and the results of the Gaussian Estimator are compared with measurements of previously used estimators. The results utilizing the new estimator lead to significantly smaller correlations than reported by studies using the Scalar Product method. Furthermore, the obtained symmetry plane correlations are compared to state-of-the-art hydrodynamic model calculations for the evolution of heavy-ion collisions. While the model predictions provide a qualitative description of the data, quantitative agreement is not always observed, particularly for correlators with significant non-linear response of the medium to initial state anisotropies of the collision system. As these results provide unique and independent information, their usage in future Bayesian analysis can further constrain our knowledge on the properties of the QCD matter produced in ultrarelativistic heavy-ion collisions.