Abstract

This paper reports solvent based optimization of radio frequency (RF) sensors for detecting ethanol mixtures in petrol using complementary split ring resonators (CSRR). The optimization is carried out using binary particle swarm optimization (BPSO) in presence of mask by placing the sample under test (SUT) above the highly sensitive CSRR region. The motivation behind this work has been to detect miniscule changes of ethanol in the dielectric constant of the petrol. The RF sensors designed using BPSO enables faster convergence providing more efficient designs offering enhanced sensitivity at a normalized frequency of 2.66% and 1.81% when compared to 1.02% of conventional CSRR as presented in the simulation results. Index Terms

Abstract

This paper demonstrates radio frequency (RF) sens- ing of saccharides such as glucose, fructose and sucrose based on Complementary Split Ring Resonator (CSRR). The CSRR based planar sensor is designed and fabricated over FR4 substrate of thickness 1.6 mm and with copper on both sides of thickness 0.035 mm. The fabricated sensor is connected to the Vector Network Analyzer (VNA) to study the electromagnetic interac- tions behavior between the designed sensor and saccharides. The quantitative and qualitative analysis is conducted by changing the concentration of saccharides in aqueous solution from 10 mg/ml to 50 mg/ml in steps of 10 mg/ml and then by changing the overall sample volume from 1 μl to 100 μl in variable step sizes. The resonant frequency property is utilized for detecting small changes in saccharides concentration of up to 10 mg/ml and overall sample volume change of 1 μl. The proposed RF sensor is highly robust, reliable, cost-effective and easy to use over other existing methodologies. Index Terms—Radio Frequency, Mic

Abstract

We have analyzed the thermal blooming effect on laser beam propagation under atmospheric turbulence in different environments. It is observed that the laser source having an input power of less than 4 kW is less affected by thermal blooming and more appropriate for incoherent beam combinations in any environmental conditions.

Abstract

The unmanned aerial vehicle (UAV) based communication is expected to play an important role in enabling a variety of applications in future cellular networks. However, because of the mobility of the UAVs, the communications links involving UAVs undergo large-scale temporal variations in the received signal quality, which may affect the quality-of-service of the underlying application. Therefore, it is crucial to characterize the time-varying process of signal quality observed by the UAVs. In this paper, we consider a scenario in which a cellular-connected UAV acts as a user equipment (UAV-UE), where the locations of base stations (BSs) follow a Poisson point process (PPP) and the UAV-UE is moving along a 3GPP-inspired straight-line trajectory. For this setting, we study the properties of the time-varying successful transmission process that is defined in terms of the time-varying signal-to-noise ratio (SNR) observed at the UAV. In particular, we show that this process is a wide sense stationary (WSS) process and derive its first- and second-order statistics. Finally, we establish an equivalence between the successful transmission processes observed by a UAV-UE served by terrestrial BSs and a terrestrial user served by UAV mounted BSs (UAV-BSs) each moving along an independent straight-line trajectory.

Abstract

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SGbased model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40% and 25% respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals. Index Terms—Network Performance Prediction, Convolutional Neural Network, Stochastic Geometry, Network Design

Abstract

This paper considers beamforming for a reconfigurable intelligent surface (RIS)-aided multiple input single output (MISO) communication system in the presence of Rician multipath fading. Our aim is to jointly optimize the transmit beamformer and RIS phase shift matrix for maximizing the mean signal-to-noise (SNR) of the combined signal received over direct and indirect links. While numerical solutions are known for such optimization problems, this is the first paper to derive closedform expressions for the optimal beamformer and the phase shifter for a closely related problem. In particular, we maximize a carefully constructed lower bound of the mean SNR, which is more conducive to analytical treatment. Further, we show that effective channel gain under optimal beamforming follows Rice distribution. Next, we use these results to characterize a closedform expression for the outage probability under the proposed beamforming scheme, which is subsequently employed to derive an analytical expression for the ergodic capacity. Finally, we numerically demonstrate the efficacy of the proposed beamformer solution in comparison with the existing algorithmically obtained optimal solution for the exact mean SNR maximization. Index Terms—RIS, Optimal Beamforming, Rician Channel, Outage Analysis, Ergodic Capacity, and Maximum Mean SNR.

Abstract

State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in highdimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples. Index Terms—Particle filters, particle flow filters, Gaussian particle filters, particle flow particle filters.

Abstract

Sparse Bayesian learning (SBL) has been successful in direction of arrival (DOA) estimation due to its robustness and high resolution using a few snapshots. Most wideband SBL algorithms make the simplifying assumption that distinct sources have the same power spectrum across frequency bands. However, this assumption may not be true in practice (for example speech signals). We analyze three wideband signal models and compare variants of wideband SBL (called SBL1, SBL2, and SBL3) with different assumptions on source signal power spectrum. The localization performance of SBL algorithms is compared with wideband processing of conventional beamforming (CBF) and multiple signal classification (MUSIC). The experimental validation is presented using simulated data and experimental LOCATA data. This comparative study shows that SBL3 which simultaneously enforces sparsity and models frequency-dependent signal spectrum shows superior performance in most scenarios. Index Terms—Compressive sensing, Sparse Bayesian learning, DOA estimation, MUSIC, CBF.

Abstract

Sound source localization is crucial in acoustic sensing and monitoring-related applications. In this paper, we do a comprehensive analysis of improvement in sound source localization by combining the direction of arrivals (DOAs) with their derivatives which quantify the changes in the positions of sources over time. This study uses the SALSA-Lite feature with a convolutional recurrent neural network (CRNN) model for predicting DOAs and their first-order derivatives. An update rule is introduced to combine the predicted DOAs with the estimated derivatives to obtain the final DOAs. The experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. We compare the performance of the networks predicting DOAs with derivative vs. the one predicting only the DOAs at low SNR levels. The results show that combining the derivatives with the DOAs improves the localization accuracy of moving sources. Index Terms— Deep learning, Microphone array, SALSALite, Sound event localization and detection (SELD).

Abstract

n the rapidly growing computing world, the data is being generated every minute, and it has been a big challenge to manage and maintain privacy of the data when it is required to share among different locations. In this regard the privacy preserving data mining is playing a major role to design and develop the methods and models to preserve privacy of data in data owners perspectives, when ever the necessity of exchanging the information between multiple parties to be done. There has been lot of research going on in the field of computational intelligence, machine learning and soft computing domains and some good number of algorithms and secured models are being developed by the researchers. We have worked on the random forest classification as the best example model to preserve privacy of two parties when a combined classification to be done in order to build a privacy preserving global random …