Abstract

The localization of acoustic sources is a parameter estimation problem where the parameters of interest are the direction of arrivals (DOAs). The DOA estimation problem can be formulated as a sparse parameter estimation problem and solved using compressive sensing (CS) methods. In this paper, the CS method of sparse Bayesian learning (SBL) is used to find the DOAs. We specifically use multi-frequency SBL leading to a non-convex optimization problem, which is solved using fixed-point iterations. We evaluate SBL along with traditional DOA estimation methods of conventional beamforming (CBF) and multiple signal classification (MUSIC) on various source localization tasks from the open access LOCATA dataset. The comparative study shows that SBL significantly outperforms CBF and MUSIC on all the considered tasks.

Abstract

Increasing demands of wearable health monitoring seek dedicated spectrum for short range wireless communication of biosignals, for which frequency band near 400 MHz has been utilized recently. While using the dedicated spectrum, biosignal communication must not cause any out of band radiations and interference, for which there is a strong need to develop spurfree, low phase noise and energy efficient wireless transmitters (TX). In this research, for the first time we present design and implementation of a reference-spur-free, low phase noise, sub-mW dual modulation mode (frequency shift keying (FSK) and on-off keying (OOK)) 400 MHz TX for wearable health monitoring, where RF synthesis depends solely on an extremely low phase noise oscillator, which uses a 400 MHz surface acoustic wave resonator and does not include any power hungry, spurgenerating frequency multiplier or phase/frequency lock loop. The proposed TX has been designed and fabricated in 180 nm CMOS technology. Measurement results show that the energy efficiencies of the proposed TX in FSK and OOK mode are 1.8 nJ/bit at 250 kb/s and 0.23 nJ/bit at 2 Mb/s, respectively. To prove the utility of the proposed TX, biosignal communication is also demonstrated with the help of a commercial receiver in actual operating scenarios, where the measured bit error rate in FSK mode was < 10−5 for range of 2 m. Index Terms—Health-monitoring, wearable, spur-free, transmitter, FSK, OOK, 400 MHz, MedRadio

Abstract

The U1 leptoquark is known to be a suitable candidate for explaining the semileptonic B-decay anomalies. We derive precise limits on its parameter space relevant for the anomalies from the current LHC high-pT dilepton data. We consider an exhaustive list of possible B-anomalies-motivated simple scenarios with one or two new couplings that can also be used as templates for obtaining bounds on more complicated scenarios. To obtain precise limits, we systematically consider all possible U1 production processes that can contribute to the dilepton searches, including the resonant pair and single productions, nonresonant t-channel U1 exchange, as well as its large interference with the Standard Model background. We demonstrate how the inclusion of resonant production contributions in the dilepton signal can lead to appreciably improved exclusion limits. We point out new search channels of U1 that can act as unique tests of the flavour-motivated models. The template scenarios can also be used for future U1 searches at the LHC. We compare the LHC limits with other relevant flavour bounds and find that a TeV-scale U1 can accommodate both RD(∗) and RK(∗) anomalies while satisfying all the bounds.

Abstract

The U1 leptoquark is known to be a suitable candidate for explaining the semileptonic B-decay anomalies. We derive precise limits on its parameter space relevant for the anomalies from the current LHC high-pT dilepton data. We consider an exhaustive list of possible B-anomalies-motivated simple scenarios with one or two new couplings that can also be used as templates for obtaining bounds on more complicated scenarios. To obtain precise limits, we systematically consider all possible U1 production processes that can contribute to the dilepton searches, including the resonant pair and single productions, nonresonant t-channel U1 exchange, as well as its large interference with the Standard Model background. We demonstrate how the inclusion of resonant production contributions in the dilepton signal can lead to appreciably improved exclusion limits. We point out new search channels of U1 that can act as unique tests of the flavor-motivated models. The template scenarios can also be used for future U1 searches at the LHC. We compare the LHC limits with other relevant flavor bounds and find that a TeV-scale U1 can accommodate both RDðÞ and RKðÞ anomalies while satisfying all the bounds.

Abstract

: Reference Evapotranspiration (ET0) is a complex hydrological variable defined by various climatic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. Conventionally ET0 is calculated by various empirical methods based on rigorous climatic data. However, there are many places where various climatic data may not available for ET0 estimation. The objective of this study is to evaluate different machine learning (ML) techniques to estimate ET0 with minimal climatic inputs. In this study, FAO56 Penman-Monteith model was considered as the standard model and different ML models based on A Long short-term memory neural networks (LSTM), Gradient Boosting Regressor (GBR), Random Forest (RF) and Support Vector Regression (SVR) were developed to estimate ET0 with climatic variables as input parameters. These models were evaluated in two different climatic regions, Hyderabad in India and Waipara in New Zealand. The results indicated that 99 % accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with fewer data. LSTM model performed better than other ML models with all input combinations at both the stations, followed by SVR and RF. Both LSTM and SVR models have been noted as the most robust ML models for estimating ET0 with minimal climate data. Even though the excellent performance can be achievable when all input variables are used, the study, however, found that even a three-parameter combination (Temperature, Wind Speed and Relative Humidity values) or two-parameter combination (Temperature and Relative Humidity, Temperature and Wind Speed) can also be promising in ET0 estimation. The presented study will help to estimate ET0 for data scare regions, which is vital for agricultural water management in semi-arid climates.

Abstract

In this paper, we quantitatively show the energy savings obtained in a heterogeneous wireless sensor network, by using Weighted Centriod algorithm. Due to the energy constraints on the sensor nodes in hierarchal networks efficient cluster head selection has become crucial factor. We propose an algorithm, in which all the nodes do not necessarily have the same initial energy. Our main contribution is to derive the location of a weighted Centroid which minimizes weights sum of squares distances from the sensor nodes to the cluster head location. Analytical results are validated through simulation in OMNET++ by using various performance metrics and compared with different cluster head selection protocols such as I-Leach, Leach-E and NR-Leach. This shows the promising potentials of our Weighted Centriod algorithm.

Abstract

Wireless Sensors that are small in size and limited battery capacity are widely used for several different applications. For this reason energy efficient as well as cost of the overall network are the two main factors influencing the Wireless Sensor Networks (WSN) performance. In this research paper, in Wireless Sensor Network, energy efficient placement of sensors on the uniform rectangular grid (as sensor field) is proposed. Also, distributed computation of minimum and/or maximum over such sensor field is discussed (for minimizing computations and communications delay). Index Terms—Sensor Placement, Leveling, Clustering, Energy Efficiency, Network Lifetime, Cost Minimization,

Abstract

Over the past two decades, a growing thrust area of research has been electrification of different mechanical and hydraulic systems in ground vehicles, which offers less environmental concerns due to the removal of hydraulic fluids and continual engine parasitic losses. The electrical equivalents for traditional mechanical linkages and hydraulic power assist systems include ‘brakeby-wire’, ‘steer-by-wire’ and ‘throttle-by-wire’ system. These equivalents, collectively known as ‘X-by-Wire’ technologies, form the fundamental structure for the Semi and Fully Autonomous Driving Systems 1 . As compared to a traditional steering system, a Steer-by-Wire (SBW) system removes the mechanical shaft that connects the steering column and steering pinion: therefore, in absence of any physical connection, the steering input is transmitted via a by-wire electronic communication module 2 (cf. Fig. 1). In order to have the SBW equipped vehicle have the similar features to that of a conventional steering system, it is important to devise a tracking control mechanism for the steering actuator such that the steering commands from hand-wheel are followed as precisely as possible. Initial attempts were made to solve such control problem employing conventional PID controller 3,4; however, in presence of inevitable modelling imprecision and parametric uncertainties, advanced control strategies are eventually

Abstract

In this research paper storage as well as retrieval of 1-D/2-D/3-D information using Hopfield type Associative Memories (AMs) is discussed. Various Artificial Neural Networks(ANN) architectures are proposed. Also, implementational issues associated with those Associative Memories are discussed. Cascade connection of AM and Convolutional Neural Network is proposed for noise immunity. Keywords: Associative Memories, Hopfield Network, Ceiling Neuron

Abstract

In this chapter, we introduce Hopfield neural networks based on the ceiling neuron model. Inspired by the binary Hopfield neural network and by the definition of ceiling neuron, we propose an extension of the Hopfield neural network from a binary set of possible states of a real-valued neuron to a multistate set. We investigate the dynamics of the proposed model in asynchronous and synchronous update modes. Computational experiments illustrate that the Hopfield neural network based on the ceiling neuron in an asynchronous update mode always settles down to a stationary state under the usual conditions on synaptic weights, that is, the synaptic weight matrix is symmetric with non-negative diagonal elements. In turn, the Hopfield neural network with ceiling neurons in the synchronous update mode can generate limit cycles, similarly to the classic Hopfield neural network.