69 research outputs found
Multi-beam object-localization for millimeter-wave ISAC-aided connected autonomous vehicles
No full textMillimeter wave (mmWave) multiple-input multiple-output (MIMO) systems capable of integrated sensing and communication (ISAC) constitute a key technology for connected autonomous vehicles (CAVs). In this context, we propose a multi-beam object-localization (MBOL) model for enhancing the sensing beampattern (SBP) gain of adjacent objects in CAV scenarios. Given the ultra-narrow beams of mmWave MIMO systems, a single pencil beam is unsuitable for closely located objects, which tend to require multiple beams. Hence, we formulate the SBP gain maximization problem, considering also the constraints on the signal-to-interference and noise ratio (SINR) of the communication users (CUs), on the transmit power, and the constant modulus of the phase-shifters in the mmWave hybrid transceiver. To solve this non-convex problem, we propose a penalty-based triple alternating optimization algorithm to design the hybrid beamformer. Finally, simulation results are provided for demonstrating the efficacy of the proposed model
An affine precoded superimposed pilot based mmWave MIMO-OFDM ISAC system
No full textA new affine-precoded superimposed pilot (AP-SIP) scheme is conceived for both wireless channel and radar target parameter estimation in a millimeter wave (mmWave) multiple-input multipleoutput (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems. The AP-SIP scheme leads to enhanced estimation accuracy and improved utilization of spectral resources. Initially, the pilot-assisted radar (PAR) and data-assisted radar (DAR) parameter estimation models are separately developed for the estimation of the radar target parameters. Subsequently, these are combined into a joint pilot-data radar (JPDR) model for simultaneously harnessing both the signals to further boost the estimation accuracy. The sparse Bayesian learning (BL)-based joint-BL (J-BL) technique is developed for this system that efficiently exploits the sparsity of the radar scattering environment. Next, a group sparse BL (G-BL) technique is also derived that exploits the group sparsity across subcarriers for the estimation of the wireless beamspace channel vector, which outperforms the competing techniques, including conventional sparse BL. The optimal pilot, transmit precoder (TPC) and receive combiner (RC) are determined at the dual-function radar-communication (DFRC) base station (BS) and also at the user equipment (UE) for maximizing the performance attained. The Bayesian Cramer-Rao bounds (BCRB) are explicitly derived to benchmark the performance of the wireless channel and radartarget parameter estimation. Simulation results are provided to demonstrate the improved performance of the proposed schemes considering multiple metrics, such as the normalized mean squared error (NMSE), bit error rate (BER) and achievable spectral efficiency (ASE)
Bayesian learning aided parameter estimation and joint beamformer design in mmWave MIMO-OFDM ISAC Systems
No full textA three-dimensional (3D) sparse signal recovery problem formulation is conceived for delay, Doppler, and angular (DDA) domain target parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems relying on a hybrid beamforming architecture. Subsequently, a 3D-sparse Bayesian learning (3D-BL) algorithm is proposed to jointly estimate the angular, range, velocity, and radar cross-section (RCS) parameters of the targets. Furthermore, an uplink beamformer is designed for the user equipment (UE) to alleviate the complexity of uplink parameter estimation at the dual-functional radar-communication (DFRC) base station (BS) by eliminating the need for angle of departure (AoD) estimation. Additionally, a Bayesian alternating minimization (BAT-MIN) algorithm is constructed for the designing of a DFRC waveform, enabling the simultaneous generation of beams toward both the radar targets and the UE. Furthermore, the sparse Bayesian learning lower bound (SBL-LB) and the Bayesian Cramér-Rao lower bound (BCRLB) are derived to serve as benchmarks for estimation performance. Finally, simulation results are presented to showcase the enhanced performance of the proposed methodologies in terms of multiple performance metrics when contrasted both to the existing sparse recovery techniques and to conventional non-sparse parameter estimation algorithms. The simulation outcomes unequivocally demonstrate the commendable performance of the proposed 3D-BL estimation methodology, approaching closely to the SBL-LB. Notably, this approach exhibits a substantial gain of at least 5 dB when compared to alternative techniques. Additionally, the introduced BAT-MIN beamformer emerges as a highly competitive solution, closely approximating the capabilities of a fully digital beamformer while maintaining a noteworthy minimum advantage over its contemporaries. These findings underscore the significance and efficacy of the proposed techniques in the context of advanced signal processing and beamforming
Quantitative and Rapid Antibacterial Assay of Micromeria biflora Benth. Leaf Essential Oil Against Dental Caries Causing Bacteria Using Phylogenetic Approach
No full textAbstractThe phylogenetic relationship of four dental caries causing bacterial pathogens has been studied using ITS1 sequences of the standard strains were aligned by using the ClustalW computer program. The essential oil obtained from the leaves of Micromeria biflora Benth., obtained by hydrodistillaton. The chemical compositions of the essential oil from Micromeria biflora Benth was analyzed by gas chromatography-mass spectrometry (GC-MS). The GC/MS analysis showed eight major active constituents in the leaf essential oil of Micromeria biflora Benth. The antibacterial activity of the oil was evaluated against four dental caries causing bacteria such as Streptococcus mutans (MTCC 890); Lactobacillus acidophilus (MTCC 447); Streptococcus mitis (MTCC 2695) and Streptococcus salivarius (MTCC 1938) using broth microdilution method recommended by Clinical Laboratory Standards Institute (CLSI) formely (NCCLS). It’s showed excellent activity against Streptococcus mutans with their Minimum inhibition concentration (MIC) 0.15 mg/ml and (IC50) 0.10 mg/ml and less effective against Lactobacillus acidophilus. The essential oil of Micromeria biflora Benth from leaf has played a significant role against dental caries causing bacteria. Relationships of the dental caries causing pathogens to the toxicity of the oil vis-à -vis phylogeny using molecular data of pathogens have also been discussed. 1Biological Product Laboratory, Dept. of Botany, University of Allahabad, Allahabad-2110022Dept. of Horticulture, Aromatic & Medicinal Plants, Mizoram University, Aizawl-796009, India 3Dental Surgeon, Saumya Dental Clinic, Taigore Town, Allahabad, India*Corresponding author, Email: [email protected], Tel: +919335108519Please Cite This Article As:  Rohit Kumar Mishra, Awadhesh Kumar, Amritesh Chandra Shukla, Pravin Tiwari and Anupam Dikshit. 2010. Quantitative and Rapid Antibacterial Assay of Micromeria biflora Benth. Leaf Essential Oil Against Dental Caries Causing Bacteria Using Phylogenetic Approach. J. Ecobiotechnol. 2(4):22-26
Range-based Primary User Localization in Cognitive Radio Networks
No full textAbstractIn cognitive radio networks (CRNs), information regarding the positions of the primary users (PUs) is crucial as it may be helpful for improving the spectrum utilization and to avoid harmful interference. Existing Range-based methods require three or more than three known secondary users (SUs) to estimate primary user location and hence have the limitation that they cannot work if there are only two known secondary users available in the system. Also the existing methods cannot work if the precondition for applying Trilateration method is not satisfied (i.e. no common intersection point between the circles drawn for the secondary users). In this article we present Range based primary user localization technique to find out the location of PUs along with their transmit power. The proposed technique relies on the information collected from only two SUs to carry out the localization process unlike the existing techniques
MODEL ORDER REDUCTION USING CHEBYSHEV POLYNOMIAL, STABILITY EQUATION METHOD AND FUZZY C-MEANS CLUSTERING
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