Browsing by Author "Ponnusamy, Palanisamy"

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    Direct localization of multiple noncircular sources with a moving nested array
    (Institute of Electrical and Electronics Engineers Inc., 2019-07-18) Kumar, Gowri; Ponnusamy, Palanisamy; Amiri, Iraj Sadegh
    This paper proposes a subspace data fusion (SDF)-based direct positioning determination (DPD) method for noncircular sources with a moving nested array (NA). The DPD algorithms using a uniform linear array (ULA) and coprime (CP) array are available in the existing literature. Sparse arrays have larger apertures than ULAs, which enhances degrees of freedom (DOFs). However, the existing sparse CP arrays have limited DOF and also have poor detecting ability. In this paper, the physical sensors of NA are rearranged to obtain a difference sum NA (DSNA) and second-order super NA (II-SNA). The rearranged NA sensors are seen to increase array aperture. The noncircular characteristics of the sources are also used to improve accuracy in positioning. To obtain high DOF in NA, DSNA, and II-SNA, the covariance matrices are vectorized. The coherency of the virtual array is resolved by applying the spatial smoothing technique. Finally, the SDF-based DPD is used to establish the cost function and the target is localized. The simulation results are provided for the NA, DSNA, and II-SNA and are compared with the existing CP array DPD algorithm. The results show that the proposed method shows signi-cant enhancement in localization accuracy. The Cramer-Rao lower bound (CRLB), complexity, and performance comparisons are also described in this paper.
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    Disrupted Brain Network Measures in Parkinson’s Disease Patients with Severe Hyposmia and Cognitively Normal Ability
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-07-08) Siva, Karthik; Ponnusamy, Palanisamy; Ramanathan, Malmathanraj
    Neuroscience has revolved around brain structural changes, functional activity, and connectivity alteration in Parkinson’s Disease (PD); however, how the network topology organization becomes altered is still unclear, specifically in Parkinson’s patients with severe hyposmia. In this study, we have examined the functional network topological alteration in patients affected by Parkinson’s Disease with normal cognitive ability (ODN), Parkinson’s Disease with severe hyposmia (ODP), and healthy controls (HCs) using resting-state functional magnetic resonance imaging (rsfMRI) data. We have analyzed brain topological organization using popular graph measures such as network segregation (clustering coefficient, modularity), network integration (participation coefficient, path length), small-worldness, efficiency, centrality, and assortativity. Then, we used a feature ranking approach based on the diagonal adaptation of neighborhood component analysis, aiming to determine a graph measure that is sensitive enough to distinguish between these three different groups. We noted significantly lower segregation and local efficiency and small-worldness in ODP compared to ODN and HCs. On the contrary, we did not find differences in network integration in ODP compared to ODN and HCs, which indicates that the brain network becomes fragmented in ODP. At the brain network level, a progressive increase in the DMN (Default Mode Network) was observed from healthy controls to ODN to ODP, and a continuous decrease in the cingulo-opercular network was observed from healthy controls to ODN to ODP. Further, the feature ranking approach has shown that the whole-brain clustering coefficient and small-worldness are sensitive measures to classify ODP vs. ODN, as well as HCs. Looking at the brain regional network segregation, we have found that the cerebellum and limbic, fronto-parietal, and occipital lobes have higher ODP reductions than ODN and HCs. Our results suggest network topological measures, specifically whole-brain segregation and small-worldness decreases. At the network level, an increase in DMN and a decrease in the cingulo-opercular network could be used as biomarkers to characterize ODN and ODP.
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    Polarization difference smoothing in bistatic MIMO radar
    (Electromagnetics Academy, 2019-12-09) Subramaniam, Karthick; Ponnusamy, Palanisamy; Chintagunta, Srinivasarao
    This paper investigates the joint direction of departure (DOD) and direction of arrival (DOA) estimation of coherent targets in bistatic multiple-input multiple-output (MIMO) radar under the presence of spatially correlated noise. Based on electromagnetic vector sensors at both transmitter and receiver of MIMO radar, a preprocessing method, namely polarization difference smoothing, is proposed to remove the coherence between targets and to suppress the spatially correlated noise. Then DODs and DOAs are estimated using the ESPRIT method. Further, this paper develops a simple approach for pair-matching between the estimated DODs and DOAs. Simulation results are compared with the receive polarization smoothing and transmit-receive polarization smoothing methods available in literature. Results show that the proposed approach improves the performance significantly.
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    Spatial and polarization angle estimation of mixed-targets in MIMO radar
    (Electromagnetics Academy, 2019-06-20) Chintagunta, Srinivasarao; Ponnusamy, Palanisamy
    This paper proposes an approach for estimating the spatial and polarization angles of mixed-targets in bistatic MIMO radar. Mixed-targets mean the combination of uncorrelated, partially correlated, and groups of coherent targets. The approach resolves rank deficiency of received signal covariance matrix and then exploits the ESPRIT-based method for estimating the angles of direction-of-departure (DOD) and direction-of-arrival (DOA). This paper also presents an analytical review and necessary conditions for resolving the rank deficiency under various scenarios of the MIMO radar. Simulation results show the effectiveness of the proposed approach.
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    The Effect of Dopaminergic Therapy in Parkinson’s Disease: A Graph Theory Analysis
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-04-02) Siva, Karthik; Ponnusamy, Palanisamy; Chavda, Vishal; Montemurro, Nicola
    Background: Dopaminergic therapy (DT) is the gold standard pharmacological treatment for Parkinson’s disease (PD). Currently, understanding the neuromodulation effect in the brain of PD after DT is important for doctors to optimize doses and identify the adverse effects of medication. The objective of this study is to investigate the brain connectivity alteration with and without DT in PD using resting-state EEG. Methods: Graph theory (GT) is an efficient technique for analyzing brain connectivity alteration in healthy and patient groups. We applied GT analyses on three groups, namely healthy control (HC), Parkinson with medication OFF (PD-OFF), and Parkinson with medication ON (PD-ON). Results: Using the clustering coefficient (CC), participation coefficient (PC), and small-worldness (SW) properties of GT, we showed that PD-ON patients’ brain connectivity normalized towards healthy group brain connectivity due to DT. This normalization effect appeared in the brain connectivity of all EEG frequency bands, such as theta, alpha, beta-1, beta-2, and gamma except the delta band. We also analyzed region-wise brain connectivity between 10 regions of interest (ROIs) (right and left frontal, right and left temporal, right and left parietal, right and left occipital, upper and lower midline regions) at the scalp level and compared across conditions. During PD-ON, we observed a significant decrease in alpha band connectivity between right frontal and left parietal (p-value 0.0432) and right frontal and left occipital (p-value 0.008) as well as right frontal and right temporal (p-value 0.041). Conclusion: These findings offer new insights into how dopaminergic therapy modulates brain connectivity across frequency bands and highlight the continuous elevation of both the segregation and small-worldness of the delta band even after medication as a potential biomarker for adverse effects due to medication. Additionally, reduced frontal alpha band connectivity is associated with cognitive impairment and levodopa-induced dyskinesia, highlighting its potential role in Parkinson’s disease progression. This study underscores the need for personalized treatments that address both motor and non-motor symptoms in PD patients.