3,386 research outputs found
A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
No full textA 2D Discrete Cosine Transform and Inverse Discrete Cosine Transform using the B.G. Lee algorithm, incorporating a signed error-tolerant adder for additions, and a signed low-power fixed-point multiplier to perform multiplications are proposed and designed in this research. A novel Application Specific Integrated Circuit hardware implementation is used for the 2D DCT/IDCT computation of each 8 × 8 image block by optimizing the input data using the concepts of pipelining. An enhanced speed in processing and optimized arithmetic computations was observed due to the eight-stage pipeline architecture. The 2D DCT/IDCT of each 8 × 8 image segment can be quickly processed in 34 clock cycles with a substantially reduced level of circuit complexity. The B.G. Lee algorithm has been implemented using signed error-tolerant adders, signed fixed-point multipliers, and shifters, reducing computational complexity, power, and area. The Cadence Genus tool synthesized the proposed architecture with gpdk-90 nm and gpdk-45 nm technology libraries. The proposed method showed a significant reduction of 31.01%, 12.17%, and 21.11% in power, area, and PDP in comparison to the existing image compression architectures. An improved PSNR of the reconstructed image was also achieved compared to existing designs
Posture Control of a Free Falling Robotic Cat for Soft Landing Using Reinforcement Learing
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A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications
No full textFunding Information: Acknowledgments: S.W.K.J, V.V., P.P. and B.G. acknowledge the support from Lee Kong Chian School of Medicine and Data Science and AI Research (DSAIR) Centre of NTU (Project Number ADH‐11/2017‐DSAIR and the support from the Cognitive Neuroimaging Centre (CONIC) at Nan‐ yang Technological University, Singapore. Publisher Copyright: © 2022 by the author. Licensee MDPI, Basel, Switzerland.Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal‐to‐noise ratio (SNRMEG =2.2 db, SNREEG <1 db) and spatial resolution (SRMEG =2–3 mm, SREEG =7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single‐channel con-nectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.Peer reviewe
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