Parkinson's disease is a serious neurological impairment which adversely affects the quality of life in individuals. While there currently does not exist any cure for this disease, it is well known that early diagnosis can be used to improve the quality of life of affected individuals through various types of therapy. Speech based screening of Parkinson's disease is an active area of research intending to offer a non-invasive and passive tool for clinicians to monitor changes in voice that arise due to Parkinson's disease. Whereas traditional methods for speech based identification rely on domain-knowledge based hand-crafted features, in this paper, we investigate the efficacy of and propose the deep acoustic embeddings for identification of Parkinsonian speech. To this end, we conduct several experiments to benchmark deep acoustic embeddings against handcrafted features for differentiating between speech from individuals with Parkinson's disease and those who are healthy. We report that deep acoustic embeddings consistently perform better than domain-knowledge features. We also report on the usefulness of decision-level fusion for improving the classification performance of a model trained on these embeddings.

The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy.

The massive multiple-input-multiple-output (MIMO) is a key enabling technology for the 5G cellular communication systems. In massive MIMO (M-MIMO) systems few hundred numbers of antennas are deployed at each base station (BS) to serve a relatively small number of single-antenna terminals with multiuser, providing higher data rate and lower latency. In this paper, an M-MIMO communication system with a large number of BS antennas with zero-forcing beamforming is proposed for the improved spectral efficiency performance of the system. The zero forcing beamforming technique is used to overcome the interference that limits the spectral efficiency of M-MIMO communication systems. The simulation results authenticate the improvement in the spectral efficiency of M-MIMO system. The spectral efficiency value using zero-forcing beamforming is near to the spectral efficiency value with the nointerference scenario.

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