Abstract:Nanometer scale power amplifiers (PA) at sub-THz suffer from severe parasitic effects that lead to experience limited maximum frequency and reduced power performance at the device transceiver front end. The integrated circuits researchers proposed different PA design architecture combinations at scaled down technologies to overcome these limitations. Although the designs meet the minimum requirements, the power added efficiency (PAE) of PA is still quite low. In this paper, a W-band single-ended common-source (CS) and cascode integrated 3-stage 2-way PA design is proposed. The design integrated different key design methodologies to mitigate the parasitic; such as combined Class AB and Class A stages for gain-boosting and efficiency enhancement, Wilkinson power combiner for higher output power, linearity, and bandwidth, and transmission line (TL)-based wide band matching network for better inter-stage matching and compact size. The proposed PA design is validated using UMS 150-nm GaAs pHEMT using advanced design system (ADS) simulator. The results show that the proposed PA achieved a gain of 20.1 dB, an output power of 17.2 dBm, a PAE of 33 % and a 21 GHz bandwidth at 90 GHz Sub-THz band. The PA layout consumes only 5.66 X 2.51 mm2 die space including pads. Our proposed PA design will boost the research on sub-THz integrated circuits research and will smooth the wide spread adoption of 6G in near future.
Abstract:Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in biomechanical changes in human gait. In literature, for the last 50 years, researchers are trying to observe the biomechanical changes due to DSPN by studying muscle electromyography (EMG), and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we are proposing to use Machine learning techniques to identify DSPN patients by using EMG, and GRF data. We have collected a dataset consists of three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius medialis (GM) and 3-dimensional GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and a newly proposed feature extraction technique scheme from literature was applied to extract the best features from the signals. The extracted feature list was ranked using Relief feature ranking techniques, and highly correlated features were removed. We have trained different ML models to find out the best-performing model and optimized that model. We trained the optimized ML models for different combinations of muscles and GRF components features, and the performance matrix was evaluated. This study has found ensemble classifier model was performing in identifying DSPN Severity, and we optimized it before training. For EMG analysis, we have found the best accuracy of 92.89% using the Top 14 features for features from GL, VL and TA muscles combined. In the GRF analysis, the model showed 94.78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals. The performance of ML-based DSPN severity classification models, improved significantly, indicating their reliability in DSPN severity classification, for biomechanical data.