Speech Separation Methodology for Hearing Aid

In the design of hearing aids (HA), the real-time speech-enhancement is done. The digital hearing aids should provide high signal-to-noise ratio, gain improvement and should eliminate feedback. In generic hearing aids the performance towards different frequencies varies and non uniform. Existing noise cancellation and speech separation methods drops the voice magnitude under the noise environment. The performance of the HA for frequency response is non uniform. Existing noise suppression methods reduce the required signal strength also. So, the performance of uniform sub band analysis is poor when hearing aid is concern. In this paper, a speech separation method using Non-negative Matrix Factorization (NMF) algorithm is proposed for wavelet decomposition. The Proposed non-uniform filter-bank was validated by parameters like band power, Signal-to-noise ratio (SNR), Mean Square Error (MSE), Signal to Noise and Distortion Ratio (SINAD), Spurious-free dynamic range (SFDR), error and time. The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union -Telecommunication standard ITU-T P.862.     

LCC resonant converter with power factor correction for power supply units

This article presents a power supply using an LCC resonant converter having power factor correction with burst mode operation. In order to improve the performance from no load to full load, a microcontroller with an active control has been introduced. The light load efficiency is increased using burst mode operation. The proposed controller provides zero voltage switching. Mathematical analysis is done, and steady state characteristics are drawn. A simple design example is given based on the equations. The proposed converter has good efficiency with good power factor at all loading conditions. This is shown by the simulation and experimental results obtained through testing the prototype.     

Standalone PV-fed LED Street Lighting Using Resonant Converter

This paper proposes a single-stage, single-switch, non-isolated step-up converter with resonant inverter for a standalone photovoltaic battery-powered light-emitting diode (LED) street light. The LED driver presented in this paper integrates a high step-up DC–DC converter with coupled inductors and LCC resonant converter into a single-stage conversion circuit topology. The proposed converter is a compact cost-effective driver for powering a 60 W LED Street light with a battery backup charged from PV array of 12 V. The modes of operation and analysis of the proposed converter are explained. The design of the circuit elements is done based on the theoretical analysis. The proposed circuit has been simulated, and prototype has been developed to demonstrate its feasibility.     

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are ever-changing in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPID-QOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.     

Seaweed sap: a sustainable way to improve productivity of maize in North-East India

The average productivity of Maize in the north-east hill region of India is very low (1.50 t/ha) mainly owing to cultivation under nutrient-starved conditions. Practical technologies have been developed to cultivate two commercially important seaweeds, viz. Kappaphycus alvarezii and Gracilaria edulis in tropical waters. The sap of these seaweeds has turned out to be a low-cost potent plant biostimulant when applied as foliar spray. A field experiment was conducted using different concentrations of both the seaweed saps in a research farm at Umiam, Meghalaya to study their impact on growth, yield and quality of maize. Results indicated that both the seaweed saps significantly improved the grain yield (10.5–13.1%), carbohydrate (12.3–17.4%) and protein content (4.8%) of maize over and above the control which received the recommended dose of fertilizers only. Thus, these seaweed saps are proved to be an eco-friendly means for enhancing maize productivity and quality in this economically backward region.