Supercapacitor electrodes synthesized from activated carbon (AC) has high energy and power capabilities as they have larger surface area, greater conductivity, and also AC has the ability to optimize the properties of supercapacitors. Supercapacitor has gained its attention due to its fast charging/discharging speed and long-term stability than the normal batteries. In this work, GO/PPy/AC composite electrodes was synthesized to increase the specific capacitance and the energy storage capability of supercapacitor through modified hummers’ method, sacrificial template polymerization method, and hydrothermal method. Here, the AC was derived from seeds of Ziziphus jujuba and shells of Prunus dulcis. The performances of GO, GO/PPy, GO/PPy/ACZJ, and GO/PPy/ACPD electrodes were evaluated using 6 M KOH electrolyte at different current densities and scan rates. The electrochemical properties of the electrodes were characterized by CV, GCD, and EIS analysis to study the suitability of the electrode material. GO/PPy/ACPD electrode exhibited the specific capacitance of 1217.1, 456.67, 270.44, and 90.88 F g-1 with current densities of 1, 2, 4, and 10 A g-1, respectively. GO/PPy/ACPD has high specific capacitance of 1217.1 F g-1 at 1 A g-1. The enhanced electrochemical performance is due to better surface area and higher specific capacitance.
Speech enhancement is a technique used to reduce the background noise present in the speech signal. It simply means the improvement in intelligibility and quality of degraded speech. The noises present in the speech signal are additive noise, echo, reverbration and speaker interference. The aim of the proposed method is to reduce the background noise present in the speech signal by using spectral subtraction techniques. The magnitude of the spectrum of estimated noise is subtracted from the spectrum of noisy speech signal. Five clean speeches are taken as sample speech. Sample noise such as pink noise, white noise and volvo noise are taken from database (TIMIT & NOIZEUS corpus). By using Non-linear spectral subtraction and Multiband spectral subtraction techniques, enhanced speech is obtained. Performance of the above two methods are compared based on the two parameters namely Signal to Noise Ratio and Log Spectral Distance.
Monitoring fruit quality, volume, and development on the plantation are critical to ensuring that the fruits are harvested at the optimal time. Fruits are more susceptible to the disease while they are actively growing. It is possible to safeguard and enhance agricultural productivity by early detection of fruit diseases. A huge farm makes it tough to inspect each tree to learn about its fruit personally. There are several applications for image processing with the Internet of Things (IoT) in various fields. To safeguard the fruit trees from illness and weather conditions, it is difficult for the farmers and their workers to regularly examine these large areas. With the advent of Precision Farming, a new way of thinking about agriculture has emerged, incorporating cutting-edge technological innovations. One of the modern farmers’ biggest challenges is detecting fruit diseases in their early stages. If infections aren’t identified in time, farmers might see a drop in income. Hence this paper is about an Artificial Intelligence Based Fruit Disease Identification System (AI-FDIS) with a drone system featuring a high-accuracy camera, substantial computing capability, and connectivity for precision farming. As a result, it is possible to monitor large agricultural areas precisely, identify diseased plants, and decide on the chemical to spray and the precise dosage to use. It is connected to a cloud server that receives images and generates information from these images, including crop production projections. The farm base can interface with the system with a user-friendly Human-Robot Interface (HRI). It is possible to handle a vast area of farmland daily using this method. The agricultural drone is used to reduce environmental impact and boost crop productivity.
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