Design and Development of Soil Moisture Based Automatic Irrigation System in Nepal

A prototype soil moisture based an automated irrigation system were developed at National Maize Research Program in 2018 to study the water requirement of drought tolerant crop genotype. The irrigation system has been controlled by Arduino UNO as a micro controller. The instant soil moisture data were collected either in Excel format or graphical format using internet of things through the programming of Global System for Mobile Communication: Subscriber Identity Module (GSM:SIM card) of Nepal Telecom. The developed automated irrigation system has found maintained the predetermined threshold soil moisture. This automated irrigation system has been developed to make applicable for drip irrigation system which has operated at low water pressure maintained by 1.5² professional-grade solenoid valve. The introduction of this automated irrigation system has developed the base for Nepalese agricultural scientist in designing and promoting irrigation technology to make Nepalese agricultural more sustainable, mechanized and productive

Deep-learning assisted detection and quantification of (oo)cysts of Giardia and Cryptosporidium on smartphone microscopy images

The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of three state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, and you only look once (YOLOv8s) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts.Comment: 18 pages (including supplementary information), 4 figures, 7 tables, submitting to Journal of Machine Learning for Biomedical Imagin

Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries

Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches

A Study on Improving M2M Network Security through Abnormal Traffic Control

Machine-to-machine (M2M) intelligent network devices are exposed to vulnerable networks and security threats always exist. The devices are composed of low-capacity hardware by their nature and are exposed to various security threats such as worms, viruses and distributed denial of service (DDoS) flooding attacks due to lack of security or antivirus programs installed in the personal computer environment. In this paper, we proposed a network filter that improves the security of M2M intelligent networks by configuring the network security filter in a specific form that can be adapted to M2M intelligent networks. The proposed filter increases user convenience and decreases unnecessary loss. Experimental results show that when the security filter is applied, the response speed of the device improved by more than 50% in an abnormal traffic environment with a cost of less than 10% delay, depending upon the characteristics of the device

A Study on Improving M2M Network Security through Abnormal Traffic Control

Machine-to-machine (M2M) intelligent network devices are exposed to vulnerable networks and security threats always exist. The devices are composed of low-capacity hardware by their nature and are exposed to various security threats such as worms, viruses and distributed denial of service (DDoS) flooding attacks due to lack of security or antivirus programs installed in the personal computer environment. In this paper, we proposed a network filter that improves the security of M2M intelligent networks by configuring the network security filter in a specific form that can be adapted to M2M intelligent networks. The proposed filter increases user convenience and decreases unnecessary loss. Experimental results show that when the security filter is applied, the response speed of the device improved by more than 50% in an abnormal traffic environment with a cost of less than 10% delay, depending upon the characteristics of the device

Low phase noise microwave oscillator using meander spurline resonator for X-band application

381-384We present a simple compact meander spurline resonator and its application to low phase noise oscillator. The resonator which has a bandstop characteristic is used in the series feedback of oscillator. This miniaturized resonator consists of a defected meander spurline with inductive characteristics and capacitive characteristics. The microwave oscillator using the meander spurline resonator shows excellent phase noise performances of -103.23 dBc/Hz at a 100 kHz offset from the carrier frequency of 9.0 GHz with an output power of 15.23 dBm due to the high Q value of the defected meander spurline resonator. The structure of resonators reduces the size and the cost

Design of low phase noise InGaP/GaAs HBT-based differential Colpitts VCOs for interference cancellation system

34-38This paper presents design, fabrication and characterization of two differential Colpitts voltage controlled oscillator (VCOs) - cross-coupled VCO (CC-VCO) and double cross-coupled VCO (DC-VCO) - in InGaP/GaAs HBT technology. Their main parameters like the oscillation frequency, output power and phase noise performance are measured and compared with other recently published studies. In the cores of two VCOs, two switching transistors are introduced to steer the core bias current to save power. An LC tank with higher inductor quality factor (Q) is used to generate oscillation frequency. The differential CC-VCO exhibited a superior phase noise characteristics of-130.12 dBc/Hz at 1 MHz from the carrier frequency (1.566 GHz) when supplied with a control voltage of 0 volt. In the same way, the differential DC-VCO achieved -134.58 dBc/Hz at 1 MHz from the carrier frequency (1.630 GHz) with the same control voltage. Two pairs of on-chip base collector (BC) diodes are used in the tank circuit to increase the VCO tuning range. It is concluded that the faster switching action of InGaP/GaAs HBT transistors exhibited the excellent phase noise characteristics

Acid and Alkali Taste Sensation

Living organisms rely on pH levels for a multitude of crucial biological processes, such as the digestion of food and the facilitation of enzymatic reactions. Among these organisms, animals, including insects, possess specialized taste organs that enable them to discern between acidic and alkaline substances present in their food sources. This ability is vital, as the pH of these compounds directly influences both the nutritional value and the overall health impact of the ingested substances. In response to the various chemical properties of naturally occurring compounds, insects have evolved peripheral taste organs. These sensory structures play a pivotal role in identifying and distinguishing between nourishing and potentially harmful foods. In this concise review, we aim to provide an in-depth examination of the molecular mechanisms governing pH-dependent taste responses, encompassing both acidic and alkaline stimuli, within the peripheral taste organs of the fruit fly, Drosophila melanogaster, drawing insights from a comprehensive analysis of existing research articles

Avoiding alkaline taste through ionotropic receptors

Summary: Taste organs contain distinct gustatory receptors that help organisms differentiate between nourishing and potentially harmful foods. The detection of high pH levels plays a crucial role in food selection, but the specific gustatory receptors responsible for perceiving elevated pH in foods have remained unknown. By using Drosophila melanogaster as a model organism, we have uncovered the involvement of ionotropic receptors (IRs) in avoiding high-pH foods. Our study involved a combination of behavioral tests and electrophysiological analyses, which led to the identification of six Irs from bitter-sensing gustatory receptor neurons essential for rejecting food items with elevated pH levels. Using the same methodology, our study reevaluated the significance of Alka and OtopLa. The findings highlight that Alka, in conjunction with IRs, is crucial for detecting alkaline substances, whereas OtopLa does not contribute to this process. Overall, our study offers valuable insights into the intricate mechanisms governing taste perception in organisms