Garbage waste monitoring, collection and management is one of the primary concerns of the present era due to its detrimental effects on environment. The traditional way of manually monitoring and collecting the garbage is a cumbersome process as it requires considerable human effort and time leading to higher cost. In this paper, an IoT based garbage monitoring system using Thingspeak, an open IoT platform is presented. The system consists of an Arduino microcontroller, an ultrasonic sensor, a load cell and a Wi-Fi module. The Arduino microcontroller receives data from the ultrasonic sensor and load cell. The depth of the garbage in the bin is measured using ultrasonic sensor and the weight of the bin with garbage is measured from the load cell. The LCD screen is used to display the data. The Wi-Fi module transmits the above data to the internet. An open IoT platform Thingspeak is used to monitor the garbage system. With this system, the administrator can monitor and schedule garbage collection more efficiently. A prototype has been developed and tested. It has been found to work satisfactorily. The details are presented in this paper.

Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, the number of the samples is significantly smaller in comparison to the number of genes. Therefore, this paper proposed the use of a simulated Kalman filter with mutation (SKF-MUT) for the feature selection of microarray data to enhance the classification accuracy of ANN. The algorithm is based on a metaheuristics optimization algorithm, inspired by the famous Kalman filter estimator. The mutation operator is proposed to enhance the performance of the original SKF in the selection of microarray features. Eight different benchmark datasets were used, which comprised: diffuse large b-cell lymphomas (DLBCL); prostate cancer; lung cancer; leukemia cancer; “small, round blue cell tumor” (SRBCT); brain tumor; nine types of human tumors; and 11 types of human tumors. These consist of both binary and multiclass datasets. The accuracy is taken as the performance measurement by considering the confusion matrix. Based on the results, SKF-MUT effectively selected the number of features needed, leading toward a higher classification accuracy ranging from 95% to 100%.

Analysis of non-stationary and nonlinear sound signals obtained from dynamical processes is one of the greatest challenges in signal processing. Turning machine operation is a highly dynamic process influenced by many events, such as dynamical responses, chip formations and the operational conditions of machining. Traditional and widely used fast Fourier transformation and spectrogram are not suitable for processing sound signals acquired from dynamical systems as their results have significant deficiencies because of stationary assumptions and having an a priori basis. A relatively new technique, discrete wavelet transform (DWT), which uses Wavelet decomposition (WD), and the recently developed technique, Hilbert–Huang Transform (HHT), which uses empirical mode decomposition (EMD), have notably better properties in the analysis of nonlinear and non-stationary sound signals. The EMD process helps the HHT to locate the signal’s instantaneous frequencies by forming symmetrical envelopes on the signal. The objective of this paper is to present a comparative study on the decomposition of multi-component sound signals using EMD and WD to highlight the suitability of HHT to analyze tool-emitted sound signals received from turning processes. The methodology used to achieve the objective is recording a tool-emitted sound signal by way of conducting an experiment on a turning machine and comparing the results of decomposing the signal by WD and EMD techniques. Apart from the short mathematical and theoretical foundations of the transformations, this paper demonstrates their decomposition strength using an experimental case study of tool flank wear monitoring in turning. It also concludes HHT is more suitable than DWT to analyze tool-emitted sound signals received from turning processes.

The focus of this paper is the design and development of an automatic solar grass trimmer prototype, emphasizing high operational efficiency with renewable energy as the primary power source. This solar-based device not only contributes to reduced air pollution but also aligns with environmental sustainability objectives. The prototype features a charge control circuit and is powered by a rechargeable 12V battery. To address low battery levels, a solar panel is employed for automatic recharging. A DC-DC buck converter is integrated to step down the higher voltage from the solar panel to match the battery requirements. Safety features, such as a current limiter and overcharge protection circuits, have been incorporated into the design. The paper provides a detailed discussion on the conceptual design of the solar grass trimmer, including the placement of sensors and motors. Additionally, the cutting motor force analysis and total weight calculation of the prototype are presented.The solar charge control circuit, along with details on the current limiter and overcharge protection circuit, is thoroughly explored. Successful development of the prototype is reported, and the battery charging circuits are analyzed using a Bench PSU and solar panel. The paper includes plots of voltage, current, and power generated by the solar panel under various weather conditions. A comparison between Bench PSU and solar panel power is also provided. This research contributes to the advancement of automated solar-powered devices, offering sustainable solutions for environmentally friendly grass trimming.

This paper focuses on the design of an Arduino-based Automated Solar Grass Trimmer with a primary emphasis on achieving high operational efficiency. A solar panel is utilized to automatically charge the battery when its level is low. A voltage level indicator circuit is incorporated to assess various battery voltage levels. The prototype integrates ultrasonic sensors for obstacle detection and inductive sensors for boundary detection. To ensure safe operation in the field, a perimeter signal generator circuit is constructed for boundary detection using inductive sensors. Ultrasonic sensors are employed for obstacle detection. The paper introduces an algorithm for detecting obstacles, boundaries, and other impediments in the path of the solar grass trimmer, illustrated through a comprehensive flowchart. Detailed discussions on the voltage level indicator circuit and boundary detection circuits are provided. The implemented algorithm, utilizing an Arduino microcontroller, is tested in the field, and the results are explained in the paper. Tabulated data from the prototype testing, specifically focusing on boundary detection and obstacle detection, demonstrate satisfactory performance.

The primary objective of this home automation study is to facilitate the implementation of a voice-controlled capabilities, primarily designed to assist individuals with disabilities or seniors. The system presented in this research enables wireless control of various household devices, such as lights, fans, and any electrical appliances through a dedicated mobile application. This voice-controlled home automation system leverages an android smartphone and a microcontroller using an android application to manage household appliances. The system can be accessed by user name and password. This system is designed with three main control interfaces such as Bluetooth connectivity, voice recognition, and manual control switching. The google cloud speech API which can convert spoken words into text is utilized for voice recognition. This generated text is then transmitted to the designated slave device, facilitating home automation through Bluetooth communication. Additionally, the system provides a manual control switch for user convenience. To control high-voltage appliances safely, an enhanced microcontroller is employed, incorporating a relay circuit for ON/OFF functionality. The developed prototype, encompassing both hardware and software components, has undergone comprehensive testing, validating its security features and compatibility with various home appliances. This innovative home automation system not only offers enhanced convenience but also prioritizes security, providing an attractive alternative to commercial solutions.