Since the revolution in solid state electronics, many innovative principles were investigated for a better and simpler design. Thus, Hall effect-based sensors and instruments gained importance. To employ this principle in several operating conditions and with different setups, several researchers contributed significantly over the decades, which ultimately led to the establishment of industries producing a wide range of Hall devices. The objective of this paper is to review the available configurations and current status of the Hall effect-based technologies. A detailed discussion is carried out on the various types of existing Hall-based devices, such as linear sensors, field-programmable sensors, switches, latches, speed and directional sensors, and vane sensors. The effect of materials and the influence of several undesired effects (such as offset voltage, temperature, noise, and drift) are also investigated. The compensation/reduction techniques are mentioned therein, and interested researchers are encouraged for the development of new techniques. This paper concludes with the discussion on the market scenario (such as electronics sector and automotive industry) and progression in current research on Hall devices while projecting some new research directions in this field.

In this paper, economic load dispatch problem is discussed and implemented with firefly algorithm optimization technique to obtain the best optimal solution for the fuel cost of generator. Moreover, we have included the renewable sources in ELD problem, by estimating generation of solar and wind power through probability density function including the underestimation cost and overestimation cost of wind and solar units. In this problem it is been assumed that the renewable source units are located near the load center; hence we are neglecting the transmission losses by renewable sources and considering them only for the coal fired units (thermal units). The problem is then executed on four test cases with and without inclusion of renewable sources along with different combinations of renewable sources and validated them with experimental approach

Increased quantities of the same sort of item are not nearly as critical to client happiness as a high-quality product. The requirements and expectations of the consumer have an impact on the overall quality of a product or service. The term “quality” may also be defined as the sum total of all the features that contribute to the production of goods and services that are satisfactory to the consumer. Certain imported commodities have lately seen an improvement in quality thanks to efforts by importing nations. Additionally, it safeguards food imported from other nations by confirming that it is safe for human consumption before it is released. This article describes a technique for monitoring perishable goods that is based on the Internet of Things and machine learning. Pictures are recorded using high-resolution cameras in this suggested architecture, and then these images are sent to a cloud server using Internet of Things devices. When uploaded to a cloud server, these photos are segmented using the K-means clustering method. Then, using the principal component analysis technique, features are extracted from the photos, and the images are categorized using machine learning models that have been trained. This proposed model makes use of the Internet of Things, image processing, and machine learning to monitor perishable food.

Deep learning has recently been hailed as the most advanced computer vision technology for image classification. The invention of convolutional neural network (CNN) simplified the effort of feature engineering. Classification of various stages of fruit maturity using machine learning algorithms is a difficult task since it is difficult to distinguish the visual features of the fruits at different maturity stages. Fruit ripeness is critical in agriculture since it impacts the quality of the fruit. Manually determining the maturity of the fruit has various flaws, including the fact that it takes a long time, needs a lot of labor, and can lead to inconsistencies. In developing countries, agriculture is one of the most important economic sectors. Created system can be employed in the food processing business, in real-life applications where the intelligent system’s accuracy, cost, and speed will improve the production rate and allow satisfying consumer demand. With small number of image samples, the system is capable of automating assembly line related work for classifying bananas along with sufficient overall accuracy. The noninvasive method will also be used to classify other clustered fruits or horticultural crops in the future. The system can either replace or aid human operators who can focus their efforts on fruit selection. The combined merits of RGB and HSI (hyperspectral imaging) for classification of bananas were highlighted in the present study; they have possible application as a model for classification of several types of horticultural produce. The multi-input model’s quick processing time can be a useful and handy technique in the farm field during postharvest procedures. Via a combination of CNN and MLP applied to data collected using RGB and hyperspectral imaging, the multi-input model reliably recognizes bananas with an accuracy level of 98.4 percent as well as an F1-score of 0.97. The AI algorithm predicted the size (large, medium, and microscopic) and perspective (front or rear half) of banana classes with 99 percent accuracy. In comparison to previous studies that simply employed RGB imaging, the presented model revealed the value of integrating RGB imaging and HSI approaches.