Cereal grains and nuts are the world’s most produced food and the economic backbone of many countries. Food safety in these commodities is crucial, as they are highly susceptible to mold growth and mycotoxin contamination in warm, humid environments. This review explores hyperspectral imaging (HSI) integrated with machine learning (ML) algorithms as a promising approach for detecting and quantifying mycotoxins in cereal grains and nuts. This study aims to (1) critically evaluate current non-destructive techniques for processing these foods and the applications of ML in identifying mycotoxins through HSI, and (2) highlight challenges and potential future research directions to enhance the reliability and efficiency of these detection systems. The ML algorithms showed effectiveness in classifying and quantifying mycotoxins in grains and nuts, with HSI systems increasingly adopted in industrial settings. Mycotoxins exhibit heightened sensitivity to specific spectral bands within HSI, facilitating accurate detection. Additionally, selecting only relevant spectral features reduces ML model complexity and enhances reliability in the detection process. This review contributes to a deeper understanding of the integration of HSI and ML for food safety applications in cereal grains and nuts. By identifying current challenges and future research directions, it provides valuable insights for advancing non-destructive mycotoxin detection methods in the food industry using HSI.

Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an “edge-based transformation and entropy coding” (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed “prediction-based transformation and entropy coding” (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: “joint photographic experts group lossless” (JPEG-LS), “set partitioning in hierarchical trees” (SPIHT) and “differential pulse code modulation” (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.

Background: In its early development, radar (radio detection and ranging) was primarily used by the navy, the military, and the aviation services, as well as space organizations for security and monitoring purposes. Nowadays, the demand of radar is expanding. Research has been conducted to overcome the limitations of radar. Objective: One of the current limitations to detect moving object. The current paper aims to fill the gap in the literature by using a radar system in the identification of moving object, capturing the distance, direction, radar pulse duration and object shape simultaneously. Velocity or the object’s speed towards or away from the radar was determined by using an algorithm to obtain the precision. Methods: The accuracy of distance measurement and angle is ensured by comparing the real values and the values obtained by the radar. The objects under study consist of metal and non-metal. Novelty of this work is the accurate detection of moving objects with suitable algorithms using only one Arduino UNO and one ultrasonic sensor. Results: The experiment design yielded much better efficiency than previous works. The proposed method predicted the exact speed of the object detected by the radar system. The experiment has successfully proven the accuracy of moving object sensor. Conclusion: Besides proper distance and velocity, a large set of data was taken to find the accuracy of the radar for objects of different shapes. For a cylindrical object, the radar provided 100% efficiency in a constant environment when the object was 5 cm away. The accuracy decreased to 30% when the distance was 17 cm away. The limitation of this system is that it was unable to detect small object or if the object was very close (1 cm).