Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver. Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected. Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88. 00%, while the highest accuracy value for drowsiness driver detection was 90. 40%. Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase per-formance.

In many practical applications, implementation of algorithms is required into low-cost and low-power hardware, proper processing power, simplicity in algorithm development and maximum flexibility. Proper implementation of these methods and real-time operations for defense systems has particular importance. Studies have shown that Raspberry Pi 2 has sufficient computational power to implement an infrared target detection algorithm. Therefore, in this paper, Raspberry Pi 2 is considered as low-cost, low-weight, and low-power hardware for optimum implementing infrared target detection methods and to optimize and reduce runtime, it with the overclocking technique is used. Finally, their performance is compared with other hardware with different software development environment. These comparisons include the Qt software development environment based on the OpenCV image processing library in the Raspberry Pi 2 hardware with Qt software development environment based on the OpenCV library functions in the PC hardware, as well as the high-level MATLAB software. The results show that implementation on the Raspberry Pi 2 in comparison with MATLAB speeds up implementation of the algorithm 6. 5 times. As well as, implementation time of the infrared target detection algorithm (C ++) using the OpenCV library on the PC is approximately eight times that of Raspberry Pi 2. Also, comparing Raspberry Pi 2 and PC in terms of power consumption, weight and cost is observed that Raspberry Pi 2 has a much better performance in terms of power consumption, weight and cost than PCs. The results show that although the use of high-level software such as MATLAB has background suppression factor (SCR) and signal to clutter ratio (BSF) higher than use of the OpenCV library, the results of runtime indicate that the proposed hardware improves the runtime of high-level software like MATLAB. The results of optimization on the Raspberry Pi 2 show that speed of the algorithm is improved by more than 40%.

Industrial electrical panels require a regular inspection as maintenance. The project applied a single board computer to the IoT system, making the electrical panel smart, which makes the maintenance procedure much easier and faster. The electronic design used a Raspberry Pi single board computer as the central system controller, and an electronic circuit for data communication was designed. The Python programming language was used for software design. Dataplicity Cloud Commander on phones and computers was used as an interface for accessing the electrical panel online via the Raspberry Pi. The design allows technicians to continuously monitor devices such as protective relays, panel temperatures, and DC power supply voltages online via mobile phones or computers. The system was tested for various operating conditions and possible errors of the power panel. The result is a low-cost smart system for electrical panels that provides essential information quickly and easily, greatly reducing panel troubleshooting time.

The identification and diagnosis of plant diseases have long been considered. This research presents a system for diagnosing the volume and type of apple diseases and the spoilage percentage of rotten apples. To estimate the volume of apples, the method of immersion in water to change the volume of the container was used, ensuring more accurate volume estimation. For disease detection and spoilage analysis, a chamber with constant lighting conditions and a halogen lamp was used. Four images were taken with a camera for better analysis. The volume of apples was calculated through two approximations of the cylinder and incomplete cone. The average error rate in this system was 5%. Also, in the present research, a novel method for feature selection was identified using a combination of the weight feature and the calculated volume of hollow apples. To calculate the percentage of failure of each apple, first, the type of failure was identified. Then, the ratio of loss of each apple relative to the whole apple was calculated and compared with the number obtained from the desired region method, which was accurate. In this study, three major diseases of apples were studied, and an algorithm was written to distinguish these three types of infections from healthy apples. The results showed that the proposed method had the necessary efficiency to calculate the volume and percentage of failure and diagnose the type of apple diseases. In addition, the system's accuracy compared to previous studies increased by up to 95%.

In this article, a smart visual acuity measurement (VAM) system is designed and implemented. Hardware of the proposed VAM system consists of two parts: a wireless remote controller, and a high-resolution LCD controlled through a Raspberry-Pi mini-computer. In the remote controller, a 3. 5” graphical LCD with a touch screen is used as a human-machine interface. When a point is pressed on the touch screen, the unique identifier (ID) code of that point as well as its page number is transmitted to the Raspberry-Pi. In the Raspberry-Pi, data are received and processed by a smart application coded in visual studio software. Then, the commanded tasks are executed by the Raspberry-Pi’s operating system. Numerous charts, characters, and pictures are stored in the proposed VAM system to provide various VAM options while the size of the optotypes is adjusted automatically based on the distance of the patient from the LCD. The performance of the proposed VAM system is examined practically under the supervision of an expert optometrist where the results indicate that visual acuity, astigmatism, and color blindness of patients can be examined precisely through the proposed VAM system in an easier and more comfortable manner.

Rice cultivar Tarom Dilamani becauded a fragrance, flavor, cooking and marketing is a qualitative rice in Iran. This cultivar have high susceptibility against blast disease (Magnaporthe grisea). One of the important trouble producers of the Dilamani's rice cultivar is chemical control against blast disease and cause poisonous pollution of natural environment. The best manner in order to control this disease and avoid its damage is preparation and cultivation resistant cultivar. In this research we used classical breeding method to developing resistance cultivar. Female parent was qualitative Tarom Dilamani cultivar and male parent was near isogenic lines C101LAC and C101A51 that carry dominant resistant genes to blast Pi-1 and Pi-2, respectivly. Parents' seeds prepared, cultivated and propagated. When rice plant entered flowering stage, sensitive cultivar was confluenced with resistant lines' pollen. Hybrid seeds would cultivated next year. Plants cause selected according to morphological characters and virulences tests. Back cross was accomplished until four years until Bc4 generation. Plant inoculation was accomplished with dominant race of fungus cause blast at Agricultural Research Station of Sari Agricultural sciences and Natural Research University. Results of this research was transferred two resistance genes in sensitive Tarom Dilamani cultivar. The recovered cultivar showed resistance to races of fungus cause blast on region. Infection type of sensitivity (4 and 5) that was prior of accomplished of this research have converted to infection type of resistance (1 and 2). Morphology and quality of plant changed slightly, but was satisfactory prevention of damage due blast and increase grain yield.

In this paper, at first we mention to some results related to PI and vertex Co-PI indices and then we introduce the edge versions of Co-PI indices. Then, we obtain some properties about these new indices.

The PI index of a graph is given by , where is the number of equidistant vertices for the edge . Various topological indices of bicyclic graphs have already been calculated. In this paper, we obtained the exact value of the PI index of bicyclic graphs. We also explore the extremal graphs among all bicyclic graphs with respect to the PI index. Furthermore, we calculate the PI index of a cactus graph and determine the extremal values of the PI index among cactus graphs.

The vertex PI index PI (G) = SxyÎE (G) [nxy (x) + nxy (y)] is a distance-based molecular structure descriptor, where nxy (x) denotes the number of vertices which are closer to the vertex x than to the vertex y and which has been the considerable research in computational chemistry dating back to Harold Wiener in 1947. A connected graph is a cactus if any two of its cycles have at most one common vertex. In this paper, we completely determine the extremal graphs with the greatest and smallest vertex PI indices mong all cacti with a fixed number of vertices. As a consequence, we obtain the sharp bounds with corresponding extremal cacti and extend a known result.