Multiple-license plate recognition is gaining popularity in the Intelligent Transport System (ITS) applications for security monitoring and surveillance. Advancements in acquisition devices have increased the availability of high definition (HD) images, which can capture images of multiple vehicles. Since license plate (LP) occupies a relatively small portion of an image, therefore, detection of LP in an image is considered a challenging task. Moreover, the overall performance deteriorates when the aforementioned factor combines with varying illumination conditions, such as night, dusk, and rainy. As it is difficult to locate a small object in an entire image, this paper proposes a two-step approach for plate localization in challenging conditions. In the first step, the Faster-Region-based Convolutional Neural Network algorithm (Faster R-CNN) is used to detect all the vehicles in an image, which results in scaled information to locate plates. In the second step, morphological operations are employed to reduce non-plate regions. Meanwhile, geometric properties are used to localize plates in the HSI color space. This approach increases accuracy and reduces processing time. For character recognition, the look-up table (LUT) classifier using adaptive boosting with modified census transform (MCT) as a feature extractor is used. Both proposed plate detection and character recognition methods have significantly outperformed conventional approaches in terms of precision and recall for multiple plate recognition.

Foreign object debris (FOD) is any undesired and unintended object placed or found in the specific vicinity of an aircraft (runway/ taxiway) that can cause damage to aircraft or harm personnel on board such as twisted metal strips, screws, nuts, and bolts, depleted concrete runway pieces, stones, pebbles and stationery items. To avoid FOD damages, all airport/ aviation organizations have deployed some sort of FOD prevention procedure. However, automatic FOD detection systems are still scarce owing to the inevitable reliance on human experts that lead to unavoidable human errors. Around 60% of FOD consists of metal which is the most deteriorating for an aircraft. Therefore, the implementation of material recognition techniques for FOD classification through Deep Convolutional Neural Networks (DCNN) is more important than FOD object detection as FOD could be of any shape, size or color. This paper developed a DCNN algorithm for FOD material classification with high accuracy for all included material classes (i.e., metal, concrete, plastic) in general and metal in particular. For this, a new dataset is introduced that consists of 2481 images taken on an operational airport runway in varying illumination and weather conditions. Through extensive testing, it was found that InceptionV3 is the best performing model with 18% improvement in metal recognition, and 11% improvement in average accuracy for all included classes.

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In this paper, we propose a novel empirical power allocation algorithm based on individual term normalization of N th order Fibonacci polynomial for multi user Non Orthogonal Multiple Access (NOMA), providing a characteristic solution to allocation problem for n-users. A deterministic mathematical expression for same predetermined power allocation scheme for both superposition coding (SC) at base station domain and successive interference cancellation (SIC) at user domain has been formulated. Fibonacci polynomial has been defined and linked to power allocation algorithm corresponding to radial distance of users from BS with sole requirement of second order statistics (SOS). Holistic analytical reasoning has been carried out for generic NOMA system and an exact closed-form expression for bit error rate (BER) has been derived using probabilistic models, which has been applied to proposed allocation algorithm. Detailed analysis validates that proposed power allocation algorithm is independent of user channel state information (CSI) which averts cumbersome computations and no prior information regarding allocation is required to be relayed to user domain for successful SIC. Numerical and simulation results (upto 7 user NOMA) have been provided to demonstrate superior performance in terms of reliability and latency of the proposed algorithm in comparison to channel inversion, a CSI based algorithm.