Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets.

Subjective evaluation of tone-mapped images is tedious and time-consuming; therefore, it is desirable to have algorithms for automatic quality assessment. Many full-reference and blind metrics have been developed for this purpose, but their performance is generally evaluated on limited benchmark datasets. This leaves a possibility that the observed performance of the metric could be due to overfitting, and it might indeed not perform well for all scenes. In this work, we propose a novel framework using population-based metaheuristics to evaluate the performance of these metrics without requiring any subjectively evaluated reference dataset. The proposed algorithm does not modify the individual image pixels, instead, the tone-mapping curve is modified to synthesize realistic tone-mapped images for evaluation. Moreover, it is not required to know the underlying model of the evaluated metric, which is treated just like a black box and can be replaced by any other metric seamlessly. Therefore, any new metrics designed in the future can also be easily evaluated by simply replacing just one module in the proposed evaluation framework. We evaluate six existing metrics and synthesize images to which the metrics fail to assign appropriate scores for visual quality. We also propose a method to rank the relative performance of evaluated metrics, through a competition in which each metric tries to find the errors in the scores given by other metrics.

The unavailability of safe drinking water leads to poor conditions related to mental and physical health. To quantify the quality of water, laboratories testing the water are present in major cities which assess the basic quality parameters of drinking water, e.g., total dissolved salts (TDS), ion concentration (conductivity), turbidity, and pH value as recommended by the World Health Organization (WHO). The unavailability of such testing laboratories at remote locations makes the testing of the drinking water difficult. Establishing such laboratories is a tedious job as it requires a lot of costly equipment and specially trained personnel to operate them, making them difficult to handle. To address these issues, a water quality monitoring system for remote areas was designed which is capable of measuring basic measurable qualities of salt concentration, ion concentration, turbidity, and pH value. With the utilization of such a system, the user can qualify the water present in the vicinity as safe or unsafe for drinking purposes. The results from the proposed system are evaluated based on standard testing results and it is found that our water quality monitoring system is in agreement with the standard lab results with an average error of 2.9%, 1.4%, 1.2%, and 1.2% for pH, turbidity, conductivity, and TDS, respectively.

In this research, we investigate a resistor capacitor electric circuit that exhibits an exponentially decaying transient response. Due to uncertainty in the precise capacitance value, we treat the capacitance as a continuous uniformly distributed random variable. Using this approach, we derive the desired transient current response of the circuit as a function of the capacitance. Subsequently, we develop a probability model for the response current, expressed in terms of probability density function and cumulative distribution function. The model’s validity and correctness are verified, and it is further utilized for probabilistic analysis of the transient current. We demonstrate the application of the model for determining the probability of the transient current response reaching a specific value. By following the same procedure used to derive the probability model of the transient current, probability distributions for other circuit parameters, such as voltages and currents, can also be obtained. Furthermore, for parameters that are functions of the transient current, the probability model can also be obtained from the already derived probability model. To illustrate this, we derive the probability models of three other parameters in the circuit from the already obtained models. We also present examples to demonstrate the usage of the developed probability models.

Though the market of HDR displays for consumers is gaining maturity, low dynamic range (LDR) displays still hold a majority share in the consumer market. Many tone-mapping operators (TMO) have been developed to convert the HDR contents into a form suitable for LDR displays. This work proposes a generic tone-mapping operator (GiTMO) that can represent all global TMOs in a generic form of a one-dimensional array of a few coefficients. We use these coefficients and the existing HDR10 delivery pipeline to produce a tone-mapped version of the HDR content at the receiver end in real time. In our framework, coefficients are sent to the receiver as supplemental enhancement information (SEI) message of High Efficiency Video Coding (HEVC). Thus, HDR10, which is designed for HDR display, becomes compatible with LDR displays too. This can relieve the content producers from the need to release separate copies of HDR and LDR content. GiTMO, being generic and efficient, can generate LDR faithfully for all global TMOs, making the consumer end oblivious of the TMO used at the transmitter side. We perform extensive evaluations to validate the accuracy and efficiency of the GiTMO and the proposed content delivery format, and their benefits over the existing approaches.