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A B S T R A C TNowadays, with huge progress in digital imaging, new image processing methods are needed to manage digital images stored on disks. Image retrieval has been one of the most challengeable fields in digital image processing which means searching in a big database in order to represent similar images to the query image. Although many efficient researches have been performed for this topic so far, there is a semantic gap between human concept and features extracted from the images and it has become an important problem which decreases retrieval precision. In this paper, a convolutional neural network (CNN) is used to extract deep and high-level features from the images. Next, an optimization problem is defined in order to model the retrieval system. Heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have shown an effective role in solving the complex problems. A recent introduced heuristic algorithm is Grasshopper Optimization Algorithm (GOA) which has been proved to be able to solve difficult optimization problems. So, a new search method, modified grasshopper optimization algorithm (MGOA) is proposed to solve modeled problem and to retrieve similar images efficiently, despite of total search in database. Experimental results showed that the proposed system named CNN-MGOA achieves superior accuracy compared to traditional methods.

Automatic waveform recognition has become an important task in radar systems and spread spectrum communications. Identifying the modulation of received signals helps to recognize different invader transmitters. In this paper, a noise aware model is proposed to recognize the modulation type based on time-frequency characteristics. To this end, Choi-Williams representation is used to obtain spatial 2D pattern of received signal. After that, a deep model is constructed to make signal clear from noise and extract robust and discriminative features from time-frequency pattern, based on auto-encoder and Convolutional Neural Networks (CNN). In order to reduce the effect of noise and adversarial disorders, a new database of different modulation patterns with different AWGN noises and fading Rayleigh channel is created which helps model to avoid the effects of noise on modulation recognition. Our database contains radar modulations such as Barker, LFM, Costas and Frank code which are known as frequently used modulations on wireless communication. Infact, the main novelty of this work is designing this database and proposing noise-aware model. Experimental results demonstrate that the proposed model achieves superior performance for automatic classification recognition with 99.24% of accuracy in noisy medium with minimum SNR of -5dB while the accuracy is 97.90% in SNR of -5dB and f=15 Hz of Doppler frequency. Our model outperforms 5.54% in negative and 0.4% in positive SNRs (even though with less SNR).

Detecting same people in different surveillance cameras, named person re-identification, has become a challenging and critical task in image processing. Since surveillance images usually have low resolution and different viewpoints, matching persons on them is still difficult. In this paper, a proposed method for person re-identification is introduced based on exploring similarity in different depth layers of convolutional neural network (CNN). To this end, after determining each person as a category for training CNN, optimum filters are obtained to find the best discriminative feature maps based on them. Smoothed discriminative features (SDF) are defined to compute similarity between persons. Experimental results, performed on CUHK01 database, demonstrate that the proposed method outperforms state-of-the-art feature extraction methods for person re-identification.

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Person re-identification (re-id) is one of the most critical and challenging topics in image processing and artificial intelligence. In general, person re-identification means that a person seen in the field of view of one camera can be found and tracked by other non-overlapped cameras. Low-resolution frames, high occlusion in crowded scene, and few samples for training supervised models make re-id challenging. This paper proposes a new model for person reidentification to overcome the noisy frames and extract robust features from each frame. To this end, a noise-aware system is implemented by training an auto-encoder on artificially damaged frames to overcome noise and occlusion. A model for person re-identification is implemented based on deep convolutional neural networks. Experimental results on two actual databases, CUHK01 and CUHK03, demonstrate that the proposed method performs better than state-ofthe-art methods.

The SARS-CoV-2 (COVID-19) pandemic outbreak has led to some lockdowns and changed human mobility and lifestyle in this country. Mashhad, one of the most polluted cities in Iran has experienced critical air pollution conditions in recent years. In the present study, the potential relationships between air quality conditions (such as popular index and criteria air pollutant concentration) and COVID-19 cases and deaths were investigated in Mashhad, Iran. To do that, the Long Short-Term Memory (LSTM) based hybrid deep learning architecture was implemented on AQI, meteorological data (such as temperature, sea level pressure, dew points, and wind speed), traffic index and impact number of death, and active cases COVID-19 from March 2019 to March 2022 in Mashhad. The results reveal the LSTM model could predict the AQI accurately. The lower error between the real and predicted AQI, including MSE, MSLE, and MAE is 0.0153, 0.0058, and 0.1043, respectively. Also, the cosine similarity between predicted AQI and real amounts of it is 1. Moreover, in the first peak of the pandemic (Aug 2021), we have the minimum amount of AQI. Meanwhile, by increasing the number of active cases and death and by starting lockdown, because the traffic is decreased, the air quality is good and the amount of AQI related to PM2.5 is 54.68. Furthermore, the decrease the active cases and death in pandemic causes a significant increase in AQI, which is 123.52 in Nov 2021, due to a decline in lockdowns, resumption of human activities, and probable temperature inversions.