The rapidly growing use and storage of private, sensitive, and personal information across different applications have given rise to the need to restrict access to such information; thus, leading to the development of biometric authentication. Multimodal biometric authentication has improved system accuracy, but it has not been able to overcome all the vulnerabilities of biometric authentication. To reduce the amount of data that is stored or communicated during the authentication process and to ensure the authenticity of the biometric templates, image-watermarking techniques have been used to embed the information in one template over the other. In this paper, a new watermarking method is proposed, based on the Discrete Cosine Transform (DCT) method and the Least Significant Bit (LSB) method. The LSBs of the quantized DCT coefficients of a face image are manipulated according to the values of a binarized fingerprint image. This combination was used to allow the storing and communication of the watermarked images using the popular JPEG format. As the watermark information is not hand-crafted, tamper detection could not be achieved by comparing a static image to the extracted watermark. Thus, a Machine-Learning (ML)-based method was implemented to detect the existence of fingerprint patterns in the watermark. However, as the proposed system used a Convolutional Neural Network (CNN) to measure the similarity between the templates collected from the user and those stored in the model database, tamper detection was already embedded in the same neural network. Experiments were conducted to evaluate the performance of the proposed system. The results show a 98.96% average accuracy, where each prediction took an average processing time of 139.06 ms. The results also showed that the accuracy of tampering detection was 100%. Besides, the size of the files on the disk (or the bandwidth required to communicate the files) was reduced to less than 50% of their original size using the proposed fragile multibiometric watermarking technique.

Medical images modalities are extensively adapted and used for disease diagnosis. These imaging modalities include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and X-ray… etc. The problem associated with medical images is the storage space and bandwidth required for archiving and transmission of this kind of images. Thus image compression is a key factor to reduce the bit rate for transmission or storage while maintaining an acceptable reproduction quality In this paper, an efficient ROI compression approach using hybrid lossy lossless compression technique will be presented. This method is based on applying discreet wavelet transform (DWT) as a lossy method and grayscale matrix (GSM) coding algorithm as lossless method. The results show that the performance of the proposed method is much better than the other existing techniques especially with smaller regions of interest.

Dust storms are one of the natural hazards whose incidence has increased in the recent years over Sahara desert, Australia and northern China. Thus, it is important to know the causation, movement and radiation effects of dust storms. Satellite remote sensing is the most common method for monitoring Dust Storms but its use over sandy ground is still limited as they have similar characteristics. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data. However, there have been fewer studies for detecting dust storms at night. The key elements of this study are to use a back-propagation artificial neural network with Brightness Temperature of band 31 and four Brightness Temperature Differences calculated using data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop a method for detecting dust storms during both day and night. Results have shown that the method can detect dust storms at both day and night and also over different land surfaces. 

Dust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave …

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.

One of the problems worsened by climate change is the occurrence of Sand Dust Storms (SDS), which are dry winds carrying sand. In recent years the numbers of SDSs have increased in North Africa, Australia and northern China. Satellite remote sensing (SR) is the main method for monitoring SDS as they happen. However its use for identifying sand dust storms over sandy ground such as Saharan desert is still limited as both materials share similar characteristics. In this paper, we propose a new approach to distinguish dust storms cloud from sandy ground and water cloud using Moderate Resolution Imaging Spectroradiometer (MODIS) bands.