Facial expression conveys important signs about the human affective state, cognitive activity, intention and personality. In fact, the automatic facial expression recognition systems are getting more interest year after year due to its wide range of applications in several interesting fields such as human computer/robot interaction, medical applications, animation and video gaming. In this study, the authors propose to combine between different descriptors features (histogram of oriented gradients, local phase quantisation and binarised statistical image features) after applying principal component analysis on each of them to recognise the six basic expressions and the neutral face from the static images. Their proposed fusion method has been tested on four popular databases which are: JAFFE, MMI, CASIA and CK+, using two different cross‐validation schemes: subject independent and leave‐one–subject‐out. The obtained results show that their method outperforms both the raw features concatenation and state‐of‐the‐art methods.

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre‐processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.

The authors consider a number of topics concerned with coding schemes over fading dispersive channels. They begin with comparing the performance of the energy receiver with the optimum and envelope receivers. Different coding schemes are used to improve the performance. The effect of channel spread on the performance of single-state Reed-Solomon (RS) codes is investigated. Channel information (CI) is discussed as a means for further improvement. The simple erasure criterion used is suboptimal, but gives a measure of the gain that can be achieved by using CI. Of special interest is the use of a concatenated coding technique for forming codes of large distance. Concatenated codes using short RS codes with CI as inner codes and RS outer codes are compared. It has been shown that the improvement obtained by concatenation is significant for overspread channels. This is attributed to the implicit diversity introduced by the channel. Furthermore, a concatenated system using convolutional codes as inner codes performs better than the system using RS inner codes.>