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The AES combined S-box/inverse S-box is a single construction that is shared between the encryption and decryption data paths of the AES. The currently most compact implementation of the AES combined S-box/inverse S-box is Canright's design, introduced back in 2005. Since then, the research community has introduced several optimizations over the S-box only, however the combined S-boxlinverse S-box received little attention. In this paper, we propose a new AES combined S-boxlinverse S-box design that is both smaller and faster than Canright's design. We achieve this goal by proposing to use new tower field and optimizing each and every block inside the combined architecture for this field. Our complexity analysis and ASIC implementation results in the CMOS STM 65nm and NanGate 15nm technologies show that our design outperforms the counterparts in terms of area and speed.

The implementation of AES S-boxes is one of the most extensively studied areas of cryptography. In this paper, we propose three new hardware designs for the AES S-box that can serve in the forward, inverse and combined data paths. Each of these designs represents the smallest AES S-box ever proposed in its respective category. We achieve this goal by using new tower field representation over normal bases and optimizing each and every block inside the three proposed architectures. Our complexity analysis and ASIC synthesis results in the CMOS STM 65nm, as well as the NanGate 15nm technologies, show that our designs outperform their counterparts in terms of area and power.

This paper presents a proposed design procedure of a Wireless Power Transfer (WPT) system based on high efficiency offset reflector antennas fed by conical horns. The system's performance evaluation is also demonstrated. The antennas in the transmitter and receiver sides of the proposed WPT system are symmetric. The performance of the system is optimized by calibrating the feeding horns and the offset reflector's dimensions to minimize the path and reflection losses of the proposed WPT system. The results show that correct line of sight alignment of the transmitter and receiver enhances the efficiency of the system. With an operating frequency of 6 GHz and 1 W of power transfer over a distance of 12 m between the transmitter and receiver, the system attains a total transfer efficiency of 62.9 %.

Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimation is normally done by trial and error experimentation, until an acceptable restored image quality is obtained. This paper, presents an exact estimation of the PSF size that yields the optimum restored image quality. The paper also describes a least squares PSF estimation, instead of the slowly iterative update, that is commonly used in Iterative Blind Deconvolution software, IBD. Moreover, a technique is also proposed to improve the sharpness of the de-convolved images using Independent Component Analysis techniques (ICA). Simulation examples are given to show that the proposed technique manages to accurately estimate the PSF size apart from competing very well with the existing approaches.

Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper, we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first, a set of reliable keypoints are extracted from a video clip (i.e., action snippet). The local descriptors characterizing the temporal shape variations of action are then obtained by using the temporal self-similarities defined on the fuzzy log-polar histograms. Finally, the SVM classifier is trained on these features to realize the action recognition model. The proposed method is validated on two popular and publicly available action datasets. The results obtained are quite encouraging and show that an accuracy comparable or superior to that of the state-of-the-art is achievable. Furthermore, the method runs in real time and thus can offer timing guarantees to real-time …

Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks is given. Second, and maybe most importantly, an adaptive cubic-spline neural network is designed to be used to detect and locate human faces in uncontrolled environments. The experimental results conducted on our test set show the effectiveness of the proposed approach and it can compare favorably with other state-of-the-art approaches in the literature.

An essential part of any activity recognition system claiming be truly real-time is the ability to perform feature extraction in real-time. We present, in this paper, a quite simple and computationally tractable approach for real-time human activity recognition that is based on simple statistical features. These features are simple and relatively small, accordingly they are easy and fast to be calculated, and further form a relatively low-dimensional feature space in which classification can be carried out robustly. On the Weizmann publicly benchmark dataset, promising results (i.e. 97.8%) have been achieved, showing the effectiveness of the proposed approach compared to the-state-of-the-art. Furthermore, the approach is quite fast and thus can provide timing guarantees to real-time applications.

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Total variation (TV) regularization is popular in image restoration and reconstruction due to its ability to preserve image edges. This paper, describes a new total variation based de-noising scheme. The proposed technique optimally finds the threshold level of the noisy image wavelet decomposition that minimizes the energy of the error between the restored and the noisy image. The minimization algorithm is regularized by including 1st as well as 2nd order derivatives effects of the noisy image, into the minimization scheme. Next, the problem of blind deconvolution of noisy images is addressed. First, the order of the blurring Point Spread Function (PSF), is accurately estimated using a de-noised version of the noisy blurred image. Then, the deconvolution algorithm is modified by including the effects of the 1 st as well as 2nd order derivatives of the blurred noisy images into the image update algorithm. Simulation …