An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two rigorous and precise methods for evaluating the performance of face detection algorithms. We report results of several standard algorithms on the new benchmark.

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FDDB: A benchmark for face detection in unconstrained settings

Proceedings of world academy of science, engineering and technology, vol 8

Currently, the radio spectrum is statically allocated and divided between licensed and unlicensed frequencies. Due to this inflexible policy, some frequency bands are growing in scarcity, while large portions of the entire radio spectrum remain unused independently of time and location. Cognitive Radio is a recent network paradigm that aims a more flexible and efficient usage of the radio spectrum. Basically, it allows wireless devices to opportunistically access portions of the entire radio spectrum without causing any harmful interference to licensed users. The present document surveys the literature on Cognitive Radio. It aims to provide a comprehensive and self-contained description of this research topic area, mainly focusing on communication protocols, spectrum decision issues, and future research directions. It is a tutorial in nature and consequently does not require any previous knowledge about Cognitive Radio. Readers are only required to have some general background on wireless data networks. Emphasis is put on Cognitive Radio genesis, issues that must be addressed, related technologies, standardization efforts, the state of the art, and future research directions according to the vision of the authors.

Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions

A cross-layer protocol of spectrum mobility and handover in cognitive LTE networks

This paper presents a new technique for detection of human faces within color images. The approach relies on image segmentation based on skin color, features extracted from the two- dimensional discrete cosine transform (DCT), and self-organizing maps (SOM). After candidate skin regions are extracted, feature vectors are constructed using DCT coefficients computed from those regions. A supervised SOM training session is used to cluster feature vectors into groups, and to assign "face" or "non-face" labels to those clusters. Evaluation was performed using a new image database of 286 images, containing 1027 faces. After training, our detection technique achieved a detection rate of 77.94% during subsequent tests, with a false positive rate of 5.14%. To our knowledge, the proposed technique is the first to combine DCT-based feature extraction with a SOM for detecting human faces within color images. It is also one of a few attempts to combine a feature-invariant approach, such as color-based skin segmentation, together with appearance-based face detection. The main advantage of the new technique is its low computational requirements, in terms of both processing speed and memory utilization.

A New Face Detection Technique using 2D DCT and Self Organizing Feature Map

This paper presents a new color face image database for benchmarking of automatic face detection algorithms and human skin segmentation techniques. It is named the VT-AAST image database, and is divided into four parts. Part one is a set of 286 color photographs that include a total of 1027 faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race. Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. In this paper, descriptions of the database development, organization, format as well as information needed for benchmarking of algorithms are depicted in detail. Keywords—Image database, color image analysis, face detection, skin segmentation.

A New Color Image Database for Benchmarking of Automatic Face Detection and Human Skin Segmentation Techniques

In this paper, we investigate stochastic multi-channel load balancing in a distributed cognitive network coexisting with primary users. In particular, we propose a probabilistic technique for traffic distribution among a set of data channels by incorporating statistical information of primary users' activities in different channels into the selection process without centralized control. Moreover, the proposed scheme is enabled by a multi-channel binary exponential backoff mechanism to further facilitate contention resolution in a multi-channel environment. It is shown through simulations that the proposed MAC layer enhancement outperforms well-known multi-channel MAC protocols both in terms of aggregate end-to-end throughput and average frame end-to-end delay. Furthermore, its performance is also compared to two heuristic channel selection techniques in a multi-channel cognitive network, coexisting with incumbents.

A Channel Selection Mechanism based on Incumbent Appearance Expectation for Cognitive Networks

Simulation is still the most widely adopted performance evaluation technique in mobile ad hoc network research, in spite of a growing number of questions about the fidelity of this technique. Implementation-based testing and evaluation of wireless networks tends to produce believable results, but the technique sometimes suffers from poor repeatability, high implementation cost, and complex experimental logistics. The topic of this paper is software tools to enable implementation-based experimental research on wireless networks. We review some of the existing tools and propose the Flexible Internetwork Stack (FINS) framework, our open-source solution for network protocol implementation, integration, and testing. FINS aims to provide researchers with monitoring, logging, and reconfiguring utilities similar to the ones provided by simulation environments or emulation testbeds.1

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On software tools and stack architectures for wireless network experiments

The volume of video streaming traffic over the Internet has increased dramatically over the last 10 years. By the end of 2014, it is expected that 77% of the US internet traffic will be video streaming [1]. In parallel, the last ten years have seen an incredible deployment of IEEE 802.11 technologies, also known as WiFi, at homes and public places. Although, the industry and research communities have given a lot attention to address the challenges of video streaming over wireless access networks (e.g., Cellular, WiMAX, WiFi), there are still interesting problems to address to enhance the user quality of experience (QoE). In this paper, we highlight the challenges that face adaptive video streaming over wireless access networks. Then, we propose a a heuristic cross-layer adaptation algorithm named E2E-MAC to improve the video client application response to changing conditions in WiFi environments. The algorithm, which runs on the client side, combines end to end (E2E) throughput measurements with MAC-based measurements from the WiFi link to choose the appropriate streaming quality—to request from the server. Simulation results have shown that our algorithm reduces the occurrence of buffer under-run, increases the average achieved streaming quality, and lowers the rate of lost video frames. This is achieved with a minor overhead on the client application's side.

Abdallah S. Abdallah

Papers

A New Face Detection Technique using 2D DCT and Self Organizing Feature Map

A New Color Image Database for Benchmarking of Automatic Face Detection and Human Skin Segmentation Techniques

A Channel Selection Mechanism based on Incumbent Appearance Expectation for Cognitive Networks

On software tools and stack architectures for wireless network experiments

A cross-layer controller for adaptive video streaming over IEEE 802.11 networks