Hiding biometric data

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.     

Use of levodopa to relieve pain from painful symmetrical diabetic polyneuropathy

Levodopa has been used to treat some painful conditions and found to be effective in neuropathic pain due to herpes zoster in a double-blind study. From our anecdotal observations about the efficacy of levodopa on diabetic neuropathic pain, we designed a double-blind placebo-controlled study to test levodopa in painful diabetic neuropathy. Twenty-five out-patients with painful symmetrical diabetic polyneuropathy were admitted to the study. Fourteen patients were given 100 mg levodopa plus 25 mg benserazide to be taken three times per day for 28 days. Eleven patients were given identical placebo capsules. A blinded neurologist evaluated the patients clinically and performed Visual Analogue Scale (VAS) measurement every week from day 0 to day 28. The results seemed promising and levodopa may be a choice for the control of pain in neuropathy for which we do not have many alternative treatments.     

Biometric cryptosystems: issues and challenges

In traditional cryptosystems, user authentication is based on possession of secret keys; the method falls apart if the keys are not kept secret (i.e., shared with non-legitimate users). Further, keys can be forgotten, lost, or stolen and, thus, cannot provide non-repudiation. Current authentication systems based on physiological and behavioral characteristics of persons (known as biometrics), such as fingerprints, inherently provide solutions to many of these problems and may replace the authentication component of traditional cryptosystems. We present various methods that monolithically bind a cryptographic key with the biometric template of a user stored in the database in such a way that the key cannot be revealed without a successful biometric authentication. We assess the performance of one of these biometric key binding/generation algorithms using the fingerprint biometric. We illustrate the challenges involved in biometric key generation primarily due to drastic acquisition variations in the representation of a biometric identifier and the imperfect nature of biometric feature extraction and matching algorithms. We elaborate on the suitability of these algorithms for digital rights management systems.     

Effect of heat treatment on the creep behavior of α/β SiAlON sintered with multication oxide sintering additives

SiAlONs are important materials for high-temperature applications and creep properties of SiAlONs are largely controlled by the amount and type of sintering additives. It has been established that heat treatment can reduce the amount of amorphous intergranular phase through crystallization. However, there is no study on the creep behavior of heat-treated SiAlON ceramics containing multication sintering additives. Therefore, the aim of the study was to investigate the effect of heat treatment on the creep properties of multication containing (Y-Sm-Ca oxides) α/β-SiAlON ceramics. The heat treatments of the sintered samples were carried out at 1600°C for 2 hours. The creep tests were carried out in the range 1300-1400°C under different loads (50-150 MPa). The existing phases and the microstructures of samples before and after creep were investigated using XRD and SEM techniques. It was found that heat treatment resulted in a better creep performance compared to as-sintered samples. The activation energy and stress exponent for heat-treated SiAlONs were also calculated as 708 ± 45 kJ/mol and 1.4, respectively. Compared to the sintered sample values, the results suggested that the acting creep mechanism of grain-boundary sliding and cavitation was reduced with the heat treatment.     

A taxonomy and evaluation for developing 802.11‐based wireless mesh network testbeds

The definition of wireless mesh networks (WMNs) has been used in the literature to connote and epitomize the ideal, ubiquitous, pervasive, and autonomic networking technology. An increasing interest has been emerging on the development of 802.11‐based WMN testbeds to test the new ideas and approaches more realistically as opposed to relying solely on simulations. Although the developed testbeds have provided several insights to researchers for furthering the technology, there are still several issues that need to be addressed, particularly, with the approval of new standards, such as IEEE 802.11s, IEEE 802.11n, and IEEE 802.16, and upcoming protocols, such as IEEE 802.11ac, 802.11ad, 802.11ah, and 802.11af TV White Space efforts. In this paper, our goal is to provide a taxonomy and insightful guidelines for the creation of 802.11‐based WMN testbeds as well as to identify several features that future WMN testbeds should possess. Utilizing these features, we evaluate the existing WMN testbeds. Finally, in addition to the existing WMN testbed experiments conducted at several layers of the protocol stack, we provide a list of open future research issues that can benefit from experiments on WMN testbeds. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive neuro-fuzzy computing technique for suspended sediment estimation

This paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in suspended sediment estimation. The monthly streamflow and suspended sediment data from two stations, Kuylus and Salur Koprusu, in Kizilirmak Basin in Turkey are used as case studies. The estimation results obtained by using the neuro-fuzzy technique are tested and compared with those of the artificial neural networks and sediment rating curves. Root mean squared errors, mean absolute errors and correlation coefficient statistics are used as comparing criteria for the evaluation of the models’ performances. The comparison results reveal that the neuro-fuzzy models can be employed successfully in monthly suspended sediment estimation.     

Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems

We examine the performance of multimodal biometric authentication systems using state-of-the-art commercial off-the-shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.