Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.     

Two-server password-only authenticated key exchange

Typical protocols for password-based authentication assume a single server that stores all the information (e.g., the password) necessary to authenticate a user. An inherent limitation of this approach, assuming low-entropy passwords are used, is that the user?s password is exposed if this server is ever compromised. To address this issue, it has been suggested to share a user?s password information among multiple servers, and to have these servers cooperate (possibly in a threshold manner) when the user wants to authenticate. We show here a two-server version of the password-only key-exchange protocol of Katz, Ostrovsky, and Yung (the KOY protocol). Our work gives the first secure two-server protocol for the password-only setting (in which the user need remember only a password, and not the servers? public keys), and is the first two-server protocol (in any setting) with a proof of security in the standard model. Our work thus fills a gap left by the work of MacKenzie et al. (2006) [31] and Di Raimondo and Gennaro (2006) [16]. As an additional benefit of our work, we show modifications that improve the efficiency of the original KOY protocol.     

FQL‐RED: an adaptive scalable schema for active queue management

In a potentially congested network, random early detection (RED) active queue management (AQM) proved effective in improving throughput and average queuing delay. The main disadvantage of RED is its sensitive parameters that are impossible to estimate perfectly and adjust manually because of the dynamic nature of the network. For this reason, RED performs differently during different phases of a scenario and there is no guarantee that it will have optimal performance. Giving adaptability to RED has been the subject of broad research studies ever since RED was proposed. After a substantial study of AQM schemes and presenting a novel categorization for so‐called modern approaches utilizing artificial intelligence tools to improve AQM, this paper proposes an algorithm enhancing RED as an add‐on patch that makes minimal changes to the original RED. Being built on the basis of AQM schemes like ARED and Fuzzy‐RED, this algorithm inherits adaptability and is able to adjust RED inaccurate parameters regarding network traffic status, trying to optimize throughput and average queuing delay in a scenario. This algorithm is a Q‐learning method enhanced with a fuzzy inference system to provide RED with self‐adaptation and improved performance as a result. Given the name of FQL‐RED, this algorithm outperformed RED, ARED, and Fuzzy‐RED, as the OPNET simulations show. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantifying Prefibrillar Amyloids in vitro by Using a “Thioflavin‐Like” Spectroscopic Method

In Alzheimer's disease (AD) and other neurodegenerative disorders, proteins accumulate into ordered aggregates, called amyloids. Recent evidence suggests that these structures include both large, insoluble fibrils and smaller, prefibrillar structures, such as dimers, oligomers, and protofibrils. Recently, focus has shifted to the prefibrillar aggregates because they are highly neurotoxic and their levels appear to correlate with cognitive impairment. Thus, there is interest in finding methods for specifically quantifying these structures. One of the classic ways of detecting amyloid formation is through the fluorescence of the benzothiazole dye, thioflavin T (ThT). This reagent has been a “workhorse” of the amyloid field because it is robust and inexpensive. However, one of its limitations is that it does not distinguish between prefibrillar and fibrillar aggregates. We screened a library of 37 indoles for those that selectively change fluorescence in the presence of prefibrillar amyloid‐β (Aβ). From this process, we selected the most promising example, tryptophanol (TROL), to use in a quantitative “thioflavin‐like” assay. Using this probe in combination with electron microscopy, we found that prefibrils are largely depleted during Aβ aggregation in vitro but that they remain present after the apparent saturation of the ThT signal. These results suggest that a combination of TROL and ThT provides greater insight into the process of amyloid formation by Aβ. In addition, we found that TROL also recognizes other amyloid‐prone proteins, including ataxin‐3, amylin, and CsgA. Thus, this assay might be an inexpensive spectroscopic method for quantifying amyloid prefibrils in vitro.     

Study on the structure and properties of Poly(methylmethacrylate)/Polypyrrole-Graphene Oxide nanocomposites

In this paper Poly(methylmethacrylate)/Polypyrrole-Graphene Oxide (PMMA/PPy-GO) nanocomposites were prepared using in-situ chemical polymerization method and its structure and properties were studied. Fourier transform infrared (FTIR), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) analysis confirmed that PPy nanopraticles covered the GO nanosheets surface and PMMA/PPy-GO nanocomposite were prepared successfully. The mechanical, electrical and thermal stability of the PMMA nanocomposites were also investigated using Tensile, Impact, Thermogravimetric analysis (TGA) and four-point probe methods. Correlation between direct morphological observation and final properties demonstrated that network structure formed by PPy-GOs in PMMA matrix. Tensile analysis showed that the addition of 0.4 wt % PPy-GO hybrids lead to 24.4 % enhancement in the Young’s modulus of PMMA compared to 5.0 % improvement that achieved at the same loading level of GO. Electrical conductivity measurement showed that dispersion of PPy-GO in PMMA matrix increased AC conductivity in the range of 8 orders of magnitude compared to PMMA. TGA analysis showed that the thermal stability of the PMMA nanocomposites improved over 20 ?C.     

Transition metal cations extraction by ester and ketone derivatives of chromogenic azocalix[4]arenes

The molecule of azocalix[n]arene is a macrocyclic used effectively in the complexation of the heavy metal pollutants (like silver and mercury). In this work, our main aim is to prepare new chromogenic azocalix[n]arene molecules to elaborate an extractant with high extractant selectivity for metal ions able to detect this type of pollutant. The solvent extraction properties of four acetyls, four methyl ketones and four benzoyls derivatives from azocalix[4]arenes which were prepared by linking 4-ethyl, 4-n-butyl, 4-acetamid anilin and 2-aminothiazol to calix[4]arene through a diazo-coupling reaction, the alkaline earth (Sr2+) and the transition (Ag+, Hg2+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, Cr3+) metal cations have been determined by extraction studies with metal picrates. Both ketones are better extractants than esters, and show a strong preference for Ag+, while Cu2+ and Cr3+ are the most extracted cation with the esters. Both acetyl and benzoyl esters are good carriers for Ag+ and Hg2+.     

Single string planning problem arising in liner shipping industries: A heuristic approach

We propose an efficient heuristic approach for solving instances of the Single String Planning Problem (SSPP) arising in the liner shipping industry. In the SSPP a Liner Service Provider (LSP) only revises one of its many operational strings, and it is assumed that the other strings are unchangeable. A string is a service route composed of a sequence of port calls—a call is a visit to a port followed by loading/unloading operations made by a vessel. In a string the vessel's round trip terminates at the same port that it started from, and the port calls follow a published itinerary. The SSPP is regularly encountered by all LSPs, and a major part of their seasonal network planning process is devoted to repeatedly solving SSPP for different regions using experts' knowledge. Despite the practical importance of the problem, very little has been written about it in the literature. A revision is carried out in the form of a controlled re-sequencing, insertion and elimination of ports from along the current string, given a set of ports limited to those that exist on the string and a set of potential ones. The outcome determines the required capacity, service level (frequency), call sequence, etc., corresponding to the LSP's seasonal strategy. Exact decomposition methods are limited and can solve only very small size instances—small in terms of the number of ports, vessel classes, vessel number and commodities. In contrast, the proposed heuristic method is an efficient approach for obtaining high quality and practical solutions to real-size instances in significantly less computational time.     

Liquid Biomarkers for Improved Diagnosis and Classification of CNS Tumors

Liquid biopsy, as a non-invasive technique for cancer diagnosis, has emerged as a major step forward in conquering tumors. Current practice in diagnosis of central nervous system (CNS) tumors involves invasive acquisition of tumor biopsy upon detection of tumor on neuroimaging. Liquid biopsy enables non-invasive, rapid, precise and, in particular, real-time cancer detection, prognosis and treatment monitoring, especially for CNS tumors. This approach can also uncover the heterogeneity of these tumors and will likely replace tissue biopsy in the future. Key components of liquid biopsy mainly include circulating tumor cells (CTC), circulating tumor nucleic acids (ctDNA, miRNA) and exosomes and samples can be obtained from the cerebrospinal fluid, plasma and serum of patients with CNS malignancies. This review covers current progress in application of liquid biopsies for diagnosis and monitoring of CNS malignancies.     

Extended Kalman Filter frequency tracker for nonstationary harmonic signals

In this paper, we propose a frequency tracking algorithm based on an Extended Kalman Filter (EKF). We introduce a generalized state space model to estimate and track frequency of a harmonic signal embedded in broad-band noise. Such nonstationary noisy harmonic signals are characterized by time-varying frequencies and amplitudes. Developing a modified state-space model, we improve performance of EKF frequency tracker for these signals. The proposed method is also used in an adaptive algorithm to estimate noise variance which is assumed to be unknown. Simulation results reveal superiority of the proposed method compared with typical EKF, short time Fourier transform, and interpolated discrete Fourier transform.