Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

Pattern Analysis and Applications - Efficient high-dimensional analyses of hyperspectral datasets and their utilization within classification algorithms is a popular topic in the field of data...     

X-ray Fluorescence Uptake Measurement of Functionalized Gold Nanoparticles in Tumor Cell Microsamples

Quantitative cellular in vitro nanoparticle uptake measurements are possible with a large number of different techniques, however, all have their respective restrictions. Here, we demonstrate the application of synchrotron-based X-ray fluorescence imaging (XFI) on prostate tumor cells, which have internalized differently functionalized gold nanoparticles. Total nanoparticle uptake on the order of a few hundred picograms could be conveniently observed with microsamples consisting of only a few hundreds of cells. A comparison with mass spectroscopy quantification is provided, experimental results are both supported and sensitivity limits of this XFI approach extrapolated by Monte-Carlo simulations, yielding a minimum detectable nanoparticle mass of just 5 pg. This study demonstrates the high sensitivity level of XFI, allowing non-destructive uptake measurements with very small microsamples within just seconds of irradiation time.     

New method for identifying finite degree Volterra series

In this paper, the identification of a class of nonlinear systems which admits input-output maps described by a finite degree Volterra series is considered. In actual fact, it appears that this class can model many important nonlinear multivariable processes not only in engineering, but also in biology, socio-economics, and ecology.To solve this identification problem, we propose a method based on local gradient search in a local parameterization of the state-space realization of finite degree Volterra series with infinite horizon. Using the local parameterization not only reduces the amount of the gradient calculations to the minimal value, but also overcomes the nonuniqueness problem of the optimal solution.Moreover, we propose a sequential projection method to provide an initial estimation of the parameters of finite degree Volterra series realization. This initial estimation is used to initialize the gradient search method.     

A Multi-Stage Secure IoT Authentication Protocol

The Internet of Things (IoT) is a network of heterogeneous and smart devices that can make decisions without human intervention. It can connect millions of devices across the universe. Their ability to collect information, perform analysis, and even come to meaningful conclusions without human capital intervention matters. Such circumstances require stringent security measures and, in particular, the extent of authentication. Systems applied in the IoT paradigm point out high-interest levels since enormous damage will occur if a malicious, wrongly authenticated device finds its way into the IoT system. This research provides a clear and updated view of the trends in the IoT authentication area. Among the issues covered include a series of authentication protocols that have remained research gaps in various studies. This study applies a comparative evaluation of authentication protocols, including their strengths and weaknesses. Thus, it forms the foundation in the IoT authentication field of study. In that direction, a multi authentication architecture that involves secured means is proposed for protocol authentication. Informal analysis can affect the security of the protocols. Burrows-Abadi-Needham (BAN) logic provides proof of the attainment of mutual authentication. NS3 simulator tool is used to compare the performance of the proposed protocol to verify the formal security offered by the BAN logic.     

Minimizing the total completion time in a two-machine flowshop problem with time delays

This article proposes to solve the problem of minimizing the total completion time in a two-machine permutation flowshop environment in which time delays between the machines are considered. For this purpose, an enumeration algorithm based on the branch-and-bound framework is developed, which includes new lower and upper bounds as well as dominance rules. The computational study shows that problems with up to 40 jobs can be solved in a reasonable amount of time.     

Potential of near-field microwave imaging in breast cancer detection utilizing tapered rectangular waveguide probes

Microwave imaging for medical applications has been of interest for many years. A novel near-field microwave non-invasive testing and evaluation (NIT&E) technique utilizing tapered rectangular waveguide probes is presented for breast cancer detection. Near-field microwave NIT&E techniques can be a successful candidate for the detection of breast cancer because of their potential in dealing with materials of low conductivity (i.e. lossy dielectrics like the breast tissue). The physical basis for breast tumor detection with microwave imaging is the contrast in dielectric properties of normal and malignant breast tissues. A method adopting Fourier transform matching (FTM) technique and utilizing the reflection coefficient at the aperture of a tapered rectangular waveguide sensor radiating into a breast is described resulting in microwave images that indicate the presence of a tumor. These images demonstrate the feasibility of detecting breast tumors using this approach.     

Anomalous high-frequency oscillations in a field emission tube and their significance in pulsed field emission

Relaxation oscillations occur when a capacitor is inserted in series with a field emission tube, a DC high-voltage power supply, and a ballast resistor. The waveform of these oscillations is highly reproducible with a dominant frequency of 200 MHz and a decay time of 20 ns. The peak current as high as 320 mA has been observed although the tungsten emitter is only rated for 10 microA. We have shown that these oscillations are due to a displacement current, charging of the anode-tip capacitance, and are not of a field emission origin. We conclude that the effects of displacement current should be considered in measurements of field emission with microsecond pulses, where high-current densities can be observed.     

Adaptive ROC-based ensembles of HMMs applied to anomaly detection

Hidden Markov models (HMMs) have been successfully applied in many intrusion detection applications, including anomaly detection from sequences of operating system calls. In practice, anomaly detection systems (ADSs) based on HMMs typically generate false alarms because they are designed using limited amount of representative training data. Since new data may become available over time, an important feature of an ADS is the ability to accommodate newly acquired data incrementally, after it has originally been trained and deployed for operations. In this paper, a system based on the receiver operating characteristic (ROC) is proposed to efficiently adapt ensembles of HMMs (EoHMMs) in response to new data, according to a learn-and-combine approach. When a new block of training data becomes available, a pool of base HMMs is generated from the data using a different number of HMM states and random initializations. The responses from the newly trained HMMs are then combined to those of the previously trained HMMs in ROC space using a novel incremental Boolean combination (incrBC) technique. Finally, specialized algorithms for model management allow to select a diversified EoHMM from the pool, and adapt Boolean fusion functions and thresholds for improved performance, while it prunes redundant base HMMs. The proposed system is capable of changing the desired operating point during operations, and this point can be adjusted to changes in prior probabilities and costs of errors. Computer simulations conducted on synthetic and real-world host-based intrusion detection data indicate that the proposed system can achieve a significantly higher level of performance than when parameters of a single best HMM are estimated, at each learning stage, using reference batch and incremental learning techniques. It also outperforms the learn-and-combine approaches using static fusion functions (e.g., majority voting). Over time, the proposed ensemble selection algorithms form compact EoHMMs, while maintaining or improving system accuracy. Pruning allows to limit the pool size from increasing indefinitely, thereby reducing the storage space for accommodating HMMs parameters without negatively affecting the overall EoHMM performance. Although applied for HMM-based ADSs, the proposed approach is general and can be employed for a wide range of classifiers and detection applications.