Late combination shows that MEG adds to MRI in classifying MCI versus controls.

Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI

Clipped Input RLS Applied to Vehicle Tracking

A new variation to the RLS algorithm is presented. In the clipped RLS algorithm (CRLS), proposed in updating the filter weights and computation of the inverse correlation matrix, the input signal is quantized into three levels. The convergence of the CRLS algorithm to the optimum Wiener weights is proved. The computational complexity and signal estimation error is lower than that of the RLS algorithm. The CRLS algorithm is used in the estimation of a noisy chirp signal and in vehicles tracking. Simulation results in chirp signal detection shows that this algorithm yields considerable error reduction and less computation time in comparison to the conventional RLS algorithm. In the presence of strong noise, also using the proposed algorithm in tracking of 59 vehicles shows an average of % reduction in prediction error variance relative to conventional RLS algorithm.</p

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

In this study, a method for holistic recognition of handwritten Farsi words is proposed, which fuses the outputs of right‐to‐left (RtL) and left‐to‐right (LtR) hidden Markov models (HMMs). The experimental results on 16,000 images of 200 names of Iranian cities, from the ‘Iranshahr 3’ are presented and compared with those methods using only RtL or LtR models. Experimental results show that the main sources of error are similar beginnings or similar endings of the words. Since RtL and LtR models when dealing with the words behave differently, there is notable error diversity between the two classifiers in such a way that their combination increases the recognition rate. Compared to the RtL‐HMM, the product of output scores of the RtL and LtR‐HMMs reduces the classification error to about 6, 6 and 3%, for three different feature sets. A subjective error analysis on the results is also provided

Common Raman Spectral Markers among Different Tissues for Cancer Detection

Introduction Raman spectroscopy is a vibrational spectroscopic technique, based on inelastic scattering of monochromatic light. This technique can provide valuable information about biomolecular changes, associated with neoplastic transformation. The purpose of this study was to find Raman spectral markers for distinguishing normal samples from cancerous ones in different tissues. Materials and Methods Ten tissue samples from the breast, colon, pancreas, and thyroid were collected. A Raman system was used for Raman spectroscopic measurement of tissues at 532 nm laser excitation. Five to six Raman spectra were acquired from each sample (a total of 52 spectra). Raman spectra were investigated in important bands associated with Amid1, CH2 (scissoring), Amid3, d(NH), n(C-C), and das (CH3) in both normal and cancerous groups. In addition, common spectral markers, which discriminated between normal and cancerous samples in the above tissues, were investigated. Results Common spectral markers among different tissues included intensities of Amid3 and CH2 (scissoring) and intensity ratios of I(Amid1)/I(CH2), I(n(C-C))/I(CH2), and I(d(NH))/I(CH2). This study showed that Amid1-, n(C-C)-, and d(NH)-to-CH2 intensity ratios can discriminate between normal and cancerous samples, with an accuracy of 84.6%, 82.7%, and 82.7% in all studied tissues, respectively. Conclusion This study demonstrates the presence of common spectral markers, associated with neoplastic changes, among different tissues