A radar-enabled collaborative sensor network integrating COTS technology for surveillance and tracking.

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution.

The study presented in this paper shows that electrocorticographic (ECoG) signals can be classified for making use of a human brain-computer interface (BCI) field. The results show that certain invariant phase transition features can be reliably used to classify two types of imagined movements accurately. Those are the left small-finger and tongue movements. Our approach consists of two main parts: channel selection based on Tsallis entropy in Hilbert domain and the nonlinear classification of motor imagery with support vector machines (SVMs). The new approach, based on Hilbert and statistical/entropy measurements, were combined with SVMs based on admissible kernels for classification purposes. The classification accuracy rates were 95% (264/278) and 73% (73/100) for training and testing sets, respectively. The results support the use of classification methods for ECoG-based BCIs.

Electroencephalography Spectral Power Density in First-Episode Mania: A Comparative Study with Subsequent Remission Period.

INTRODUCTION: Our aim in this study was to investigate spectral power density (PSD) in first-episode mania and subsequent remission period and to evaluate their difference. METHODS: Sixty-nine consecutive cases referring to our hospital within the previous 1 year, who were evaluated as bipolar disorder manic episode according to The Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) at the first episode and had the informed consent form signed by first degree relatives, were included in this study. Exclusion criteria included having previous depressive episode, using drugs which could influence electroencephalographic activity before electroencephalography (EEG), and having previous neurological disease, particularly epilepsy, head trauma, and/or loss of consciousness. EEG records were obtained using a digital device in 16 channels; 23 surface electrodes were placed according to the International 10-20 system. Spectral power density (dbµV/Hz) of EEG signal provided information on the power carried out by EEG waves in defined frequancy range per unit frequency in the present study. RESULTS: A peak power value detected on the right with FP2P4 and on the left with F7T3 electrodes were found to be higher in the manic episode than in the remission period (p=0.018 and 0.025). In the remission period, in cases with psychotic symptoms during the manic period, F4C4 peak power value was found to be lower than that in cases with no psychotic findings during the manic period (p=0.027). There was no relation was found between YMRS scores and peak power scores. CONCLUSION: Electrophysiological corollary of mood episode is present from the onset of the disease, and it differs between the manic and remission periods of bipolar disorder. In the remission period, peak power values of PSD distinguish cases with psychotic findings from cases without psychotic findings when they were manic.

Sub-similarity matching based on data mining with dihedral angles.

Protein sub-similarity matching remains largely unknown even though it is becoming one of the most important open problems in bioinformatics for drug and vaccine design. Variations in human immune responses to vaccines are, and thus responses, fail. We propose a new matching and protein alignment method based on clustering and Longest Common Subsequence (LCS) techniques. After clustering, we found LCS between a candidate protein and meningitis outer membrane antigen for each candidate. Each similarity was scored, and closest similarities were determined with statistical methods. We located three closely matching proteins among a total of 50 human immune system proteins. Moreover, we selected a HIV-1 related protein from one of scenarios, because it revealed a relationship between HIV and meningitis patients. We also found that Ω main chain torsion angle for atoms CA, C and N is the best angle for determining sub-similarities between meningitis antigen and immune proteins.

Determination of ECoG information flow activity based on Granger causality and Hilbert transformation.

Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm×8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8×8 (0.016-300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.