Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.
Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.
Purpose To analyze the integrity of white matter (WM) tracts in primary insomnia patients and provide better characterization of abnormal WM integrity and its relationship with disease duration and clinical features of primary insomnia. Materials and Methods This prospective study was approved by the ethics committee of the Guangdong No. 2 Provincial People's Hospital. Tract-based spatial statistics were used to compare changes in diffusion parameters of WM tracts from 23 primary insomnia patients and 30 healthy control (HC) participants, and the accuracy of these changes in distinguishing insomnia patients from HC participants was evaluated. Voxel-wise statistics across subjects was performed by using a 5000-permutation set with family-wise error correction (family-wise error, P < .05). Multiple regressions were used to analyze the associations between the abnormal fractional anisotropy (FA) in WM with disease duration, Pittsburgh Sleep Quality Index, insomnia severity index, self-rating anxiety scale, and the self-rating depression scale in primary insomnia. Characteristics for abnormal WM were also investigated in tract-level analyses. Results Primary insomnia patients had lower FA values mainly in the right anterior limb of the internal capsule, right posterior limb of the internal capsule, right anterior corona radiata, right superior corona radiata, right superior longitudinal fasciculus, body of the corpus callosum, and right thalamus (P < .05, family-wise error correction). The receiver operating characteristic areas for the seven regions were acceptable (range, 0.60-0.74; 60%-74%). Multiple regression models showed abnormal FA values in the thalamus and body corpus callosum were associated with the disease duration, self-rating depression scale, and Pittsburgh Sleep Quality Index scores. Tract-level analysis suggested that the reduced FA values might be related to greater radial diffusivity. Conclusion This study showed that WM tracts related to regulation of sleep and wakefulness, and limbic cognitive and sensorimotor regions, are disrupted in the right brain in patients with primary insomnia. The reduced integrity of these WM tracts may be because of loss of myelination. (©) RSNA, 2016.
• The aberrant insular-based connectivity pattern of PI patients was detected. • Regions showing increased connectivity with left insular were mainly in emotional circle. • Significant correlations between changed FC and SDS and SAS score were found.
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