Iterative Time-Varying Filter Algorithm Based on Discrete Linear Chirp Transform

Denoising of broadband non--stationary signals is a challenging problem in communication systems. In this paper, we introduce a time-varying filter algorithm based on the discrete linear chirp transform (DLCT), which provides local signal decomposition in terms of linear chirps. The method relies on the ability of the DLCT for providing a sparse representation to a wide class of broadband signals. The performance of the proposed algorithm is compared with the discrete fractional Fourier transform (DFrFT) filtering algorithm. Simulation results show that the DLCT algorithm provides better performance than the DFrFT algorithm and consequently achieves high quality filtering.Comment: 6 pages, conference pape

Classification of Pulmonary Nodules by Using Hybrid Features

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity)

Design of a Broadband Semi-Conical PVDF Ultrasonic Sensor For Obstacle Detection Applications

Abstract Most of the commercially available air ultrasonic transducers are ceramic based and operate at 40 kHz. This paper describes a method to design and build ultrasonic transducers using low-cost piezoelectric Polyvinylidene Fluoride (PVDF) film. The transducer has a semi-conical geometry, which provides a higher bandwidth, low ringing time compared to traditional ceramic ultrasonic transducers. We have built a prototype sensor and compared its typical characteristics with a commercially available ceramic transducer. In experiments, pulse compression technique used to detect reflected ultrasonic waves with a high SNR. We found it to be practical for applications requiring short-range obstacle detection and distance measurement

Analysis of Gait Dynamics of ALS Disease and Classification of Artificial Neural Networks

In this study, a gait device was used for gathering data. A group comprising control group and ALS patients was requested to walk using this device. Gait signals of the control group individuals and ALS patients taken from their left feet were recorded by means of the sensors sensitive to the force which was placed to the device. Spectral and statistical analyses of these signals were made. The results obtained from these analyses were used for making classification with Artificial Neural Network. In consequence of the classification, the individuals with ALS disease were diagnosed accurately with an average rate of 82 %. In the study, the signals taken from left foot of 14 normal individuals and 13 ALS patients were analyzed

Epileptic EEG Classification by Using Advanced Signal Decomposition Methods

Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. EMD and its derivative, EEMD are recently developed methods used to decompose nonstationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). In this study multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then essential IMFs are selected. Finally, time- and spectral-domain, and nonlinear features are extracted from selected IMFs and classified. DMD is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. We present single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. As a third method, we use the SST representations of seizure and pre-seizure EEG data. Various features are calculated and classified by Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naive Bayes (NB), Logistic Regression (LR), Boosted Trees (BT), and Subspace kNN (S-kNN) to detect pre-seizure and seizure signals. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates

Knowledge and attitudes towards complementary and alternative medicine among medical students in Turkey

OBJECTIVE: This study aims to examine knowledge and attitudes towards Complementary and Alternative Medicine among medical students in Turkey, and find out whether they want to be trained in Complementary and Alternative Medicine (CAM). METHODS: A cross-sectional study was carried out between October and December 2010 among medical students. Data were collected from a total of seven medical schools. FINDINGS: The study included 943 medical students. The most well known methods among the students were herbal treatment (81.2 %), acupuncture (80.8 %), hypnosis (78.8 %), body-based practices including massage (77 %) and meditation (65.2 %), respectively. Acupuncture, aromatherapy, herbal treatment and meditation were better known among female participants compared to males (p < 0.05). Females and first year students, generally had more positive attitudes. A larger proportion of female students compared to male students reported that a doctor should be knowledgeable about CAM (p = 0.001), and this knowledge would be helpful in their future professional lives (p = 0.015). Positive attitudes towards and willingness to receive training declined as the number of years spent in the faculty of medicine increased. CONCLUSIONS: Majority of the medical students were familiar with the CAM methods widely used in Turkey, while most of them had positive attitudes towards CAM as well as willingness to receive training on the subject, and they were likely to recommend CAM methods to their patients in their future professional lives. With its gradual scientific development and increasing popularity, there appears a need for a coordinated policy in integrating CAM into the medical curriculum, by taking expectations of and feedback from medical students into consideration in setting educational standards

A microsatellite marker for yellow rust resistance in wheat

Bulk segregant analysis (BSA) was used to identify molecular markers associated with yellow rust disease resistance in wheat (Triticum aestivum L.). DNAs isolated from the selected yellow rust tolerant and susceptible F-2 individuals derived from a cross between yellow rust resistant and susceptible wheat genotypes were used to established a "tolerant" and a "susceptible" DNA pool. The BSA was then performed on these DNA pools using 230 markers that were previously mapped onto the individual wheat chromosomes. One of the SSR markers (Xgwm382) located on chromosome group 2 (A, B, D genomes) was present in the resistant parent and the resistant bulk but not in the susceptible parent and the susceptible bulk, suggesting that this marker is linked to a yellow rust resistance gene. The presence of Xgwm382 was also tested in 108 additional wheat genotypes differing in yellow rust resistance. This analysis showed that 81% of the wheat genotypes known to be yellow rust resistant had the Xgwm382 marker, further suggesting that the presence of this marker correlates with yellow rust resistance in diverse wheat germplasm. Therefore, Xgwm382 could be useful for marker assisted selection of yellow rust resistances genotypes in wheat breeding programs