Evaluation of feature selection algorithms for detection of depression from brain sMRI scans

Detection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.

A new approach based on support vector machine for solving stochastic optimization

Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.

An ultra low power OTA with improved unity gain bandwidth product

An operational transconductance amplifier (OTA) using dynamic threshold MOS (DTMOS) and hybrid compensation technique is presented in this paper. The proposed topology is based on a bulk and gate driven input differential pair. Two separate capacitors are employed for the OTA compensation where one of them is used in a signal path and the other one in a non-signal path. The circuit is designed in the 0.18μm CMOS TSMC technology. The proposed design technique shows remarkable enhancement in unity gain-bandwidth and also in DC gain compared to the bulk driven input differential pair OTAs. The Hspice simulation results show that the amplifier has a 92dB open-loop DC gain and a unity gain-bandwidth of 135kHz while operating at 0.4V supply voltage. The total power consumption is as low as 386nW which makes it suitable for low-power bio-medical and bio-implantable applications.

Space vector modulation based fast speed response field-orientation control of induction motor drive with adaptive neural integrator

This paper presents a novel fast speed response control strategy for the poly-phase induction motor drive system based on flux angle. The control scheme is derived in rotor field coordinates and employs the estimation of the rotor flux and its position. An adaptive notch filter is proposed to eliminate the dc component of the integration of signals used for the rotor flux estimation. To improve the performance of the rotor flux estimator, derivative term of the back emf is incorporated in the system. The voltage components in the synchronous reference frame are generated in the controllers which are transformed to stationary reference frame for driving the motor. Space vector modulation technique is used here. Simulation of the drive system was carried out and the results were compared with those obtained for a system that produces the above mentioned voltage components using the conventional PI controller. It is observed that the proposed control methodology provides faster response than the conventional PI controller incorporated system.

Vector control of a position sensorless SPMSM drive with RNN based stator flux estimator

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on single layer Recurrent Neural Network (RNN) is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The RNN estimator is used to estimate flux components along the stator fixed stationary axes. The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The proposed estimator can be used to accurately measure the motor fluxes and rotor angle over a wide speed range. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implememnt in a practical environment

Psychosocial stress is positively associated with body mass index gain over 5 years : evidence from the longitudinal AusDiab study

Objective
Emerging evidence suggests that psychosocial stress may influence weight gain. The relationship between stress and weight change and whether this was influenced by demographic and behavioral factors was explored.

Design and Methods
A total of 5,118 participants of AusDiab were prospectively followed from 2000 to 2005. The relationship between stress at baseline and BMI change was assessed using linear regression.

Results
Among those who maintained/gained weight, individuals with high levels of perceived stress at baseline experienced a 0.20 kg/m2 (95% CI: 0.07-0.33) greater mean change in BMI compared with those with low stress. Additionally, individuals who experienced 2 or ≥3 stressful life events had a 0.13 kg/m2 (0.00-0.26) and 0.26 kg/m2 (0.14-0.38) greater increase in BMI compared with people with none. These relationships differed by age, smoking, and baseline BMI. Further, those with multiple sources of stressors were at the greatest risk of weight gain.

Conclusion
Psychosocial stress, including both perceived stress and life events stress, was positively associated with weight gain but not weight loss. These associations varied by age, smoking, obesity, and multiple sources of stressors. Future treatment and interventions for overweight and obese people should consider the psychosocial factors that may influence weight gain.