Applied Electronics and Instrumentation Engineering

In many applications of electronic nose, the instrument is trained with data generated by human experts prior to its deployment in the fields. Quite often, these data are conflicting and inaccurate and thus the performance of an electronic nose is degraded. Moreover, degradation of its performance may also be due to the presence of redundant features or sensors in the array. While deploying an electronic nose for a specific application, it is observed that some of the sensors may not be required and only a subset of the sensor array contributes to the decision, which implies that optimization of the sensor array is also very important. To obtain a consistent and precise data set, both the conflicting data and irrelevant features must be removed. The rough set theory is capable of dealing with such an imprecise, inconsistent data set and in this paper, the rough-set based algorithm has been applied to remove the conflicting training patterns and optimize the sensor array in an electronic nose instrument used for sensing aroma of black tea samples.

It has always been in human nature that he tends to keep his prized possession in a secured place, a place where he thinks it would be safe from the rest of the world. The technical knowledge of man has increased exponentially with time leading to breakthroughs in modern security system. As every domain has witnessed pros and cons, hence the modern technology has also given rise to unethical developments in the arena of technically advanced security systems. The proposed system in this paper has been developed keeping in mind the vulnerabilities projected by the said technological advancement. A complex set of logic enhanced by competing software has been incorporated to design the work in concern.

The present status of rheumatoid arthritis treatment is early aggressive therapy. The main obstacle to early institution of therapy is determination of irreversibility of arthritis. The objective was to find out the outcome of early arthritis and predictors of early arthritis evolving into rheumatoid arthritis.

The present status of rheumatoid arthritis treatment is early aggressive therapy. The main obstacle to early institution of therapy is determination of irreversibility of arthritis. The objective was to find out the outcome of early arthritis and predictors of early arthritis evolving into rheumatoid arthritis.

It has always been in human nature that he tends to keep his prized possession in a secured place, a place where he thinks it would be safe from the rest of the world. The technical knowledge of man has increased exponentially with time leading to breakthroughs in modern security system. As every domain has witnessed pros and cons, hence the modern technology has also given rise to unethical developments in the arena of technically advanced security systems. The proposed system in this paper has been developed keeping in mind the vulnerabilities projected by the said technological advancement. A complex set of logic enhanced by competing software has been incorporated to design the work in concern.

—Cognitive failure of drivers during driving becomes nowadays the most alarming issue for traffic fatalities, and hence, it is important to intend correct motor actions during driving to avoid accidents. This paper proposes a novel two-stage motor intension classifier to detect the correct motor planning of the drivers during car driving. Common spatial pattern filters are introduced here for feature extraction as well as artifact removal from the raw electroencephalographic signals. Experiments undertaken further confirm that the basic motor intensions including i) steering control, ii) accelerator, iii) brake, and iv) no action and as well as the sub-classification of individual control are best classified by Support vector machine with polynomial kernel from a list of standard classifiers with an average classification accuracy of 85.70% Keywords— cognitive failure; motor intension classifier; electroencephalography; common spatial pattern, event-related desynchronization/synchronization, support vector machine with polynomial kernel.

This paper aims at detecting online cognitive failures in driving by decoding the electroencephalography (EEG) signals acquired during visual alertness, motor planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the preprocessed EEG signals obtained from his prefrontal and frontal lobes into two classes: alert and nonalert. Motor planning performed by the driver using the preprocessed parietal signals is classified into four classes: braking, acceleration, steering control, and no operation. Cognitive failures in motor planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the copilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time delay between the onset of motor imagination and the electromyogram response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration, and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/h, the lead time is more than 600 ms, which offer a safe distance of 10.66 m. Index Terms-EEG, motor-execution and type-2 fuzzy classifiers, motor-planning, visual alertness. I. INTRODUCTION D RIVING involves complex cognitive processes, concerning sensory perception, motor-planning and motor-execution. The cognitive failure detection (CFD) problem, introduced here, refers to classifying cognitive failures involved in visual alertness (VA), motor-planning (MP) and motor-execution (ME) phases of driving with a motive to alert the driver by an (audio) alarm before an accident takes place.

Patients with prosthesis defects find it is very difficult to perform day-to-day basic tasks which involve employment of their limbs. This motivates us to develop a system where an artificial limb is employed to mimic the arm gestures of the patients for assisting them. Towards developing this system, we have taken the help from the electroencephalography (EEG) signals acquired from the brain of the patients to build a bypass network (BPN) to direct the artificial limb. Since difficulties are already present in the arm movements of the patients (here subjects), thus only gestures of those subjects are not sufficient to build the proposed system. This research finds tremendous applications in rehabilitative aid for the disable persons. To concretize our goal we have developed an experimental setup, where the target subject (for training phase healthy subjects are taken into account) is asked to catch a ball while his/her brain (occipital, parietal and motor cortex) signals using EEG acquisition device and body gestures using Kinect sensor are simultaneously acquired. These data are mapped using four cascade-correlation learning architecture (CCLA) to train artificial limb (we have used Jaco robot arm) to move accordingly. Utilizing the mapping results obtained from these four CCLAs, a BPN is developed. When a rehabilitative patient is unable to catch the ball, then in that scenario, the artificial limb is helpful for assisting the patient to catch the ball with a high accuracy of 85.65%. The proposed system can be implemented not only for ball catching experiment but also in several applications where an artificial limb needs to perform a locomotive task based on EEG and body gesture.

The main notion of this paper is to identify the cognitive load during a mental arithmetic task experiment using fNIRS signals. The first objective is to classify the difficulty level and the state of inactivity during the given task. To identify the classes, the feature vectors have to undergo all the possible steps of a pattern classification problem. In this paper, we have developed a novel Feature Selection technique to reduce the dimension of the feature vectors by omitting the redundant features. For this purpose, an objective function depending upon the class density or likelihood functions is optimized using the well-known Differential Evolution algorithm. General type-2 fuzzy classifier is used for subsequent classification step. The proposed Feature selection technique gives a satisfactory accuracy results over principal component analysis. Also the fuzzy classifier outperforms the other well-known classifier like support vector machine, k-nearest neighborhood. The load of a subject undergoing the experiment is measured at a particular class relying upon the mean type-1 fuzzy value of all feature entities.