Uvais Qidwai

A Hybrid Least-Squares GA Based Algorithm for Harmlonic Estimation

Harmonic estimation in a distorted signal along with additive noise has been an area of interest for researchers in many disciplines of science and engineering. This paper presents a new algorithm to estimate the harmonics in power systems using genetic algorithms (GA). The harmonic estimation problem is linear in amplitude and nonlinear in phase. The proposed hybrid algorithm takes advantage of this structure and iterates between linear least-squares amplitude estimation and the nonlinear GA-based phase estimation. Improvement in both convergence for solution as well as processing time is demonstrated from this algorithm.

Fun to learn: project-based learning in robotics for computer engineers

This article shares my experiences with the project-based methodology of teaching the same material that a standard text in robotics would teach and will comparatively elaborate the improvement observed in the group of students involved. Other than the understanding of the course material, the use of project-based learning methodology has shown a great increase in motivation and focus on the subject in the students towards the engineering education. The performance improvement is presented in this work as a comparison with respect to underlying program educational outcomes between the previous years of teaching in conventional way, with past two years of experience using the projectbased approach. The work includes appropriate rubrics involved in evaluating the subject. While the work is seemingly undergraduate teaching exercise, it has proven so far to have very positive impact on the industrial research project performed by the author.

Infrared Image Enhancement using Bounds for Surveillance Applications

In this paper, two algorithms have been presented to enhance the infrared (IR) images. Using the autoregressive moving average model structure and Hinfin optimal bounds, the image pixels are mapped from the IR pixel space into normal optical image space, thus enhancing the IR image for improved visual quality. Although Hinfin-based system identification algorithms are very common now, they are not quite suitable for real-time applications owing to their complexity. However, many variants of such algorithms are possible that can overcome this constraint. Two such algorithms have been developed and implemented in this paper. Theoretical and algorithmic results show remarkable enhancement in the acquired images. This will help in enhancing the visual quality of IR images for surveillance applications.

Fuzzy logic: A “simple” solution for complexities in neurosciences?

Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum. Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures. Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.

Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients

BACKGROUND Despite a lot of research into patient selection, a significant number of patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction. METHODS Data of 501 patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome. RESULTS Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98. CONCLUSION Fuzzy inference system is a sensitive method of predicting patients who will fail to improve with surgical intervention.

A novel system for scoring of hormone receptors in breast cancer histopathology slides

Grading of breast cancer is often done by an expert pathologist based on their analysis of micro-level structural features of the cancerous tissue specimen as well as the level of presence of certain protein molecules in the specimen. The process of assessment of the level of presence of estrogen and progesterone receptors molecules is subjective by its very nature and therefore, causes large inter-expert and sometimes even intra-expert variability, potentially adding noise to the process of selecting the treatment regime for the patient. Quantification of immunohistochemical stains is critical for an objective assessment of breast cancer histopathology specimens. We present a fast, compact and inexpensive system for scoring the Estrogen and Progesterone hormone receptors in breast cancer histopathology slides using image analysis algorithm. We describe hardware and software issues in the construction of the system, and present a comparison of scores produced by our system to those produced by many expert pathologists.

Ubiquitous Arabic voice control device to assist people with disabilities

In this paper, the design of a very cost effective Arabic speech language controlled device, with continuous speech recognition is presented. The device has been developed as an integrated prototype (patent pending) with the local center for providing technological solutions for people with disabilities. This system is explained with three case studies; which are computer keyboard and mouse control, a wheelchair control and industrial articulated robot control (as a test case too demonstrate the potential of the p resented device for extending it to numerous other applications). The main motivation for this design came from personal experiences as well as collaboration with the rehabilitation centers and local hospitals that many elderly patients and people with disabilities usually have stable speech, even in case of quadriplegic, and elderly patients. Moreover, they have more affection for their own language. Hence, a voice/speech controlled system in their own language (Arabic in this case) would make their interactions more convenient and would help in enhancing their quality of life. The presented system would allow them to have a way of expressing their idea by a voice controlled portable system, which is easy plug-and-play man-machine interface.

Autonomous corrosion detection in gas pipelines: a hybrid-fuzzy classifier approach using ultrasonic nondestructive evaluation protocols

In this paper, a customized classifier is presented for the industry-practiced nondestructive evaluation (NDE) protocols using a hybrid-fuzzy inference system (FIS) to classify the corrosion and distinguish it from the geometric defects or normal/healthy state of the steel pipes used in the gas/petroleum industry. The presented system is hybrid in the sense that it utilizes both soft computing through fuzzy set theory, as well as conventional parametric modeling through H? optimization methods. Due to significant uncertainty in the power spectral density of the noise in ultrasonic NDE procedures, the use of optimal H2 estimators for defect characterization is not so accurate. A more appropriate criterion is the H? norm of the estimation error spectrum which is based on minimization of the magnitude of this spectrum and hence produces more robust estimates. A hybrid feature set is developed in this work that corresponds to a) geometric features extracted directly from the raw ultrasonic A-scan data (which are the ultrasonic echo pulses in 1-D traveling inside the metal perpendicular to its 2 surfaces) and b) mapped features from the impulse response of the estimated model of the defect waveform under study. An experimental strategy is first outlined, through which the necessary data are collected as A-scans. Then, using the H? estimation approach, a parametric transfer function is obtained for each pulse. In this respect, each A-scan is treated as output from a defining function when a pure/healthy metals A-scan is used as its input. Three defining states are considered in the paper; healthy, corroded, and defective, where the defective class represents metal with artificial or other defects. The necessary features are then calculated and are then supplied to the fuzzy inference system as input to be used in the classification. The resulting system has shown excellent corrosion classification with very low misclassification and false alarm rates.

Fuzzy detection of critical cardiac abnormalities using ECG data: A ubiquitous approach

Electrocardiogram (ECG) based health diagnosis of cardiac diseases has been a saturated area of research and almost any known heart-condition can be detected and diagnosed by doctors in the hospital setting. However, these approaches fall extremely short when attempting to design an automatic detection system to do the same. The situation becomes even more difficult when the measurement system is being designed for a ubiquitous application in which the patient is not confined to the hospital and the device is attached to him/her externally while the person is involved in daily chores. This paper presents the classification technique for one such system which is being built by the same team. Hence the presented work covers the initial findings related to some of the cardiac conditions that can be monitored in the ubiquitous scenario. This detection system produces warning signals that can be conveyed to the concerned healthcare personnel if signs of critical cardiac conditions begin to show. Due to the compact nature of such systems, the detection and classification techniques have to be extremely simple in order to be stored in the small memory of the microcontroller of the ubiquitous system. The paper presents one such technique that is a combination of digital filters and Fuzzy classifications implemented at look-up table level in order to preserve the simplicity of the system.

Robotic toys for autistic children: Innovative tools for teaching and treatment

This paper presents an initial study related to the use of robotic toys as teaching and therapeutic aid tools for teachers and care-givers as well as parents of children with various levels of autism spectrum disorder (ASD). Some of the most common features related to the behavior of a child with ASD are his/her social isolation, living in their own world, not being physically active, and not willing to learn new things. While the teachers, parents, and all other related care-givers do their best to improve the condition of these kids, it is usually quite an uphill task. However, one remarkable observation that has been reported by several teachers dealing with ASD children is the fact that the same children do get attracted to toys with lights and sounds. Hence, this project targets the development/modifications of such existing toys into appropriate behavior training tools which the care-givers can use as they would desire. Initially, the remote control is in hand of the trainer, but after some time, the child is entrusted with the control of the robotic toy to test for the level of interest. It has been found during the course of this study that children with quite low learning activity got extremely interested in the robot and even advanced to controlling the robot with the PS2 type joystick. It has been observed that the children did show some hesitation in the beginning 5 minutes of the very first sessions of such interaction but were very comfortable afterwards which has been considered as a very strong indicator of the potential of this technique in teaching and rehabilitation of children with ASD or similar brain disorders.