Saeed Ahmed Khan

Image edge detection scheme using wavelet transform

The Wavelet Transform remained quite rapidly used technique today for analysing the signals. For image edge detection, wavelet transform provides facility to select the size of the image details that will be detected. Wavelets transform separates the lower frequencies and higher frequencies easily, which is prime important for edge detection. The wavelet scale sets the size of detected edges. For discrete wavelet transform, many signals are passed through wavelet filter for choice of the scale. For 2-D image, wavelet analysis is carried out in terms of horizontal and vertical function and edges are detected separately. In this paper the Daubachies wavelet transform has been used, where 2-D image is decomposed at three levels and at each level lower and higher frequencies have been separated. As Daubachies wavelet gives appropriate edge at three levels for black and white image along with some ghost edges. Some threshold has been used to cater these ghost edges. For edges detection in an image, a MATLAB code have been developed, which tests the two-dimensional scene. Two dimensional image objects have been investigated. The intention was to test 2D images by using wavelet transform of real objects.

Medical image De-noising schemes using wavelet transform with fixed form thresholding

Medical Imaging is currently a hot area of biomedical engineers, researchers and medical doctors as it is extensively used in diagnosing of human health and by health care institutes. The imaging equipment is the device, which is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and transmission process. This results in blurry image visible in low contrast. The Image De-noising System (IDs) is used as a tool for removing image noise and preserving important data. Image de-noising is one of the most interesting research areas among researchers of technology-giants and academic institutions. For Criminal Identification Systems (CIS) & Magnetic Resonance Imaging (MRI), IDs is more beneficial in the field of medical imaging. This paper proposes algorithm for de-noising medical images using different types of wavelet transform, such as Haar, Daubechies, Symlets and Biorthogonal. In this paper noise image quality has been evaluated using filter assessment parameters like Variance, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). It has been observed form the numerical results that, the performance of proposed algorithm reduced the mean square error and achieved best value of peak signal to noise ratio (PSNR). In this paper, the wavelet based de-noising algorithm has been investigated on medical images along with threshold.

Dependence of effective screening length in granular columns on bead and silo sizes and their ratio

Apparent mass measurements at the bottom of silos have been carried out. An important parameter in the Janssen model known as the effective screening length has been investigated for different bead and silo diameters as well as of their ratios. It is found that the effective screening length augments with the grain diameter d in addition to the granular column size. It is also revealed that λ exhibits stronger correlation with the bead diameter than that of the silo. This phenomenon is attributed to the reduced-shielding of the vertical stresses to the horizontal ones.

Medical image de-noising schemes using wavelet threshold techniques with various noises

Nowadays the medical image processing has a vital role in diagnosis a human health in most of the health care institutions. Recently intensive research have been done in ultra sound imaging application to remove the blurry of noise image which is affected by random noise during the acquisition, analyzing and transmission. The blurry image produces inaccurate results that are difficult for doctors and biomedical engineers to extract fine physical exam report for patient. The discrete wavelet transform respectively uses the low pass filter and high pass filter to improve the affected image and getting lower and higher frequencies content image. The Discrete Wavelet Transform (DWT) has been applied for ultra sound image as affective tool in order to decompose the original image into details and approximation coefficients. It can be done by passing through it during the filters and reconstruct a sub-band details from the wavelet coefficients without changing the important features of the original image. This experimental work has been applied here to observe the medical image de-noising performance by using wavelet filter parameters such as PSNR & MSE from numerical results for the sake of efficient de-noising of noisy medical image. However, bayes threshold and Poisson noise techniques have been added to original image that produced maximum value of PSNR and minimum value MSE. The wavelet based de-noising algorithm has been investigated for medical image de-noising and best results of bayes technique and Hard & Soft threshold methods were achieved when different noises have been applied such as Poisson noises, Gaussian, Salt & Pepper and speckle. Meanwhile comparison evolution is being performed by taking individual noise values.

A Ferroelectric Ceramic/Polymer Composite‐Based Capacitive Electrode Array for In Vivo Recordings

A new implantable capacitive electrode array for electrocorticography signal recording is developed with ferroelectric ceramic/polymer composite. This ultrathin and electrically safe capacitive electrode array is capable of attaching to the biological tissue conformably. The barium titanate/polyimide (BaTiO3 /PI) nanocomposite with high dielectric constant is successfully synthesized and employed as the ultrathin dielectric layer of the capacitive BaTiO3 /PI electrode array. The performance of the capacitive BaTiO3 /PI electrode array is evaluated by electrical characterization and 3D finite-element modeling. In vivo, neural experiments on the visual cortex of rats show the reliability of the capacitive BaTiO3 /PI electrode array. This work shows the potentials of capacitive BaTiO3 /PI electrode array in the field of brain/computer interfaces.

MWCNTs based flexible and stretchable strain sensors

Carbon nanotubes have potential applications in flexible and stretchable devices due to their remarkable electromechanical properties. Flexible and stretchable strain sensors of multi-walled carbon nanotubes (MWCNTs) with aligned or random structures were fabricated on poly-dimethylsiloxane (PDMS) substrate with different techniques. It was observed that the spraycoatedtechniquebased strain sensor fabricated on PDMS substrate showed higher sensitivity higher stretchability, better linearity and excellent longer time stability than the sensor fabricated with other methods presented in this work. The scanning electron microscopy images indicated the spray coating technique can produce a better uniform and compact CNT network, which is the important role affecting the performance of CNT-based flexible strain sensors.

Highly stretchable, sensitive and flexible strain sensors based on MWCNTs fabriated by different methods

Three samples of strain sensors have been fabricated and presented in this paper with different methods. First sample was made by spray coating of MWCNTs on poly-dimethylsiloxane (PDMS) substrate; second sample was by depositing of 0.3 wt% of MWCNTs powder on PDMS substrate at 100°C and third sample was fabricated by CVD method to grow MWCNTs array on SiO2 substrate. It revealed from experiment results that strain sensor fabricated by spray coating method showed high sensitivity with tunability from 3.25-19.17 %, high stretchability of 60 %, good linearity and excellent long time stability.

Medical Image De-Noising Schemes Using Different Wavelet Threshold Techniques

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.