VO2 thin films synthesized via direct oxidation on piezoelectric GaN/AlGaN/GaN/Si and SiO2/Si substrates have been used to demonstrate free space modulation of near and mid-IR light using a pulsed electric field. Interdigitated metal finger patterns deposited on high-quality 140 nm VO2 thin films on suspended III-nitride or SiO2/Si membranes were used to apply the pulsed electric field and modulate laser beams of wavelengths varying from 1064 to 2600 nm passing through it as it switched between metal and semiconducting phases. Strong wavelength dependence of the intensity modulation is observed with the modulation magnitude varying from 23.4% at 1550 nm to 52.1% at 2600 nm for VO2 film grown on the III-nitride membrane. The VO2 film on SiO2/Si resulted in an intensity modulation of almost half of that on III-nitrides due to higher IR absorption in the SiO2 and Si layers. Infrared microscopic images of the membrane recorded across the phase transition of the VO2 film indicate significant temperature change over only a small fraction of the interdigitated finger pattern, clearly indicating the localized nature of the phase transition enabled by both the electric field and thermal heating. The intensity modulation depth did not change significantly over a frequency range of 10 kHz, which is likely limited by the thermal mass of the structure, so improved design concentrating electric field and reducing thermal mass and conductivity is expected to further improve the frequency response.

Self-polarized energy harvesting materials have seen increasing research interest in recent years owing to their simple fabrication method and versatile application potential. In this study, we systematically investigated self-polarized P(VDF-TrFE)/carbon black (CB) composite thin films synthesized on flexible substrates, with the CB content varying from 0 to 0.6 wt.% in P(VDF-TrFE). The presence of –OH functional groups on carbon black significantly enhances its crystallinity, dipolar orientation, and piezoelectric performance. Multiple characterization techniques were used to investigate the crystalline quality, chemical structure, and morphology of the composite P(VDF-TrFE)/CB films, which indicated no significant changes in these parameters. However, some increase in surface roughness was observed when the CB content increased. With the application of an external force, the piezoelectrically generated voltage was found to systematically increase with higher CB content, reaching a maximum value at 0.6 wt.%, after which the sample exhibited low resistance. The piezoelectric voltage produced by the unpoled 0.6 wt.% CB composite film significantly exceeded the unpoled pure P(VDF-TrFE) film when subjected to the same applied strain. Furthermore, it exhibited exceptional stability in the piezoelectric voltage over time, exceeding the output voltage of the poled pure P(VDF-TrFE) film. Notably, P(VDF_TrFE)/CB composite-based devices can be used in energy harvesting and piezoelectric strain sensing to monitor human motions, which has the potential to positively impact the field of smart wearable devices.

In this work, VO2 based thermal sensing thin film synthesized on flexible muscovite substrates by direct oxidation of deposited vanadium metal, were investigated for the impact of doping and strain on their electrical properties. We investigated both undoped and Ti doped VO2 on muscovite substrate and compared with those on Quartz substrate. Both doped and undoped VO2 were found to undergo phase transition due to effect of heat as well as mechanical strain on muscovite substrate. On the other hand, the Ti doped VO2, on both quartz and muscovite substrate showed significant reduction in the transition temperature compared to the undoped VO2 thin films on these two substrates. When subjected to mechanical strain, the VO2 thin film on muscovite substrates resulted in a decrease or an increase in resistance depending on whether the applied strain was tensile or compressive, respectively. The resistance change was also steeper around the transition temperature compared to room temperature, exhibiting high gauge factor. This metal doped VO2 on flexible muscovite substrate has the significantly low transition temperature which causes the VO2 film to undergo phase transition at a near-room temperature and enables it to be used as a temperature sensor with enhanced sensitivity.

A transparent indium tin oxide (ITO) contact to bulk n-GaN and n-GaN thin film on c-face sapphire with a specific contact resistivity of 8.06 × 10−4 Ω.cm2 and 3.71 × 10−4 Ω.cm2 was measured, respectively. Our studies relied on an RF sputtering system for ITO deposition. We have investigated the formation of the ITO-based contacts on untreated and plasma treated samples. A nonlinear I–V curve was observed for ITO deposited on untreated samples. On the other hand, an I–V curve with linear behavior was observed for plasma-treated samples, indicating the formation of ohmic contacts. From the C-V measurements, it was observed that there was also an increase in the carrier concentration in plasma treated samples compared to untreated samples. This can be attributed to the removal of surface oxide layer present on the GaN surface, and increase in nitrogen vacancies after SiCl4 plasma treatment. In addition, the increase in nitrogen vacancies at the GaN surface can also enhance localized surface/sub-surface carriers, thereby reducing the contact resistance further.

Infrared transmission characteristics of VO2 thin films synthesized on multiple substrates, using a low-pressure direct oxidation technique, have been characterized. Material characterization of these films indicates high material quality, which resulted in large variation of electrical and optical properties at phase transition. A change in optical transmissivity greater than 80% was observed for these films utilizing infrared (IR) laser illumination at 1550 nm. Phase transition enabled by temperature change induced by a pulsed high-power laser beam resulted in modulated IR laser transmission with a low time constant in VO2 on transparent quartz and muscovite substrates. Investigation of the effect of mechanical strain on phase transition in VO2 grown on flexible muscovite substrate indicate shift in transition temperature to higher for tensile and lower for compressive strains.

Polycrystalline VO2 thin films synthesized on two piezoelectric substrates (AT-cut quartz and GaN/AlGaN/GaN/ Si) using low pressure direct oxidation technique have been characterized and compared to VO2 grown on traditional non-piezoelectric substrates sapphire and SiO2/Si. X-ray diffraction and atomic force microscopy characterization performed on the as grown films confirmed high quality of the VO2 films grown on both the piezoelectric and non-piezoelectric substrates. Changes in material properties associated with the semiconductor metal transition (SMT) of the VO2 films were investigated through resistivity and transmitted optical power changes measured across the SMT. It was observed that the VO2 films grown on the piezoelectric substrates are of high quality, and their electrical and optical properties changes are quite comparable to the best reported values on films synthesized on various substrates using different synthesis processes.

Development of compact and fast modulators of infrared light has garnered strong research interests in recent years due to their potential applications in communication, imaging, and sensing. In this study, electric field induced fast modulation near-infrared light caused by phase change in VO2 thin films grown on GaN suspended membranes has been reported. It was observed that metal insulator transition caused by temperature change or application of electric field, using an interdigitated finger geometry, resulted in 7% and 14% reduction in transmitted light intensity at near-infrared wavelengths of 790 and 1550 nm, respectively. Near-infrared light modulation has been demonstrated with voltage pulse widths down to 300 µs at 25 V magnitude. Finite element simulations performed on the suspended membrane modulator indicate a combination of the Joule heating and electric field is responsible for the phase transition.

VO 2 film was incorporated with GaN microcantilevers, and proved to be highly sensitive detector to detect acoustic waves at resonance frequency, but the VO 2 film was not of very high quality. In order to produce high quality VO 2 mesa to be used for microcantilevers, VO 2 thin films were synthesized using low pressure chemical vapor deposition technique (LPCVD) on AlGaN/GaN epitaxial layer on Si (111) substrate, and characterized using X-ray diffraction (XRD) and atomic force microscopy (AFM) to determine their material quality. Electrical and optical imaging methods were also used as characterization methods to determine the changes in properties due to the metal-insulator transition (MIT) of VO 2 . Resistance transition ratio before and after MIT transition was found to be > 1100, which indicates high quality of the material, suitable for a variety of sensing applications. Optical images taken before, during and after transition clearly indicate reflectivity changes caused by the MIT transition.

At present, brain tumor segmentation from 3D MRI data has become much popular in biomedical instrumentation. Various automatic and semi-automatic methods have been developed for this purpose but with huge computational burden due to the enormous volume of 3D data. Therefore, an effective automatic approach for detecting a tentative Region of Interest (ROI), in which the presence of tumor is guaranteed, is highly demanding as it can help to investigate brain tumor with reduced computation time. In this paper, an automatic brain tumor region detection scheme is developed based on the variation of intensity distribution in 3D volumetric tumor and non-tumor region. First, an efficient scheme is developed utilizing the CDF of the intensity distribution which drastically reduces a large volume of non-tumor data. Next, in order to enhance separability between tumor and non-tumor region of the brain, a 3D mean filtering operation is carried out utilizing a spherical window. After that, the roughness on the surface of the mean-filtered volume is reduced by implementation of a smoothing operation on its surface. Next, a voxel-wise analysis is performed. For each voxel, an unsupervised classification is performed whether it is a tumor voxel or non-tumor voxel based on intensity distribution inside the voxel. The non-tumor voxels are discarded and a precise ROI is extracted by taking the surviving voxels which ensures the presence of the whole tumor within that region.

Accurate detection of a brain tumor from 3D MRI images is very important for the physicians to provide proper treatment to the patients diagnosed with fatal diseases. If the detection of the tumor region is done manually, it is a very prolonged task to analyze a single case. Often it is erroneous as well. This can create adverse effect on planning the treatment of the patient. Therefore, in this work, a completely automated method of detection of the brain tumor has been proposed. Human brain size being huge, and often the characteristics of tumor tissues and non-tumor tissues having similarity, it is very difficult and time consuming for a classifier to work with the entire brain data. This paper deals with detecting the brain tissue so accurately that the classifier will require only a very small volume to work on. This method takes out 2D slices of images from the 3D data and then detects the tumor by investigation of the features derived from the histograms of the slices. At first, 2D slices have been taken along the XY plane and the tumorous hemisphere is detected from the intensity histogram of the two hemispheres. A threshold intensity is determined by analyzing the histogram of the detected hemisphere. After applying the threshold, median filtering is performed and a second threshold value is applied if needed. After that, a connectivity checking is performed on the image and the biggest cluster is selected as pixels representing the tumor. Finally, the 2D slices containing the detected tumor are stacked upon and unified together. The proposed method, with Dice Similarity Coefficient Metric of 0.8056, has surpassed many other algorithms.

Segmentation of a region containing the brain tumor from 3D magnetic resonance imaging (MRI) data can help physicians to diagnose accurately the size and malignancy of the tumor. However, manual segmentation is time consuming and involves risk of having inaccurate result. In this paper, an automatic method of segmenting the region of interest (ROI), a region encompassing the brain tumor and its neighborhood, is proposed based on voxel statistics. In the proposed method, first possible candidate selection is performed utilizing intensity characteristics of tumor region in the FLAIR and T1 images of MRI data. Next, a cubic shaped 3D mean filtering operation is applied on the whole volumetric data to obtain filtered volume where some random intensity behavior is expected to be eliminated. Finally, from the resulting 3D FLAIR data, ROI is extracted based on cumulative distribution function of intensity. It is found that the extracted ROI offers significant reduction of the overall MRI volume without losing tumor data. The proposed ROI extraction scheme is tested on 20 real life high grade tumor cases obtained from a widely used database and a very satisfactory performance is obtained in terms of segmentation accuracy, overall volume reduction and computational time.

Sleep apnea is a sleep disorder that affects one's breathing during sleep. A large number of people all over the world are suffering from this disease. Electroencephalogram (EEG) provides electrical activity of the brain signal that enables physicians to diagnose and monitor sleep apnea events. In this paper, an efficient scheme for classifying apnea and non-apnea events of an apnea patient is proposed based on temporal variation of Beta band energy in a frame of EEG data. Unlike conventional approaches, instead of extracting features from the whole frame at a time, a given test frame of EEG signal is divided into overlapping sub-frames and spectral characteristics are extracted from each pre-processed sub-frame. By investigating the spectro-temporal characteristics of all the traditional frequency bands of EEG signal, it is found that the temporal variation of spectral energy in Beta band plays the dominant role in classifying apnea and non-apnea events. Statistical features are extracted from the temporal pattern of Beta band energy and are used in K nearest neighborhood classifier. Extensive experimentation is carried out on several apnea patients with various apnea indices and a very satisfactory apnea detection performance is achieved in comparison to that obtained by some existing methods.