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Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

On pure and metal, non-metal, co-doped rutile TiO2, DFT simula- tions are performed. For the stability study of doped materials, the defect formation energies of non-metal (S), metal (Fe), and metal and non-metal (Fe/S) co-doped materials are determined. A Ti- rich environment is preferable over an O-rich environment. With values of 2.98 eV, 2.18 eV, 1.58 eV, and 1.40 eV, the bandgap for pristine, S-doped, Fe-doped, and Fe/S co-doped materials is found to be direct. The effective masses (m*) and ratios (R) of charge carriers are also examined, and it is discovered that Fe/S co-doped material has the lowest charge carrier recombination rate. The maximum static dielectric constant is found in the Fe/S co-doped material. Doped material’s absorption spectra shifted into the vis- ible region. Additionally, using SCAPS-1D simulation software, a complete solar cell device study using these materials as ETL is performed for the first time. The absorber layer and the ETL settings have been tweaked to perfection. Current-voltage (IV) characteristics, quantum efficiency (QE), capacitance-voltage (CV) characteristics, and capacitance-frequency (Cf) character- istics are provided for optimize solar cells.When the smallest degree of defect for each layer is taken into account, the solar cell with Fe/S co-doped ETL has the highest efficiency of 34.27%.

A split gated silicon nanotube field-effect transistor (FET) biosensor has been proposed for the label free detection of the biomolecules for the first time in literature. The sensitivity of the sensing device has been analysed considering the on current (I ON) and the threshold voltage (V th) variation. Sub-threshold regime has been considered here to detect the charged/neutral biomolecules. Extensive simulations have been done using the SILVACO ATLAS. Sensitivity analysis has been carried out by considering half-filled and full-filled nanogaps with the neutral or charged biomolecules inside the cavity. Significant sensitivity and excellent reduction in short-channel effects has been observed in proposed biosensor.

Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the proposed technique. The Mean Structural SIMilarity (MSSIM) index is used to measure the quality of the segmented images. The results of the proposed technique are compared with the original MFO, PSO, BFO, and WOA. Experimental results and analysis suggest that the proposed technique outperforms other techniques in terms of segmentation quality images and stability. Moreover, computation time required for multilevel thresholding is also reduced to a manageable level.

For computational arithmetic, a full adder is the primary logic units in VLSI applications. A new full adder circuit design has been presented in this article which is based on input switching activity pattern and gate diffusion input (GDI) technique. The adder has been designed in two stages. The first stage is an XOR–XNOR module, whereas, the final stage is for the required outputs. By using the switching activity pattern of inputs and GDI techniques at each stage, the switching activities of the transistors have been minimized. This improves delay, power consumption and computational complexity. The adder has been designed and evaluated by using the synopsis tool and compared with different existing adder cells found in the literature. It is found that the presented adder shows an improvement at least 72.86% and 66.67% in terms of speed and energy consumption, respectively. Extensive performance analyses of the full adder have also been evaluated at 32 nm CMOS and 32 nm CNFET technology node which shows promising performances in both the technology nodes.

In this paper, a novel method is proposed for the first time to achieve multiple threshold voltage (VT) for Silicon Nanotube FET. Using TCAD simulator, it is shown that threshold voltage can be varied by tuning the halo doping and tube diameter of Silicon Nanotube FET. The typical method to attain multiple threshold voltages is to select the pertinent gate work-function for individual devices. But this is not practically feasible due to the difficult process complexity. In this work, the tuning of halo doping and nanotube diameter can be elected to provide three possible values of VT at 14-nm technology node. Furthermore, short-channel effect like Drain Induced Barrier Lowering (DIBL) is also found to be reduced with HALO doped region at source side. It is also observed that the threshold voltage decreases while increasing the tube diameter. Moreover, HALO doping at drain side is found showing a significant improvement in analog/RF performances of the device which eventually makes the device more suitable for radio-frequency integrated circuit applications.

Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

Background: The Adder is one of the most prominent building blocks in VLSI circuits and systems. Performance of such systems depends mostly on the performance of the adder cell. The scaling down of devices has been the driving force in technological advances. However, in CMOS technology performance of adder cell decreases as technology node scaled down to deep micron regime. Objective: With the growth of research, new device model has been proposed based on carbon nano tube field effect transistor (CNFET). Therefore, there is a need of full adder cell, which performs sufficiently well in CNFET as well as different CMOS technology nodes. Method: A new low power full adder cell has been proposed with a hybrid XOR/XNOR module by using CNFET, which is also compatible for the CMOS technology nodes. The performance of the adder cell is validated with HSPICE simulation in terms of power, delay and power delay product. It is observed that the proposed adder cell performs better than the CMOS, CPL, TGA, 10 T, 14 T, 24 T, HSPC and Hybrid_FA adder cells. The CNFET full adder is designed in 32 nm CNFET model and to appraise its compatibility with Bulk-Si CMOS technology, 90 nm and 32 nm CMOS technology node is used. Conclusion: The proposed adder is very much suitable for both CMOS and CNFET technology based circuits and systems. To validate the result, simulation has been carried out with Synopsis tool. This full adder will definitely dominate other full adder cells at various technology nodes for VLSI applications.

In this manuscript, the impact of dielectric pocket on analog/radio-frequency (RF) performances of SOI-TFET is investigated. The inclusion of a dielectric pocket to SOI-TFET has been found to have ...

This paper describes the classification of four different varieties of rice grain based on four sets of features, namely morphology, colour, texture and wavelet. The classification is carried out on single rice kernel using image pre-processing steps followed by a cascade network classifier. The performance of the classifiers based on the above feature sets is also compared. It is found that morphological feature is more suitable for the classification of rice kernels, as compared to other features. The number of input features is reduced by a feature selection process using statistical analysis system (SAS) software. The classification accuracy based on selected features is compared with that of original features using different classifiers. It is found that the selected features are able to provide classification accuracy very close to the original features. The performance of the proposed cascade classifier is also tested against standard datasets from the University of California, Irvine (UCI), and the results are compared with other classifiers. The results show that the proposed classifier provides better classification accuracy as compared to other classifiers.

A new tubular field effect transistor (FET) device named silicon nanotube tunnel field effect transistor (Si-NTTFET) has been proposed which is emerged out of structural engineering and the gate dielectric engineering. The proposed structure offers better immunity towards short channel effects (SCEs) because of the combined effect of minimal doping at the drain side and control of channel region due to the double gate. The tunnelling probability is also improved due to narrow energy band variation. The high k dielectric material such as HfO 2 enhances the ON current by a factor of 3 and 14 as compared to Si 3 N 4 and SiO 2 gate dielectric, respectively.

A new multilevel thresholding based image segmentation technique is developed which utilizes Masi entropy as an objective function. Thresholding is an important image segmentation technique. It may be divided into two types such as bi-level and multilevel thresholding. Bi-level thresholding uses a single threshold to classify an image into two classes: object and the background. For an image containing a single object in a distinct background, bi-level thresholding can be successfully used for segmentation. But in case of complex images containing multiple objects, bi-level thresholding often fails to give satisfactory segmentation. In such cases, multilevel thresholding is generally preferred over bi-level thresholding. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are generally used to optimize the threshold searching process to reduce the computational complexity involved in multilevel thresholding. In this paper, Particle Swarm Optimization (PSO) along with Masi entropy is proposed for multilevel thresholding based image segmentation. The proposed technique is evaluated using a set of standard test images. The proposed technique is compared with the recently proposed Dragonfly Algorithm (DA) based technique that uses Kapur’s entropy as objective function. The proposed technique is also compared with PSO based technique that uses minimum cross entropy (MCE) as objective function. The quality of the segmented images is measured using Mean Structural SIMilarity (MSSIM) index and Peak Signal-to-Noise Ratio (PSNR). The experimental results suggest that the proposed technique outperforms Kapur’s entropy and gives very competitive result when compared with the MCE based technique. Further, computational complexity of multilevel thresholding is also greatly reduced.

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

This paper presents an analysis on electronic, mechanical and optical properties of cubic titanium dioxide using Orthogonalized Linear Combinations of Atomic Orbitals (OLCAO) basis set under the framework of Density Functional Theory. The structural property, namely lattice constant ‘a’, and the electronic properties such as, the band diagram, density of states (DOS) have been studied and analyzed. Whereas, the mechanical properties like, bulk moduli, Shear moduli, Young’s Moduli, poison’s ratio have also been investigated thoroughly. Moreover, optical properties such as refractive index, extinction co- efficient, reflectivity, absorption coefficient have been studied and analyzed thoroughly. The results are compared with previous theoretical and experimental results. It is found that, DFT based simulation produces results which are approximation to experimental results, whereas, the calculated values of elastic constants are better than the previous theoretical and experimental values.

The impact of high- k dielectric pocket (DP) on the ambipolar conduction of tunnel field-effect transistors (TFETs) is demonstrated using two-dimensional Technology Computer Aided Design (TCAD) simulations. In the proposed structure of TFETs, an optimised portion of the upper drain region is replaced with a high- k DP at the channel-drain interface. It is demonstrated that due to the enhancement of the depleted drain region under DP, the minimum tunnelling width at channel-drain interface increases, and attains a maximum value for an optimum length and thickness of DP. Eventually, this increment in the minimum tunnelling width leads to a significant reduction in ambipolar conduction in TFETs. Furthermore, it is shown that performance parameters including the ON-state current, subthreshold swing and output characteristics are not affected by the presence of the proposed DP. Even, the gate-to-drain capacitance is reduced with the inclusion of DP at the channel-drain interface, thus leading to an improved cut-off frequency of TFETs. Moreover, it is also demonstrated that only a 10 nm of gate-on-drain overlapping along with this DP is capable of eliminating the ambipolarity completely for even a higher gate voltage of -0.8 V.

Enhancing solar cell efficiency is one of the most challenging and demanding topics for PV researchers. An ab initio study is done here on the structure of crystal, formation energies, electronic structure, the density of states, effective mass, and optical properties of pure and S-doped, Fe-doped, and Fe-/S-doped anatase TiO2. Co-doping causes a significant reduction in the bandgap, suppresses the charge carrier’s recombination rate, and shifted absorption spectra from UV to the visible region. Hence, co-doped anatase TiO2 with Fe and S is more favorable as a buffer layer of the solar cell compared to S-doped and Fe-doped TiO2. The forecasted values of all quantities will benefit researchers to examine these materials for further photovoltaic applications.

The compressor is a useful element that is widely used in VLSI circuits and systems. It is generally used as a processing element. In this work, a 4-2 compressor has been designed with XOR-XNOR module and multiplexer module. The XOR-XNOR module is consists of six transistors and the multiplexer is consists of transmission gate. The compressor has been designed and simulated in Synopsys tool by using 90 nm CMOS technology. Average power dissipation, worst case delay and power delay product are computed. The same has been observed by varying the supply voltage. A study of the performance comparison has been carried out with existing compressors. It is found that the proposed compressor has low power consumption and the best PDP.

This paper presents a study and analysis applicable to structural, electronic, optical and mechanical properties of one of rare variant of Titanium dioxide (TiO 2 ) i.e. brookite using self-consistent Orthogonalized Linear Combination of Atomic Orbitals method (OLCAO) under the framework of Density Functional Theory (DFT). Structural, electronic and mechanical properties are investigated using Generalized Gradient Approximation (GGA) with Perdew-Burke-Ernzerhof (PBE), Perdew-Burke-Ernzerhof solid (PBES), Becke-Perdew86 (BP86), Perdew Wang91(PW91) and Becke88-Perdew Wang91 Correlation(BPW91) as exchange-correlation. Correlation of electronic and optical properties are performed using GGA-PBES and Meta-gga(MGGA)-Tran and Blaha(TB09). The observed data are match up with the previously reported computational as well as experimental data. Obtained lattice parameters using GGA-PBES, Bond length between Ti and O using PBE and BPW91, bandgap value using MGGA-TB09 and bulk modulus using PW91 and BPW91 matches very well with the experimental values. Comparision using GGA-PBES and MGGA- (TBO9) shows that calculated dielectric constant and refractive index as obtained using GGA-PBES are higher than MGGA approach and optical absorption for brookite TiO 2 occurs in UV region while absorption spectrum using MGGA shifts the wavelength towards the lower energy band of EM spectrum.

To remove simultaneously the ambipolar conduction and enhance HF performances, we propose a promising configuration of DG-TFET with asymmetric gate–drain overlap (ASGDO DG-TFET) in which only back gate is overlapped with drain region. This proposed structure of DG-TFET removes the trade-off between ambipolarity and HF performances by taking the merit of gate–drain overlap in terms of reduction in ambipolarity and suppressing its demerit with reduced gate–drain parasitic capacitance. Using 2-D simulation, it is observed that ambipolar conduction can be suppressed to a large extent in DG-TFET with only 20 nm of back gate–drain overlap, thus not limiting the scaling of drain region compared to symmetric gate–drain overlap DG-TFET (SGDO DG-TFET). Due to the presence of enhanced depletion layer in the drain region caused by a large vertical electrical field, tunneling width at drain–channel interface is found to be maximum in the proposed device, which eventually prevents the charge carriers to tunnel. Furthermore, ASGDO improves the HF performance parameters such as cutoff frequency and gain–bandwidth product compared to SGDO due to reduction in gate–drain parasitic capacitance, and this improvement is found to be consistent while scaling down the channel length.