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In every modern ICs the adders are essential components. Adder’s performance has a substantial impact on the architecture of signal processing, controller, the module of filter, the module of data storage, etc., high-speed and area-efficient circuits are the most substantial parameters in every modern integrated circuit. Carry select adder operates at high speed, but it consumes more power due to the large area. The present approach discloses different VLSI hybrid carry select adder architectures. The hybrid technology-based Carry Select adder (CSELA) consists of two stages, namely the Hancarlson adder stage and Hybrid Stage is proposed. In this technique, all the stages (4 bits in each stage) are performed simultaneously to improve the speed and area further. The propagation delays of the proposed adder are the summation of two full adders, seven Multiplexers (4:1) and BEC(3 bit) for producing Cout. The proposed work indicates that the hybrid carry select adder operates at high a speed with a lesser area than the conventional adder. The proposed design is simulated and synthesized in Xilinx ISE 12.1 using Verilog HDL with a family of Vertex6 FPGA devices (Device No. XC6VLX75T, Package FF484, Speed -3). The synthesized report shows that the speed of the proposed adder is improved by 49.06%, 52.61%, 47.58%, 19.08%, 39.9%, 1.25%, 44.43%,19.08%, 44.07% and 71.59% compared to RCA, CBL-based CSELA, CLA, Weinberger BEC-based CSELA,D latched CSELA, Brent Kung CSELA, Brent Kung RCA-based CSELA, CSA Weinberger, Conventional CSELA and Ling CSELA, respectively.

The major role of electronic devices is providing low power dissipation and high speed with compact area. The speed of electronic devices depends on arithmetic operations. Multiplication is the important arithmetic operation in many VLSI signal processing applications. Hence, a high-speed multiplier is needed to design any signal processing module. Many multipliers are surveyed in the literature. They are Array, Wallace tree, booth, Vedic and Compressor-based multiplier. The speed of these multipliers depends on partial product accumulation. The hybrid parallel adder-based multiplier is proposed to improve the speed of multiplication compared to the existing technique. In this technique the partial products of, two consecutive bits (multiplicands), are added simultaneously with the help of a hybrid adder (Hancarlson, Weinberger and Ling adder). The proposed architecture is synthesized and simulated using Xilinx ISE 12.1 with various FPGA boards. Synthesized report shows that the speed of proposed multiplier (Spartan 6 FPGA implementation) is improved when compared to an Array multiplier (22.14%), Wallace tree multiplier (20.41%), Multiplier using compressor (13.89%), Vedic Multiplier using CLA (13.03%), Vedic Multiplier using RCA (3.54%), Modified Booth multiplier (4.42%) and Vedic Multiplier using HCA with BEC (3.28%).

Electroencephalography (EEG) is a typical tool utilized for the discovery of epileptic seizures. Notwithstanding, long-term EEG, visual investigation of records is described by its subjectivity, time-consuming procedure, and misdiagnosis. Different types of seizure detection algorithms are used to solve the problems but these methods have a high false classification ratio. Therefore this work introduces a novel automatic seizure detection mechanism to reduce the false classification ratio. The proposed strategies suggest that users choose the appropriate one for a given classification problem. The wavelet transform is based on the principle of describing the temporal evolution of a signal across different time scales by providing information about the local regularity. Multi-Model Features must be extracted from individual sources of data by constructing a signal that is appropriate for the type of data. The extraction of features from one source is independent of the extraction of features from an ECG Signal. Therefore, the proposed method for detecting automatic epilepsy identification may be considered a significant option compared to the traditional strategies, which can reduce the overload of visual analysis and accelerate the detection of seizures.

ABSTRACTIn this paper, a new speech enhancement approach using the modified Tunable-Q Wavelet Transform (TQWT) for Tamil speaker recognition systems is proposed. Different forms of the existing wavelet transforms like continuous wavelet transform and discrete wavelet transform provide little ability to tune the Q factor and redundancy of the wavelet and have less reconstruction property. TQWT is fully discrete, has perfect reconstruction property and is modestly overcomplete. The Q-factor and redundancy can be easily tuned. Speech signals vary in both time and space. The entire speech signal is divided into different parts. Depending on the oscillatory contents of the speech signal, different Q factors are applied for each part instead of applying a single Q factor for the entire data. This gives more accuracy in decomposition. The proposed method is evaluated on several speakers and under various noise conditions including babble, street and exhibition noises. In order to tune the quality factor (Q) of the TQWT and to obtain good reconstruction property, speech enhancement has been conducted with varying values of Q. The reconstruction error, cost function, Signal to Noise Ratio(SNR) and Mean Opinion Score(MOS) show that the new approach highly improves the performance of speech enhancement based on the modified TQWT. Hardware implementation is also done using TMS320C6713 DSK and the results are similar to simulation.

Agriculture is the backbone of Indian economy. Most of the people living in rural areas depend on agriculture for their livelihood. Nevertheless, the farmers are facing a lot of difficulties in crop production due to climatic change. In addition, diseases in plants affect the production of crops drastically. Presently, the farmers identify the plant diseases by visual inspection which, in turn, requires an expert's help and it is a time consuming task. Hence, in this paper, deep learning networks are used to identify different types of diseases in the leaves of different plants. The model is trained with 45,562 images and validated with 8,049 images belonging to 17 categories of diseases. It is fine-tuned with the hyperparameters such as learning rate, epochs, batch size and input image size and then tested with 9,469 images which yield a total classification accuracy of 96.8%.

The clusters of historical documents are of great importance in terms of cultural and scientific. In order to access the documents, originality should be maintained. So conversion of digital form is highly required for recognition. While converting, those documents may be due to poor quality, overlapping of characters, complex background and so on. In this paper, an efficient system for recognizing English characters from degraded historical document images is proposed. Initially, modified Adaptive Thresholding based binarization process is performed to eliminate the noise content in the input image. The characters are segmented through the rectangular bounding box method. Then Local binary pattern (LBP) algorithm is enforced to extricate the features of each characters. Finally, Spatial Pyramid Matching (SPM) classifier is used for texture classification. HDLA 2011 dataset is employed to validate the proposed method. The proposed method achieves 94.6% recognition accuracy and 0.34 s computation time for Lucida Black-letter font. This method also outperforms better than the existing recognition techniques.

Epigallocatechin-3-gallate (EGCG), a plant polyphenol catechin is potent chondrogenic drug which enhances cartilage regeneration. Hence, sustained release of EGCG from interlinked porous polycaprolactone (PCL) scaffolds can be beneficial in engineering a cartilage construct. In this study, 10% (w/v) and 12% (w/v) PCL scaffold were fabricated by incorporation of 2 different concentrations (1 mg/ml & 2 mg/ml) of EGCG loaded albumin nanoparticles (ENP). Morphological analysis of the fabricated scaffolds with Field emission scanning electron microscope (FESEM) revealed interlinked porous structures (pore size range 50 μm–300 μm) with embedded ENP on the scaffolds surface. In addition, the ENP synthesized by desolvation technique were spherical in shape with diameters in the range of 110 nm–210 nm. Further, X-ray diffraction of ENP and scaffolds implied amorphization of EGCG which is highly preferred in drug delivery application. Moreover, spectroscopic analysis with Fourier Transform Infrared (FTIR) and Raman spectrophotometry confirmed incorporation of ENP into scaffolds and chemical stability of PCL and EGCG during fabrication. Importantly, all groups showed sustained release for 22 days and non fickian diffusion through scaffolds. The drug release data also inferred that the concentration of ENP modulated EGCG release from scaffolds. Furthermore, in vitrochondrogenic study with Human chondrocytes for 21 days revealed significant increase in glycosaminoglycan deposition under the influence of EGCG. All these results suggest that ENP loaded polycaprolactone scaffolds, with intrinsic ability for releasing EGCG in a sustained fashion can serve as a prospective matrix in cartilage engineering.