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Analog–digital converters (ADCs) are electronic circuits that convert continuous time signals into digital signals (binary data) for analyzing, estimation, and transmission. Transceivers use ADC in the baseband stage of receiver to convert the analog signals to digital form for further signal processing. At high-frequency applications, like millimeter wave applications, the ADC consumes more power due to the high sampling rate with respect to the increased bandwidth. In this article, a novel dynamic comparator is presented with delay and power analysis for the design. The expression is derived for all operational regions of the transistor. The new design is proposed for low-power consumption and reduced delay of the circuit. The novel design overcomes the drawbacks of the less operating frequency range, high latency, and more power dissipation of existing comparator designs. The proposed low-power dynamic comparator is simulated with 45 nm CMOS technology that provides total power dissipation of 33.92 µW, delay of 19.71 ps, and energy per conversion of 0.19 fJ/conv. with a supply voltage of 1 V. [ABSTRACT FROM AUTHOR]

As an initial block in the receiver front-end, LNA needs to achieve high gain with minimal noise at high frequencies. LNA designs at 5G communication have drawbacks such as increased noise figure, minimum gain, and poor linearity. A CMOS Low-Noise Amplifier for the frequency range of 30 GHz with optimization technique of current reuse technique and linearization technique is proposed and implemented to obtain maximum gain with reduced noise figure with improved linearity in this paper. The linearization technique improves the input of third-order intercept point ( I I P 3 ) of an LNA. The parameters, such as sufficient gain, low noise and enhanced linearity, are considered to design the LNA for wireless 5G communication. The proposed design shows an improvement in gain and lesser noise figure compared to conventional designs. The simulation results show that the proposed LNA provides a maximum gain of 20.6 dB, a noise figure of 3.1 dB and I I P 3 of 6 dB at 30 GHz with a power consumption of 6.2 mW from a supply voltage of 1.2 V. The proposed Low-Noise Amplifier is designed and simulated in the 45 nm CMOS technology. [ABSTRACT FROM AUTHOR]

As the fabrication technology goes beyond nano-scale, the VLSI layout design using Complementary Metal Oxide Semiconductor (CMOS) becomes obsolete due to its short channel effects and leakage currents. Quantum-dot Cellular Automata (QCA) is a novel paradigm proposed to overcome the drawbacks of CMOS circuits at nano-scale. Complex arithmetical operations need accurate and fast computing architectures. Though performing arithmetical operations on fixed-point numbers is easy, however, floating-point numbers have significant advantages. Moreover, the operations on floating-point numbers decide the speed and accuracy of the arithmetic unit. Hence, it is crucial to design a precise computing architecture for floating-point numbers. This paper presents for the first time an optimized architecture using QCA technology for the addition of single-precision floating-point numbers. The proposed model has been designed with lesser number of blocks which have been proved to be the best among the existing models. The proposed floating-point adder is simulated using QCADesigner tool that requires 10,370 quantum cells in an area of 21.25 μm2 with a delay of 15 clock cycles. The energy dissipation analysis of proposed architecture is studied using QCA Designer-E tool based on which the power dissipation is also calculated. The proposed model has a power dissipation of 6.56 nW, which is an improvement of 98% compared to present CMOS technology. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease is a widespread, evitable and remediable ailment accentuated by the deregulation of a stream of air in the lungs or due to pleura anomalies owing to harmful fuels. Timely detection and prevention are of utmost importance to curb the spread of these disorders. To diagnose the respiratory illness, clinicians use the traditional approach of auscultation and this led to the development of state-of-the-art technology tools for sensing the morbidities. In this pursuit, in this work, a novel deep-learning structure is framed for better classification of lung sounds with the amalgamation of features extracted using the Empirical mode decomposition technique and improved network models. In this paper, a two-stage approach is proposed to classify the acoustic files from the ICBHI benchmark dataset. At the first stage, intrinsic mode function (IMF) feature vectors are extracted from lung sounds and the best combination of IMF features to classify respiratory disorders is found. In the next stage, Gammatone filters are applied on the best combination IMF features and Gammatone cepstral coefficients (GTCC) are computed. The GTCC are input to the deep-learning model, Recurrent Neural Network-based stacked BiLSTM classifier for classification. It is observed that the IMF 3 has more meaningful information and enhances the performance in conjunction with GTCCs compared to other IMFs and MFCC. Moreover, the results demonstrate that the proposed GTCC of the third IMF component applied to the stacked BiLSTM framework excels the competing Convolutional Neural Network method of classification in terms of accuracy, specificity and sensitivity. [ABSTRACT FROM AUTHOR]

Comparator is an essential building block in many digital circuits such as biometric authentication, data sorting, and exponents comparison in floating-point architectures among others. Quantum-dot Cellular Automata (QCA) is a latest nanotechnology that overcomes the drawbacks of Complementary Metal Oxide Semiconductor (CMOS) technology. In this paper, novel area optimized 2n-bit comparator architecture is proposed. To achieve the objective, 1-bit stack-type and 4-bit tree-based stack-type (TB-ST) comparators are proposed using QCA. Then, two tree-based architectures of 4-bit comparators are arranged in two layers to optimize the number of quantum cells and area of an 8-bit comparator. Thus, this design can be extended to any 2n-bit comparator. Simulation results of 4-bit and 8-bit comparators using QCADesigner 2.0.3 show that there is a significant improvement in the number of quantum cells and area occupancy. The proposed TB-ST 8-bit comparator uses 2.5 clock cycles and 622 quantum cells with area occupancy of 0.49 µm2 which is an improvement by 10.5% and 38%, respectively, compared to existing designs. Scaling it to a 32-bit comparator, the proposed architecture requires only 2675 quantum cells in an area of 2.05 µm2 with a delay of 3.5 clock cycles, indicating 9.35% and 28.8% improvements, respectively, demonstrating the merit of the proposed architecture. Besides, energy dissipation analysis of the proposed TB-ST 8-bit comparator is simulated on QCADesigner-E tool, indicating average energy dissipation reduction of 17.3% compared to existing works. [ABSTRACT FROM AUTHOR]

The distinctive characteristics of Underwater Acoustic Sensor network, makes it highly susceptible to attacks. Effective and skilful interaction between the sensors and Autonomous Underwater Vehicles is required to actuate the communication in the network. In the existence of malicious nodes, this leads to critical security issues especially in pressure based routing protocols. Thus, an effective security scheme is indispensable for efficient data transmission. In this paper, the prime aim is to detect the virulent nodes in pressure routing by formulating Collaborative Entrapping Scheme (CES). It is observed that incorporating CES in the pressure based routing protocol effectively detects and prevents the virulent nodes from participating in the routing process, but introduces an overhead. To decrease the overhead due to the CES approach, in this paper, a fraction of data packet (Dfrac) only is used to indicate the successful reception which reduces the end to end delay. Simulation results indicate that the proposed Secure Pressure based Routing protocol shows performance improvement compared to the existing Depth Based and Hydro cast Routing protocols even in the presence of multiple malicious nodes. [ABSTRACT FROM AUTHOR]

Floating point digital signal processing technology has become the primary method for real time signal processing in most digital systems presently. However, the challenges in the implementation of floating point arithmetic on FPGA are that, the hardware modules are larger, have longer latency and high power consumption. In this work, a novel efficient reversible floating point fused arithmetic unit architecture is proposed confirming to IEEE 754 standard. By utilizing reversible logic circuits and implementation with adiabatic logic, power efficiency is achieved. The hardware complexity is reduced by employing fused elements and latency is improved by decomposing the operands in the realization of floating point multiplier and square root. To validate the design, the proposed unit was used for realization of FFT and FIR filter which are important applications of a DSP processor. As detection is one of the core baseband processing operations in digital communication receivers and the detection speed determines the data rates that can be achieved, the proposed unit has been used to implement the detection function. Simulation results and comparative studies with existing works demonstrate that the proposed unit efficiently utilizes the number of gates, has reduced quantum cost and produced less garbage outputs with low latency, thereby making the design a computational and power efficient one. [ABSTRACT FROM AUTHOR]

In this work, a reversible single precision floating-point square root is proposed using modified non-restoring algorithm. To our knowledge, this is the first work proposed for floating-point square root using reversible logic. The main block involved in the implementation of reversible square root using modified non-restoring technique is Reversible Controlled-Subtract-Multiplex. Further, optimized Reversible Controlled-Subtract-Multiplex blocks are introduced in order to minimize the number of reversible gates used, number of constant inputs used, number of garbage outputs produced as well as the quantum cost. The proposed reversible single precision floating-point square root is realized using an 8-bit reversible adder, an 8-bit and a 25-bit reversible shift register, 12-bit reversible unsigned square root, 6-bit reversible unsigned square root, 4-bit reversible unsigned square root, 3-bit reversible unsigned square root and ten 1-bit reversible unsigned square root units. [ABSTRACT FROM AUTHOR]

Underwater Acoustic networks comprise sensor nodes distributed randomly in a subsea environment. These nodes deployed at varying pressure levels forward the monitored information through multi-hop transmissions. Data transmission through acoustic links exhibits challenges like frequency-dependent attenuation, noise, multipath distortion, and low propagating speed of acoustic signal (about 1500 m/s) causing the Doppler effect. Various existing algorithms consider only the energy and pressure levels in the routing process. The consideration of channel quality in routing is necessary to enhance the overall network performance. Hence, it is envisaged that the drawbacks of meta-heuristic techniques in computing the optimal solution can be solved by constructing a hybrid optimization algorithm with the fitness function based on pressure levels, energy, and channel conditions. Hybrid-SSO integrates Sparrow Search Optimization with Grey Wolf Algorithm to detect optimal relay nodes for data transmission towards the destination node. Implementation of the proposed algorithm is done using Aquasim (NS2.30) and its performance is collated with the existing techniques. Simulation results show that the proposed Hybrid-SSO successfully reaches a maximum Packet Delivery Ratio of 99.2% for 180 nodes with a transmission range of 100 m while reducing the Average End to End Delay, Average Energy Consumption, and No. of Dead nodes. • Proposes a Hybrid Sparrow Search Optimization technique. • Implementation of the proposed algorithm is done using Aquasim (NS2.30). • Simulation results show that the proposed Hybrid-SSO. • Packet Delivery Ratio of 99.2% for 180 nodes. [ABSTRACT FROM AUTHOR]

Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, a despeckling algorithm is proposed based on non-subsampled contourlet transform. This transform has the property of high directionality, anisotropy and translation invariance, which can be controlled by non-subsampled filter banks. This study aims to denoise the speckle noise in ultrasound images using adaptive binary morphological operations, in order to preserve edges, contours and textures. In morphological operations, structural element plays an important role for image enhancement. In this work, different shapes of structural element have been analysed and filtering parameters have been changed adaptively depending on the nature of the image and the amount of noise in the image. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method is able to preserve edges and image structural details compared with existing methods. [ABSTRACT FROM AUTHOR]

Existing Extended Progressive Visual Cryptography scheme (EPVCS) suffers from the problem of pixel expansion, poor quality of reconstructed image and residual trace of cover images in the reconstructed image. Hence in this paper, Two in One Image secret sharing scheme for EPVCS is proposed which decodes the encrypted image in two stages. The proposed scheme provides k , n threshold construction using meaningful shares without pixel expansion and demonstrates that the reconstructed image has improved quality compared to the existing schemes. To reduce the computational complexity, the proposed scheme uses simple Modular Arithmetic operations instead of Galois field. The proposed scheme has the additional advantages of supporting any value of k and n , no overhead in resizing the secret image and no residual trace of cover image. Simulation results and performance analysis show the effectiveness of proposed scheme with improved contrast, 99% Structural Similarity of the reconstructed image and good progressive reconstruction. [ABSTRACT FROM AUTHOR]

Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. In this paper, a pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders. The proposed approach models the segmented respiratory sound signal into Bump and Morse scalograms from several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) method. From the extracted intrinsic mode functions, the percentage energy calculated for each wavelet coefficient in the form of scalograms are computed. Subsequently, these scalograms are given as input to the pre-trained optimized CNN model for training and testing. Stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms were examined to check the prediction accuracy on the dataset comprising of four classes of lung sounds, normal, crackles (coarse and fine), wheezes (monophonic & polyphonic) and low-pitched wheezes (Rhonchi). On comparison to the baseline method of standard Bump and Morse wavelet transform approach which produced 79.04 % and 81.27 % validation accuracy, an improved accuracy of 83.78 % is achieved by the virtue of scalogram representation of various IMFs of EMD. Hence, the proposed approach achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature. [ABSTRACT FROM AUTHOR]

Visual Cryptography Scheme (VCS) is a cryptographic technique for protecting secret images. The advantage of using VCS is that decoding can be done without use of any computations. Nevertheless, the reconstructed image has poor visual quality. Therefore, Two in One Image Secret Sharing Scheme (TiOISSS) was proposed which takes the advantage of VCS and provides good quality decoded images. However, the existing TiOISSS has security limitations as it is implemented only for noisy shadows. Hence, in this paper, modified TiOISSS is proposed and implemented for meaningful shadows. To enhance the security of the shares and prevent fake shares that may be introduced by hackers, an authentication image is shared along with the secret image. The quality of the reconstructed image is improved by using adaptive halftoning technique. Experimental results demonstrate the improved security and quality by the proposed scheme. [ABSTRACT FROM AUTHOR]