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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]

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]