Unified Systolic-Like Architecture for DCT and DST Using Distributed Arithmetic

A common computing-core representation of the discrete cosine transform and discrete sine transform is derived and a reduced-complexity algorithm is developed for computation of the proposed computing-core. A parallel architecture based on the principle of distributed arithmetic is designed further for the computation of these transforms using the common-core algorithm. The proposed scheme not only leads to a systolic-like regular and modular hardware for computing these transforms, but also offers significant improvement in area-time efficiency over the existing structures. The structure proposed here is devoid of complicated input/output mapping and does not involve any complex control. Unlike the convolution-based structures, it does not restrict the transform length to be a prime or multiple of prime and can be utilized as a reusable core for cost-effective, memory-efficient, high-throughput implementation of either of these transforms     

Fast computation of multidimensional discrete Hartley transform

Efficient algorithms for the fast computation of 2D and 3D discrete Hartley transforms have been proposed. It is shown that the proposed algorithms offer a significant saving in computation over the existing methods for various array sizes.<>     

Synthesis of Biodiesel from Canola Oil Using Heterogeneous Base Catalyst

A series of alkali metal (Li, Na, K) promoted alkali earth oxides (CaO, BaO, MgO), as well as K₂CO₃ supported on alumina (Al₂O₃), were prepared and used as catalysts for transesterification of canola oil with methanol. Four catalysts such as K₂CO₃/Al₂O₃ and alkali metal (Li, Na, K) promoted BaO were effective for transesterification with >85 wt% of methyl esters. ICP-MS analysis revealed that leaching of barium in ester phase was too high (~1,000 ppm) when BaO based catalysts were used. As barium is highly toxic, these catalysts were not used further for transesterification of canola oil. Optimization of reaction conditions such as molar ratio of alcohol to oil (6:1–12:1), reaction temperature (40–60 °C) and catalyst loading (1–3 wt%) was performed for most efficient and environmentally friendly K₂CO₃/Al₂O₃ catalyst to maximize ester yield using response surface methodology (RSM). The RSM suggested that a molar ratio of alcohol to oil 11.48:1, a reaction temperature of 60 °C, and catalyst loading 3.16 wt% were optimum for the production of ester from canola oil. The predicted value of ester yield was 96.3 wt% in 2 h, which was in agreement with the experimental results within 1.28%.     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

Efficient bit-parallel multipliers over finite fields GF(2)

Hardware implementation of multiplication in finite field GF(2^m) based on sparse polynomials is found to be advantageous in terms of space-complexity as well as the time-complexity. In this paper, we present a new permutation method to construct the irreducible like-trinomials of the form (x + 1)^m + (x + 1)ⁿ + 1 for the implementation of efficient bit-parallel multipliers. For implementing the multiplications based on such polynomials, we have defined a like-polynomial basis (LPB) as an alternative to the original polynomial basis of GF(2^m). We have shown further that the modular arithmetic for the binary field based on like-trinomials is equivalent to the arithmetic for the field based on trinomials. In order to design multipliers for composite fields, we have found another permutation polynomial to convert irreducible polynomials into like-trinomials of the forms (x² + x + 1)^m + (x² + x + 1)ⁿ + 1, (x² + x)^m + (x² + x)ⁿ + 1 and (x⁴ + x + 1)^m + (x⁴ + x + 1)ⁿ + 1. The proposed bit-parallel multiplier over GF(2^4m) is found to offer a saving of about 33% multiplications and 42.8% additions over the corresponding existing architectures.     

Fault-Tolerant Bit-Parallel Multiplier for Polynomial Basis of GF（2^m）

Novel fault-tolerant architectures for bit-parallel polynomial basis multiplier over GF（2^m）, which can correct the erroneous outputs using linear code, are presented. A parity prediction circuit based on the code generator polynomial that leads lower space overhead has been designed. For bit-parallel architectures, the Moreover, there is incorporation of space overhead only marginal time error-correction is about 11%. overhead due to capability that amounts to 3.5% in case of the bit-parallel multiplier. Unlike the existing concurrent error correction （CEC） multipliers or triple modular redundancy （TMR） techniques for single error correction, the proposed architectures have multiple error-correcting capabilities.     

High-Throughput Memory-Based Architecture for DHT Using a New Convolutional Formulation

A new formulation is presented for the computation of an -point discrete Hartley transform (DHT) from two pairs of [(N/2-1)/2]-point cyclic convolutions, and further used to obtain modular structures consisting of simple and regular memory-based systolic arrays for concurrent pipelined realization of the DHT. The proposed structures for direct-memory-based implementation is found to involve nearly the same hardware complexity as those of the existing structures, but offers two to four times more throughput and two to four times less latency compared with others. The distributed-arithmetic (DA)-based implementation is also found to offer very less memory-complexity and considerably low area-delay complexity compared with the existing DA-based structures.     

Multiple Decision Expert Systems for Performance Analysis of a Boiler System

Artificial neural networks (ANN)-based multiple decision expert systems (MDES) were developed for assessing the performance of a boiler system. Different configurations of ANN were used with different decision combination methods, including a neural combiner, to propose the model. The model was developed using the plant data collected over a period of five months to predict steam temperature, pressure, and mass flow rate, using feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature as the input parameters. The predictive capability of the model is evaluated in terms of both correlation coefficient (R) and mean absolute percentage error (MAPE). The results observed from this work demonstrate that neural combiner and ANN-based MDES can efficiently predict the data on steam properties consistently, and that the model can serve as an efficient tool for monitoring boiler behavior under real-time conditions. Superiority of the proposed model over others under various scenarios is also demonstrated.     

Surface inhibition of double base rocket propellants with photocurable unsaturated esters

Inhibition of a double base propellant surface with a good percentage of nitro bodies in the propellant composition is a rather difficult process. One of the methods of inhibiting the surface is to use unsaturated polyesters with free radical cure having high exotherm as a barrier coating. This system has been improved by using a 1:1 mix of photocurable vinyl ester with pendant —OH groups and unsaturated polyester that underwent room temperature free radical cure. Adhesion of the barrier coat to the propellant surface through its —OH groups was stronger than the propellant strength itself. Additional layers of commercially available unsaturated polyester could be easily grafted on to the barrier coat through chemical cure.