Implementation of Braun Multiplier with Efficient Pyramidal Adder Structure for Arithmetic Applications

Multiplier plays key role in Signal Processing and VLSI based environment applications, as it consumes more power and area compared other devices. In real time applications power and area are important parameters. Multiplier is essential component as it occupies large area and consumes more power compared to any other element .we have so many adders to design multiplier .In this paper Pyramidal adders are used which uses half-adder and full-adder to increase the speed and to reduce the number of gates used in the multiplier, but delay is not decreased significantly. If we modify the Pyramidal adder with XNOR’s and MUX instead of normal half-adder and full-adder, such pyramidal adder uses less gates and delay is reduced compared normal 16-bit adder. The use of XNOR’s and MUX in Pyramidal adder reduces delay, as the MUX function is only select the output among inputs. The use of such pyramidal adder in multiplier delay can be decreased greatly.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

Beyond the Traditional Analyses and Resource Management in Real-Time Systems

The difficulties in estimating the Worst-Case Execution Time (WCET) of applications make the use of modern computing architectures limited in real-time systems. Critical embedded systems require the tasks of hard real-time applications to meet their deadlines, and formal proofs on the validity of this condition are usually required by certification authorities. In the last decade, researchers proposed the use of probabilistic measurement-based methods to estimate the WCET instead of traditional static methods. In this chapter, we summarize recent theoretical and quantitative results on the use of probabilistic approaches to estimate the WCET presented in the PhD thesis of the author, including possible exploitation scenarios, open challenges, and future directions.

Multimodal Biometric Authentication for Smartphones

Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study

Background Semantic textual similarity (STS) measures the degree of relatedness between sentence pairs. The Open Health Natural Language Processing (OHNLP) Consortium released an expertly annotated STS data set and called for the National Natural Language Processing Clinical Challenges. This work describes our entry, an ensemble model that leverages a range of deep learning (DL) models. Our team from the National Library of Medicine obtained a Pearson correlation of 0.8967 in an official test set during 2019 National Natural Language Processing Clinical Challenges/Open Health Natural Language Processing shared task and achieved a second rank. Objective Although our models strongly correlate with manual annotations, annotator-level correlation was only moderate (weighted Cohen κ=0.60). We are cautious of the potential use of DL models in production systems and argue that it is more critical to evaluate the models in-depth, especially those with extremely high correlations. In this study, we benchmark the effectiveness and efficiency of top-ranked DL models. We quantify their robustness and inference times to validate their usefulness in real-time applications. Methods We benchmarked five DL models, which are the top-ranked systems for STS tasks: Convolutional Neural Network, BioSentVec, BioBERT, BlueBERT, and ClinicalBERT. We evaluated a random forest model as an additional baseline. For each model, we repeated the experiment 10 times, using the official training and testing sets. We reported 95% CI of the Wilcoxon rank-sum test on the average Pearson correlation (official evaluation metric) and running time. We further evaluated Spearman correlation, R², and mean squared error as additional measures. Results Using only the official training set, all models obtained highly effective results. BioSentVec and BioBERT achieved the highest average Pearson correlations (0.8497 and 0.8481, respectively). BioSentVec also had the highest results in 3 of 4 effectiveness measures, followed by BioBERT. However, their robustness to sentence pairs of different similarity levels varies significantly. A particular observation is that BERT models made the most errors (a mean squared error of over 2.5) on highly similar sentence pairs. They cannot capture highly similar sentence pairs effectively when they have different negation terms or word orders. In addition, time efficiency is dramatically different from the effectiveness results. On average, the BERT models were approximately 20 times and 50 times slower than the Convolutional Neural Network and BioSentVec models, respectively. This results in challenges for real-time applications. Conclusions Despite the excitement of further improving Pearson correlations in this data set, our results highlight that evaluations of the effectiveness and efficiency of STS models are critical. In future, we suggest more evaluations on the generalization capability and user-level testing of the models. We call for community efforts to create more biomedical and clinical STS data sets from different perspectives to reflect the multifaceted notion of sentence-relatedness.

Trust-Based Security: Advancement towards Protection of the Adhoc Network

MANET (mobile ad hoc network) is a collection of mobile nodes that interact without the need for a fixed physical foundation. MANETs have grown in popularity as a result of characteristics like dynamic topology, quick setup, multi-hop data transfer, and so on. MANETs are well-suited to various real-time applications, including environmental monitoring, disaster management, and covert and military operations, because of their distinguishing characteristics. MANETs may also be used in conjunction with new technologies like cloud computing, IoT, and machine learning algorithms to help realize the vision of Industry 4.0. Secure and reliable data transfer is essential for any MANET-based sensitive real-time applications that must achieve the requisite QoS. It is challenging to provide safe and efficient data transfer with MANET. As a result, this article examines different Trust-based Approaches that take a step forward in providing secure transmission while simultaneously improving MANET performance. Furthermore, the study's analysis based on many aspects exposes the inadequacies of existing techniques and provides future directions for improvement.

RESONATOR-BASED METERING OF POWER COMPONENTS ACCORDING TO IEEE STANDARD 1459-2010

This paper proposes an accurate and computationally efficient implementation of the IEEE Std. 1459-2010 for power measurements. An implementation is based on digital resonators embedded in a feedback loop. In the first algorithm stage, the unknown signal harmonic parameters are estimated. By this, the voltage and current signals are processed independently on each other. In the second algorithm stage, the unknown power components are estimated (calculated) from based on estimated spectra. To demonstrate the performance of the developed algorithm, computer simulated data and laboratory testing records are processed. Simple LabView implementation, based on point-by-point processing feature, demonstrates techniques modest computation requirements and confirms that the proposed algorithm is suitable for real–time applications.