In this paper, we introduce a 10-transistor static random access memory cell that features an unbalanced read decoupled bit line (RBL) with a 4T read pin for high-speed operation. Based on the stored data bit, the RBL is pre-charged to half the cell's supply voltage and allowed to charge and discharge. The Complementary Data (QB) node is driven by an inverter that connects the RBL to the virtual power rails via a transmission gate during read operation. RBL rises to VDD level for a read of 1 and dumps to ground level for a read of 0. During write mode and standby mode, the virtual power rails have the same RBL precharge level value. During the reading process, these are connected to actual supply levels. Dynamic control of virtual rails significantly reduces RBL leakage. The proposed 12T cell is larger compared to the SRAM 10T and reduces read lag by more than 50% compared to the 10T. The general performance characteristics of the 12T and 10T are similar, and the general designs are designed and implemented at Tanner EDA.

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Author : Dr. Supratim Saha

Title : Static Random Access Memory with Half Vdd and Dynamically Powered Read Port for High Speed and Low Switching Power Capabilities

Volume/Issue : 2023;05(02)

Page No : 1-4

Today's undergraduates are expected to have 21st-century skills as part of learning and developing critical thinking abilities.Indeed, the “National Education Policy 2020” seeks to establish an educational system founded on the rich diversity of India's linguistic, cultural, and creative traditions. Refusing to recognize English as an Indian language in the National Education Policy is a sly attempt to impose Hindi. Local languages, particularly tribal ones, are being lost, and this paper expresses worry about their dwindling space. The English and English-aware elites blame the marginalization of other Indian languages as naive. This research paper explains that the value of language cannot be overstated when discussing education. Because of the diversity of Indian languages, the NEP encourages multilingualism and bilingual education while emphasizing the need to improve proficiency in Indian languages. Regarding acquiring a job, "English" is still up for debate regarding its cultural values and ethos. However, the policy's instructions for language usage must be examined in the language classroom. The students of undergraduate Engineering degrees need to be taught about Indian culture and the arts to create a strong sense of cultural identity and an appreciation for art, as they are more prone to migration for higher studies or for better career opportunities (software) but not as a compulsion. Ancient India's language and education were caste-based.The English educational system helped people escape constrained knowledge systems.This attribute improved the country's appeal when the British left.Many middle-class Indian parents believe that sending their children to an English-medium school will help them in the employment market.English is crucial to our global commercial and social competitiveness.

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Author : Mr. SHAIK ILIYAS ALI, Dr. R. VEERA SUDARSANA REDDY

Title : ENGLISH LANGUAGE AND DIASPORA STUDIES FOR ENGINEERING STUDENTS UNDER NEP-2020

Volume/Issue : 2023;05(02)

Page No : 5-19

Alzheimer's disease is a neurological brain disorder that progresses and is incurable. Early diagnosis of Alzheimer's disease can aid in effective care and stop brain tissue destruction. Researchers have used a number of analytical and machine learning models to diagnose Alzheimer's disease. Clinical research routinely uses magnetic resonance imaging (MRI) analysis to identify Alzheimer's disease. Because Alzheimer's disease MRI data and typical MRI data of older persons are comparable, diagnosing Alzheimer's disease can be challenging. In many disciplines, including medical image processing, cutting-edge deep learning approaches have recently effectively proven performance at the level of a human. By analyzing brain MRI data, we suggest a deep convolutional neural network for diagnosing Alzheimer's disease. Our model performs better for early-stage diagnosis and can recognise different phases of Alzheimer's disease than most existing techniques, which mostly do binary classification.

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Author : Dr. Sundharraj M 1 , Makila S 2 , Sakthi Keerthana M 3 , Vasundhara B 4 ,

Title : ALZHEIMER DISEASE PREDICTION USING DEEP LEARNING

Volume/Issue : 2023;05(02)

Page No : 20-25

The outbreak of a fire is a dangerous act that has a number of consequences. Detecting fire at an early stage and extinguishing it can help in preventing various accidents. So far we rely on human resources. This often puts the person's life at risk. Therefore, fire protection becomes an important aspect for saving human lives. A fire fighting robot was designed and designed to detect the fire location and extinguish the fire using sprinklers when the pump is started. This robot uses three flame sensors for accurate fire detection. This proposed model of fire fighting robot using NODEMCU is used to detect the presence of fire and extinguish it automatically without any human intervention. It contains gear motors and a motor controller to control the movement of the robot when it detects any occurrence of fire and automatically starts a water pump to extinguish that fire. This robot model has a water ejector that is capable of releasing water at the scene of a fire. The water ejector tube can be moved in the desired direction using a servo motor. The entire operation is controlled by the NODEMCU microcontroller

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Author : Nivash C, Sanjay R, Kaviya R S, Mohammed Ansar, M,Poorvasanthiya S,Monisha S

Title : SMART FIRE FIGHTING ROBOT

Volume/Issue : 2023;05(02)

Page No : 26-29

With modern enterprises two to five times larger than they were 20 or even ten years ago, producing enormous volumes of structured, semi-structured and unstructured data that needs more sophisticated processing and analysis so as to exploit this data in a way strategic decision making can be made, the demand for more capable storage and analytics also is growing rapidly. While traditional data processing platforms have limitations in their ability to scale, resource management, workload isolation and operational flexibility when it comes to large-scale analytics workloads. This paper describes the design, implementation and execution on one such a scalable analytics framework based on Apache Spark and Kubernetes to process enterprise data. The pipeline built in this architecture integrates the distributed processing capability of Apache Spark along with the orchestration and resource management capabilities of Kubernetes to achieve a cloud-native analytics environment that supports variable workloads. It supports elastic resource allocation, automatic deployment and fault tolerance, along with rapid execution of complex analytical queries. We perform a thorough empirical analysis over wide-ranging enterprise datasets of 10+ million records to measure execution time, throughput, resource utilization and scalability. Results show that the integrated Spark-Kubernetes architecture achieves better processing efficiency, workload manageability, and fault resilience in contrast to conventional deployment methods. The results confirm that cloud-native data analytics systems are successful at resolving modern enterprise computing requirements and provide rich insights to organizations in need of flexible and economical big data per processing platforms

Keywords : Apache Spark, Kubernetes, Enterprise Data Processing, Cloud-Native Analytics, Big Data Analytics, Distributed Data Processing, Resource Management, Scalability, Performance Evaluation, Container Orchestration, Data Engineering, RealTime Analytics, Cloud Computing, Enterprise Intelligence, Elastic Computing.

Author : Processing Naga Hemanth Badabagni Staff Data Engineer | AI Data Platform Architect | Enterprise Data Engineering Leader

Title : Design and Performance Evaluation of Scalable Analytics Systems Using Apache Spark and Kubernetes for Enterprise Data

Volume/Issue : 2023;05(02)

Page No : 31-56