Browsing by Author "Jafari Navimipour, Nima"

Now showing 1 - 4 of 4

Citation - WoS: 8
An Efficient Architecture of Adder Using Fault-Tolerant Majority Gate Based on Atomic Silicon Nanotechnology
(Ieee-inst Electrical Electronics Engineers inc, 2023) Ahmadpour, Seyed-Sajad; Jafari Navimipour, Nima; Bahar, Ali Newaz; Yalcin, Senay
It is expected that Complementary Metal Oxide Semiconductor (CMOS) implementation with ever-smaller transistors will soon face significant issues such as device density, power consumption, and performance due to the requirement for challenging fabrication processes. Therefore, a new and promising computation paradigm, nanotechnology, can replace CMOS technology. In addition, a new frontier in computing is opened up by nanotechnology called atomic silicon, which has the same extraordinary behavior as quantum dots. On the other hand, atomic silicon circuits are highly prone to defects, so suggested fault-tolerant structures in this technology play important roles. The full adders have gained popularity and find widespread use in efficiently solving mathematical problems. In the following article, we will explore the development of an efficient fault-tolerant 3-input majority gate (FT-MV3) using DBs, further enhancing the capabilities of digital circuits. A rule-based approach to the redundant DB achieves a less complex and more robust atomic silicon layout for the MV3. We use the SiQAD tool to simulate proposed circuits. In addition, to confirm the efficiency of the proposed gate, all common defects, such as single and double dangling bond omission defects and DB dislocation defects, are examined. The suggested gate is 100% and 66.66% tolerant against single and double DB omission defects, respectively. Furthermore, a new adder design is introduced using the suggested FT-MV3 gate. The results show that the suggested adder is 44.44% and 35.35% tolerant against single and double DB omission defects. Finally, a fault-tolerant four-bit adder is designed based on the proposed adder.
Citation - WoS: 49
Citation - Scopus: 82
The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors
(Mdpi, 2023) Amiri, Zahra; Heidari, Arash; Darbandi, Mehdi; Yazdani, Yalda; Jafari Navimipour, Nima; Esmaeilpour, Mansour; Sheykhi, Farshid
With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.
Citation - WoS: 18
Citation - Scopus: 19
An Energy-Aware Nanoscale Design of Reversible Atomic Silicon Based on Miller Algorithm
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Ahmadpour, Seyed-Sajad; Jafari Navimipour, Nima; Bahar, Ali Nawaz; Mosleh, Mohammad; Yalcin, Senay
Area overhead and energy consumption continue to dominate the scalability issues of modern digital circuits. In this context, atomic silicon and reversible logic have emerged as suitable alternatives to address both issues. In this article, the authors propose novel nano-scale circuit design with low area and energy overheads using the same. In particular, the authors propose a reversible gate with Miller algorithm and atomic silicon technology. This article is extremely relevant in todays era, when the world is moving toward low area and low energy circuits for use in edge devices.
Citation - WoS: 16
Citation - Scopus: 24
A Fire Evacuation and Control System in Smart Buildings Based on the Internet of Things and a Hybrid Intelligent Algorithm
(Mdpi, 2023) Mohammadiounotikandi, Ali; Fakhruldeen, Hassan Falah; Meqdad, Maytham N.; Ibrahim, Banar Fareed; Jafari Navimipour, Nima; Unal, Mehmet
Concerns about fire risk reduction and rescue tactics have been raised in light of recent incidents involving flammable cladding systems and fast fire spread in high-rise buildings worldwide. Thus, governments, engineers, and building designers should prioritize fire safety. During a fire event, an emergency evacuation system is indispensable in large buildings, which guides evacuees to exit gates as fast as possible by dynamic and safe routes. Evacuation plans should evaluate whether paths inside the structures are appropriate for evacuations, considering the building's electric power, electric controls, energy usage, and fire/smoke protection. On the other hand, the Internet of Things (IoT) is emerging as a catalyst for creating and optimizing the supply and consumption of intelligent services to achieve an efficient system. Smart buildings use IoT sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. This research proposes a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. This research utilizes a hybrid nature-inspired optimization approach, Emperor Penguin Colony, and Particle Swarm Optimization (EPC-PSO). The EPC algorithm is regulated by the penguins' body heat radiation and spiral-like movement inside their colony. The behavior of emperor penguins improves the PSO algorithm for sooner convergences. The method also uses a particle idea of PSO to update the penguins' positions. Experimental results showed that the proposed method was executed accurately and effectively by cost, energy consumption, and execution time-related challenges to ensure minimum life and resource causalities. The method has decreased the execution time and cost by 10.41% and 25% compared to other algorithms. Moreover, to achieve a sustainable system, the proposed method has decreased energy consumption by 11.90% compared to other algorithms.