Fakhreddine Zayer

Khalifa University | KU · Department of Electrical and Computer Engineering

This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VIO framework is incorporating a GAN-based pre-processing module and a hybrid CNN-LSTM module for prec...

This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated artificial intelligence with sign...

This paper presents an innovative approach utilizing in-memory computing (IMC) for the development and integration of AES (Advanced Encryption Standard) cipher technique. Our research aims to enhance cybersecurity measures for a wide range of applications for IoT, such as robotic self-driving and several uses contexts. Memristor (MR) design optimiz...

This paper introduces a perspective approach for simulating a memristive sensor tailored for low power biological analyte detection. The necessity for such innovation stems from the increasing demand for efficient biosensing technologies that can operate with minimal poxer consumption. Within this study, a numerical dynamic memristive model serves...

Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, loca...

This paper presents a novel methodology for modeling memristive biosensing within COMSOL Multiphysics, focusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasing inlet concentrations, insights into binding concentration curve and output resistance variati...

Memristors offer high density, low energy consumption, memory effect capability, and advanced sensing capabilities, making them ideal for revolutionizing nonvolatile memory. We are developing a nanosensor with low power consumption that uses machine learning to accurately and efficiently detect biomolecules. Our simulations and machine learning cla...

Robotic automation has always been employed to optimize tasks that are deemed repetitive or hazardous for humans. One instance of such an application is within transportation, be it in urban environments or other harsh applications. In said scenarios, it is required for the platform’s operator to be at a heightened level of awareness at all times t...

Optical, structural, electronic, and energetic properties of tetraphenyl (hydroxyl) imidazole were carried out using B3LYP-D3/6-311G (d, p) theoretical level. Complexation of imidazole compound with Br⁻, Cl⁻, CN⁻, SCN⁻, NO3⁻, CH3COO⁻, SO4⁻ and I⁻ anions have been analyzed using experimental UV spectroscopy. Data indicated that the addition of vario...

The paper proposes in-memory computing (IMC) solution for the design and implementation of the Advanced Encryption Standard (AES) based cryptographic algorithm. This research aims at increasing the cyber security of autonomous driverless cars or robotic autonomous vehicles. The memristor (MR) designs are proposed in order to emulate the AES algorit...

Random spray retinex (RSR) is an effective image enhancement algorithm owing to its effectiveness in improving the image quality. However, the computing complexity of the algorithm, the required hardware resources, and memory access hamper its deployment in many application scenarios, for instance, in IoT systems with limited hardware resources. Wi...

This paper presents one-diode-one resistor-one-resistor-one-diode (1D1R-1R1D) based Resistive Random Access Memory (RRAM) crossbar architecture and introduces the Complementary Resistive Switching (CRS) structure as alternative improved strategies for the electrothermal RRAM integration. Signal integrity issue is mitigated by using the CRS topology...

The paper proposes novel solutions to improve the signal and thermal integrity of crossbar arrays of Resistive Random-Access Memories, that are among the most promising technologies for the 3D monolithic integration. These structures suffer from electrothermal issues, due to the heat generated by the power dissipation during the write process. This...

In this article, a 3-D electrothermal numerical model is used to perform the signal and thermal integrity analysis of 3-D stacked Resistive-switching random access memory (RRAM) arrays. Two main issues are found: voltage drop along the interconnects and thermal crosstalk between the memory cells. Possible solutions to these issues are here thorough...

Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering...

Two of the most critical issues affecting the device reliability in monolithic Resistive Random Access Memories (RRAM) integration are given by the electrical and thermal crosstalk between active and passive layers in the stack. In this paper, nickel-based one-diode-one-resistor (1D1R) and 1D1R-1R1D cross-point structures are redesigned in order to...

This paper presents a comparative synthesis of the suitability of three memristive device technologies and their corresponding spike-timing-dependent plasticity (STDP) learning windows for neuromorphic applications. The physical mechanisms behind the nonlinear switching memristive dynamics of ReRAM, based on titanium dioxide, ferroelectric tunnel j...

This paper presents an extension of the threshold adaptive memristor (TEAM) model, which is derived based on the analysis of the physical tunnel barrier memristor (TBM) model. A novel window function is proposed in order to ensure the effective resolution of the boundary conditions, full scalability, and accurate imitation of the nonlinear dependen...

This paper presents the design and implementation of a low power and ultrafast spike-timing dependent plasticity (STDP) of a crossbar structure based on the ferroelectric tunnel memristor (FTM) in which nonlinear switching behaviors are mathematically described by a physics-based compact model. The FTM is used as 1R structure without a cell selecto...

This paper presents the development and evaluation of a large-signal equivalent circuit model that accounts for the power supply fluctuation and temperature variation of I/O buffers circuit designed based on the fully depleted silicon on insulator (FDSOI) 28 nm process for signal-power integrity (SPI) simulation. A solid electrical analysis based o...

This chapter presents the physical mechanism analysis and the compact behavioral modeling of the titanium oxide, ferroelectric tunnel junctions, and phase change materials memristive devices. The memristive devices mathematical theoretical model’s derivation and physics-based model structure representations along with their resistive switching mech...

Three-dimensional integrated circuits (3D-ICs) based on Through-Silicon-Vias (TSVs) interconnection technology are appeared as a viable solution to overcome problems of cost, reliability, yield and stacking area. In order to be commercially feasible, the 3D-IC yield must be as high as possible, which requires a tested and reparable TSVs. To overpas...