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Recent Submissions

  • Enhanced acoustic pressure sensors based on coherent perfect absorber-laser effect

    Farhat, Mohamed; Ahmed, Waqas Waseem; Khelif, Abdelkrim; Salama, Khaled N.; Wu, Ying (Journal of Applied Physics, AIP Publishing, 2021-03-14) [Article]
    Lasing is a well-established field in optics with several applications. Yet, having lasing or huge amplification in other wave systems remains an elusive goal. Here, we utilize the concept of coherent perfect absorber-laser to realize an acoustic analog of laser with a proven amplification of more than 10 4 in terms of the scattered acoustic signal at a frequency of a few kHz. The obtained acoustic laser (or the coherent perfect absorber-laser) is shown to possess extremely high sensitivity and figure of merit with regard to ultra-small variations of the pressure (density and compressibility) and suggests its evident potential to build future acoustic pressure devices such as precise sensors.
  • On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols

    Celis Sierra, Sebastian; Farhat, Mohamed; Zhang, Li; Bagci, Hakan; Eltawil, Ahmed; Salama, Khaled N. (Electronics, MDPI AG, 2021-03-06) [Article]
    In this paper, we propose the theoretical framework for a reconfigurable radiation pattern modulation (RRPM) scheme, which is reminiscent of the index modulation technique. In the proposed scheme, information is encoded using far-field radiation patterns generated by a set of programmable radiating elements. A considerable effort has been invested to allow for high transmission of the reconfigurable radiation pattern symbols; yet, the receiving system has received little attention and has always been considered ideal. Depending on the number of receivers and their respective positions, two variables are considered here for data transmission: the sampling resolution and the fraction of the covered space by the receiving antennas. Hence, we quantitatively investigate their effect on the bit-error-rate (BER) by making use of a limited number of measurements that approximate the behavior of the system under real-field conditions.
  • A Wideband Magnetic Frequency Up-Converter Energy Harvester

    Fakeih, Esraa; Almansouri, Abdullah S.; Kosel, Jürgen; Younis, Mohammad I.; Salama, Khaled N. (Advanced Engineering Materials, Wiley, 2021-03-05) [Article]
    Many sensor applications require small and noninvasive methods of powering, such as marine animal tracking and implantable healthcare monitoring. In such cases, energy harvesting is a viable solution. Vibrational energy harvesting is abundantly available in the environment. These vibrations usually are low in frequency and amplitude. Conventional vibrational harvesters convert the environmental vibrations into electrical signals; however, they suffer from low-voltage outputs and narrow bandwidths, limiting the harvesting to a small range of frequencies. Herein, a new mechanical harvester is introduced using a magnetic frequency up-converter. It is implemented using attractive-force magnetic coupling between a soft magnet and a permanent magnet to convert low-frequency vibrations into high-frequency pulses. Combined with a piezoelectric generator, the harvester generates a high output voltage for an extended bandwidth of operation. The proposed harvester shows a 50.15% increase in output voltage at the resonant frequency (12.2 Hz), resulting in 14.79 V at 1.0 g, with a maximum peak voltage of 16.28 V. The bandwidth of operation ranges from 10.77 to 22.16 Hz (11.39 Hz), which when compared with a single-beam harvester shows an increase of 3250% in the bandwidth, where the average power is greater for 92.56% of this bandwidth.
  • Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems

    Guo, Wenzhe; Fouda, Mohammed Elneanaei; Eltawil, Ahmed; Salama, Khaled N. (Frontiers in Neuroscience, Frontiers Media SA, 2021-03-04) [Article]
    Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs’ constraints and considerations in neuromorphic systems.
  • Gold nanostructured laser-scribed graphene: A new electrochemical biosensing platform for potential point-of-care testing of disease biomarkers

    Rauf, Sakandar; Lahcen, Abdellatif Ait; Aljedaibi, Abdulrahman; Beduk, Tutku; Ilton de Oliveira Filho, José; Salama, Khaled N. (Biosensors and Bioelectronics, Elsevier BV, 2021-02) [Article]
    Improvements in the Laser-scribed graphene (LSG)-based electrodes are critical to overcoming limitations of bare LSG electrodes in terms of sensitivity, direct immobilization of detection probes for biosensor fabrication, and ease of integration with point-of-care (POC) devices. Herein, we introduce a new class of nanostructured gold modified LSG (LSG-AuNS) electrochemical sensing system comprising LSG-AuNS working electrode, LSG reference, and LSG counter electrode. LSG-AuNS electrodes are realized by electrodeposition of gold chloride (HAuCl4) solution, which gave ∼2-fold enhancement in sensitivity and electrocatalytic activity compared to bare LSG electrode and commercially available screen-printed gold electrode (SPAuE). We demonstrate LSG-AuNS electrochemical aptasensor for detecting human epidermal growth factor receptor 2 (Her-2) with a limit of detection (LOD) of 0.008 ng/mL and a linear range of 0.1-200 ng/mL. LSG-AuNS-aptasensor can easily detect different concentrations of Her-2 spiked in undiluted human serum. Finally, to show the LSG-AuNS sensor system's potential to develop POC biosensor devices, we integrated LSG-AuNS electrodes with a handheld electrochemical system operated using a custom-developed mobile application.
  • Coating of Conducting and Insulating Threads with Porous MOF Particles through Langmuir-Blodgett Technique

    Rauf, Sakandar; Andrés, Miguel A.; Roubeau, Olivier; Gascón, Ignacio; Serre, Christian; Eddaoudi, Mohamed; Salama, Khaled N. (Nanomaterials, MDPI AG, 2021-01-10) [Article]
    The Langmuir-Blodgett (LB) method is a well-known deposition technique for the fabrication of ordered monolayer and multilayer thin films of nanomaterials onto different substrates that plays a critical role in the development of functional devices for various applications. This paper describes detailed studies about the best coating configuration for nanoparticles of a porous metal-organic framework (MOF) onto both insulating or conductive threads and nylon fiber. We design and fabricate customized polymethylmethacrylate sheets (PMMA) holders to deposit MOF layers onto the threads or fiber using the LB technique. Two different orientations, namely, horizontal and vertical, are used to deposit MIL-96(Al) monolayer films onto five different types of threads and nylon fiber. These studies show that LB film formation strongly depends on deposition orientation and the type of threads or fiber. Among all the samples tested, cotton thread and nylon fiber with vertical deposition show more homogenous monolayer coverage. In the case of conductive threads, the MOF particles tend to aggregate between the conductive thread’s fibers instead of forming a continuous monolayer coating. Our results show a significant contribution in terms of MOF monolayer deposition onto single fiber and threads that will contribute to the fabrication of single fiber or thread-based devices in the future.
  • Towards a low cost fully integrated IGZO TFT NO2 detection and quantification: A solution-processed approach

    Vijjapu, Mani Teja; Surya, Sandeep Goud; Zalte, Maruti; Yuvaraja, Saravanan; Baghini, Maryam Shojaei; Salama, Khaled N. (Sensors and Actuators B: Chemical, Elsevier BV, 2021-01-09) [Article]
    Semiconducting metal oxide gas sensors that are inexpensive, room temperature operable are imperative for toxic gas and air pollution monitoring. The heating or continuous light activation requirements are unavoidable for these types of gas sensors. This paper reports for the first time a room temperature operable solution-processed In-Ga-Zn-O thin-film transistor (TFT) gas sensor to detect NO2 without continuous light activation. The TFT sensor exhibits promising sensing and selectivity towards NO2. The devices are re-generable and require light activation only to recover after exposure to the gas. Kelvin probe force microscopy characterization studies are performed to understand in detail the sensing mechanism of the solution-processed TFT device. A novel solution-processed TFT based gas-sensitive digital indicators that yield digital output quantifying the NO2 concentration are demonstrated. One of the integrated gas-sensitive digital indicators is developed using only two InGaZnO TFTs incorporating a signal conditioning circuit and analog to digital converter. Furthermore, it paves the way for portable, compact, and inexpensive systems for various gas sensing applications, including smart cities and sustainable ecosystems.
  • Laser-scribed Graphene Electrodes as an Electrochemical Immunosensing Platform for Cancer Biomarker ‘eIF3d’

    Balaban, Simge; Beduk, Tutku; Durmus, Ceren; Aydindogan, Eda; Salama, Khaled N.; Timur, Suna (Electroanalysis, Wiley, 2021-01-08) [Article]
    eIF3d is a protein biomarker which has a potential for the diagnosis of various cancers. Herein, a bio-platform was constructed for eIF3d sensing by using LSG and surface functionalization with anti eIF3d antibody via EDC/NHS chemistry. Following the surface modifications, XPS and several electrochemical methods were used. Difference in the signals were related to biomarker amounts between 75–500 ng/mL. LOD was calculated as 50.4 ng/mL. Selectivity of biosensor was tested by using of various interference molecules. EIF3d was also successfully detected in synthetic biological samples. Thus, to the best of our knowledge, this study is one of the rare studies on use of LSGs in immunosensor studies.
  • Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks

    Guo, Wenzhe; Yantir, Hasan Erdem; Fouda, Mohamed E.; Eltawil, Ahmed; Salama, Khaled N. (IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers (IEEE), 2021) [Article]
    The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (FPGA) online learning neuromorphic platform for the digital implementation based on two numerical methods, namely, the Euler and third-order Runge-Kutta (RK3) methods. The optimization scheme explores the impact of biological time constants on information transmission in the SNN and improves the convergence rate of the SNN on digit recognition with a suitable choice of the time constants. The parallel digital implementation leads to a significant speedup over software simulation on a general-purpose CPU. The parallel implementation with the Euler method enables around 180x (20x) training (inference) speedup over a Pytorch-based SNN simulation on CPU. Moreover, compared with previous work, our parallel implementation shows more than 300x (240x) improvement on speed and 180x (250x) reduction in energy consumption for training (inference). In addition, due to the high-order accuracy, the RK3 method is demonstrated to gain 2x training speedup over the Euler method, which makes it suitable for online training in real-time applications.
  • A highly selective electron affinity facilitated H2S sensor: the marriage of tris(keto-hydrazone) and an organic field-effect transistor

    Yuvaraja, Saravanan; Bhyranalyar, Veerabhadraswamy Nagarajappa; Bhat, Sachin Ashok; Surya, Sandeep Goud; Yelamaggad, Channabasaveshwar Veerappa; Salama, Khaled N. (Materials Horizons, Royal Society of Chemistry (RSC), 2021) [Article]
    The proposed H$_{2}$S gas sensor is a novel heterojunction combination that can readily absorb toxic gases, changing the channel resistance of the device. The OFET device is a highly stable and selective tool that can help in taking preventive measures.
  • IMCA: An Efficient In-Memory Convolution Accelerator

    Yantir, Hasan Erdem; Eltawil, Ahmed; Salama, Khaled N. (IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Institute of Electrical and Electronics Engineers (IEEE), 2021) [Article]
    Traditional convolutional neural network (CNN) architectures suffer from two bottlenecks: computational complexity and memory access cost. In this study, an efficient in-memory convolution accelerator (IMCA) is proposed based on associative in-memory processing to alleviate these two problems directly. In the IMCA, the convolution operations are directly performed inside the memory as in-place operations. The proposed memory computational structure allows for a significant improvement in computational metrics, namely, TOPS/W. Furthermore, due to its unconventional computation style, the IMCA can take advantage of many potential opportunities, such as constant multiplication, bit-level sparsity, and dynamic approximate computing, which, while supported by traditional architectures, require extra overhead to exploit, thus reducing any potential gains. The proposed accelerator architecture exhibits a significant efficiency in terms of area and performance, achieving around 0.65 GOPS and 1.64 TOPS/W at 16-bit fixed-point precision with an area less than 0.25 mm².
  • Electrochemical sensors targeting salivary biomarkers: A comprehensive review

    Mani, Veerappan; Beduk, Tutku; Khushaim, Walaa; Ceylan, Ayse Elcin; Timur, Suna; Wolfbeis, Otto S.; Salama, Khaled N. (TrAC Trends in Analytical Chemistry, Elsevier BV, 2020-12) [Article]
    The analysis of salivary markers has grown into a promising non-invasive route for easy, safe, and pain-free monitoring and has the potential to alter the existing way of clinical diagnosis and management. Advancements in sensing technology, the arrival of novel materials, the innovative fabrication technologies, and sampling accuracy have made significant progress and establishing saliva as a fluid for routine analysis. Salivary biomarkers are useful to diagnose not only cardiovascular diseases, bacterial or viral infections but also cancer, diabetes, or Alzheimer’s disease. In addition, saliva is analyzed in toxicology, forensic medicine and drug abuse. Electrochemical assays and sensors are well accepted tools because they allow for fast and cost-effective analysis. Nanomaterials, microfluidics, smartphones, paper-based, flexible and wearable devices have made significant advancements in saliva analysis. This review discusses the recent progress made in electrochemical methodologies for detecting salivary biomarkers
  • An Assistive Magnetic Skin System: Enabling Technology for Quadriplegics

    Almansouri, Abdullah S.; Upadhyaya, Lakshmeesha; Nunes, Suzana Pereira; Salama, Khaled N.; Kosel, Jürgen (Advanced Engineering Materials, Wiley, 2020-10-27) [Article]
    People with quadriplegia no longer have control over their legs, neither the hands and cannot continue living their life independently. On top of that, severely injured quadriplegics (i.e., C1 and C2 injuries) suffer from speaking difficulties and minimal head and neck movements. With the advancement in wearable artificial skins and the Internet of Things, realizing comfortable and practical solutions for quadriplegics is more tangible than ever. Here, a comprehensive assistive magnetic skin system is presented that allows quadriplegics, including the severely injured ones, to move around individually and control their surroundings with ease. The system tracks facial expressions by tracking the movement of magnetic tattoos attached to the face, using magnetic field sensors incorporated into eyeglasses. The magnetic tattoos are made of highly flexible, stretchable, breathable, and biocompatible magnetic skins. In combination with smart-glasses, smart-wheelchair, and smart-gadgets, the users can move around and control their environment with their facial expressions. The system is also designed to allow quadriplegics to perform outdoor activities effortlessly. It supports line-of-sight communication and does not require pre-tethering to the smart-gadgets, unlike the existing solutions. Thus, enabling the user to walk on pathways, activate pedestrian lights, control public elevators, and perform various outdoor activities independently.
  • Reliable High-Frequency Fabricated Fractional-Order Capacitors and Their Passive Circuit Models Guvenilir Yuksek Frekansli Kesirli Dereceden Kapasitorler ve Pasif Devre Modelleri

    Kartci, Aslihan; Herencsar, Norbert; Salama, Khaled N. (Institute of Electrical and Electronics Engineers (IEEE), 2020-10-05) [Conference Paper]
    The impedance characteristics of three different type of fractional-order capacitors (FOCs) with an order of -0.74, -0.79, and -0.91 are analyzed. The used devices have excellent feature such as constant phase angle in the frequency range 10 MHz - 100 MHz. Their impedance data is fitted with second-order passive electrical model structures of Foster-I abd Foster-II using standard EIA-48 compliant component values phase error. The effect on phase and pseudo-capacitance using a detailed experimental study of series-, parallel-, and inter-connected FOCs is also shown.
  • An Efficient 2D Discrete Cosine Transform Processor for Multimedia Applications

    Yantir, Hasan Erdem; Eltawil, Ahmed; Salama, Khaled N. (Institute of Electrical and Electronics Engineers (IEEE), 2020-10-05) [Conference Paper]
    The memory bottleneck is the biggest concern affecting the scalability of traditional computer architectures. Furthermore, the necessity of applications to process the huge amount of data is increasing, especially after the evaluation of artificial intelligence (AI). This fact forces researchers to move through the more data-centric architectures rather than the existing processor centric ones. In-memory processors are such architecture that combines the memory and processor in the same location to eliminate the memory bottleneck. Associative processors are an ideal candidate for in-memory computation, especially for signal processing since the data is a key point in these applications. To demonstrate this, 2D DCT is implemented in associate in-memory processors. According to the comparison with the state of the art hardware realization, the proposed accelerator achieves the best energy efficiency for 4K HD inputs at 30 frames per second.
  • Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search

    Krestinskaya, O.; Salama, Khaled N.; James, A. P. (Institute of Electrical and Electronics Engineers (IEEE), 2020-09-29) [Conference Paper]
    The selection of hyperparameters and circuit components for optimum hardware implementation of a neural network is a challenging task, which has not been automated yet. This work proposes the method for the selection of optimum neural network architecture and hyperparameters using genetic algorithm based on the hardware-related performance metrics, such an on-chip area, power consumption, processing time and robustness to hardware non-idealities, and focus on memristor-based analog network architecture. The experimental results show that the proposed approach allows to select the optimum architecture based on the designers' preferences.
  • Rail-to-rail complementary input StrongARM comparator for low-power applications

    Al-Qadasi, Mohammed A.; Alshehri, Abdullah; Alturki, Abdullah; Almansouri, Abdullah S.; Salama, Khaled N.; Fariborzi, Hossein; Al Attar, Talal (IET Circuits, Devices and Systems, Institution of Engineering and Technology (IET), 2020-09-10) [Article]
    This study proposes a new scheme for rail-to-rail input StrongARM latch comparator. Additional differential input p-type metal-oxide-semiconductor (PMOS) and cross-coupled n-type metal-oxide-semiconductor (NMOS) transistors have been introduced to achieve the rail-to-rail input range. The proposed scheme offers low-energy consumption of 15.2 fJ and a high speed of 3 GHz, which makes it attractive for energy harvested Internet of Thing applications. The proposed architecture is fabricated in 65 nm complementary metal-oxide-semiconductor (CMOS) technology and the functionality is verified using post-layout simulations and chip measurements.
  • Semi-transparent graphite films growth on Ni and their double-sided polymer-free transfer.

    Deokar, Geetanjali Baliram; Genovese, Alessandro; Surya, Sandeep Goud; Long, Chen; Salama, Khaled N.; Da Costa, Pedro M. F. J. (Scientific reports, Springer Nature, 2020-09-07) [Article]
    Nanorange thickness graphite films (NGFs) are robust nanomaterials that can be produced via catalytic chemical vapour deposition but questions remain regarding their facile transfer and how surface topography may affect their application in next-generation devices. Here, we report the growth of NGFs (with an area of 55 cm2 and thickness of ~ 100 nm) on both sides of a polycrystalline Ni foil and their polymer-free transfer (front- and back-side, in areas up to 6 cm2). Due to the catalyst foil topography, the two carbon films differed in physical properties and other characteristics such as surface roughness. We demonstrate that the coarser back-side NGF is well-suited for NO2 sensing, whereas the smoother and more electrically conductive front-side NGF (2000 S/cm, sheet resistance - 50 Ω/sq) could be a viable conducting channel or counter electrode in solar cells (as it transmits 62% of visible light). Overall, the growth and transfer processes described could help realizing NGFs as an alternative carbon material for those technological applications where graphene and micrometer-thick graphite films are not an option.
  • Numerical modeling for terahertz testing of non-metallic pipes

    Farhat, Mohamed; Amer, A. M.; Cunningham, V. B.; Salama, Khaled N. (AIP Advances, AIP Publishing, 2020-09-04) [Article]
    In the oil and gas industry, safety and operational efficiency at production sites are of paramount importance. A reliable non-destructive testing technology for non-metallic pipes has a high potential financial impact, since it may facilitate the replacement of metallic pipes with non-metallic ones. This article features a perspective and future trends in the field of terahertz sensing technology. Importantly, several numerical simulations that illustrate many exciting potential applications for this emerging technology are described. These range from underground detection of spilt liquids and the content of pipes to the detection of cracks in plastic pipes using both frequency-domain and time-domain finite-element simulations.
  • Electrochemical sensors and biosensors using laser-derived graphene: A comprehensive review

    Ait Lahcen, Abdellatif; Rauf, Sakandar; Beduk, Tutku; Durmus, Ceren; Aljedaibi, Abdulrahman; Timur, Suna; Alshareef, Husam N.; Amine, Aziz; Wolfbeis, Otto S.; Salama, Khaled N. (Biosensors and Bioelectronics, Elsevier BV, 2020-08-27) [Article]
    Laser-derived graphene (LDG) technology is gaining attention as a promising material for the development of novel electrochemical sensors and biosensors. Compared to established methods for graphene synthesis, LDG provides many advantages such as cost-effectiveness, fast electron mobility, mask-free, green synthesis, good electrical conductivity, porosity, mechanical stability, and large surface area. This review discusses, in a critical way, recent advancements in this field. First, we focused on the fabrication and doping of LDG platforms using different strategies. Next, the techniques for the modification of LDG sensors using nanomaterials, conducting polymers, biological and artificial receptors are presented. We then discussed the advances achieved for various LDG sensing and biosensing schemes and their applications in the fields of environmental monitoring, food safety, and clinical diagnosis. Finally, the drawbacks and limitations of LDG based electrochemical biosensors are addressed, and future trends are also highlighted.

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