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Abstract The increasing demand for precise neuromodulation necessitates advancements in techniques to achieve higher spatial resolution. Magnetic stimulation, offering low signal attenuation and minimal tissue damage, plays a significant role in neuromodulation. Conventional transcranial magnetic stimulation (TMS), though noninvasive, lacks the spatial resolution and neuron selectivity required for spatially precise neuromodulation. To address these limitations, the next generation of magnetic neurostimulation technologies aims to achieve submillimeter‐resolution and selective neuromodulation with high temporal resolution. Invasive and nanoinvasive magnetic neurostimulation are two next‐generation approaches: invasive methods use implantable microcoils, while nanoinvasive methods use magnetic nanoparticles (MNPs) to achieve high spatial and temporal resolution of magnetic neuromodulation. This review will introduce the working principles, technical details, coil designs, and potential future developments of these approaches from an engineering perspective. Furthermore, the review will discuss state‐of‐the‐art microfabrication in depth due to its irreplaceable role in realizing next‐generation magnetic neuromodulation. In addition to reviewing magnetic neuromodulation, this review will cover through‐silicon vias (TSV), surface micromachining, photolithography, direct writing, and other fabrication technologies, supported by case studies, providing a framework for the integration of magnetic neuromodulation and microelectronics technologies.

Motion artifacts can be a significant noise source in biomagnetic measurements when magnetic sensors are not separated from the signal source. In ambient environments, motion artifacts can be up to ten times stronger than the desired signals, varying with environmental conditions. This study evaluates the variability of these artifacts and the effectiveness of a gradiometer in reducing them in such settings. To achieve these objectives, we first measured the single channel output in varying magnetic field conditions to observe the effect of homogeneous and gradient background fields. Our analysis revealed that the variability in motion artifact within an ambient environment is primarily influenced by the gradient magnetic field rather than the homogeneous one. Subsequently, we configured a gradiometer in parallel and vertical alignment with the direction of vibration (X-axis). Our findings indicated that in a gradient background magnetic field ranging from 1 nT/mm to 10 nT/mm, the single-channel sensor output exhibited a change of 164.97 pT per mm unit increase, while the gradiometer output showed a change of only 0.75 pT/mm within the same range. Upon repositioning the gradiometer vertically (Y direction), perpendicular to the direction of vibration, the single-channel output slope increased to 196.85 pT, whereas the gradiometer output only increased by 1.06 pT/mm for the same range. Our findings highlight the influence of ambient environments on motion artifacts and demonstrate the potential of gradiometers to mitigate these effects. In the future, we plan to record biomagnetic signals both inside and outside the shielded room to compare the efficacy of different gradiometer designs under varying environmental conditions.

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode–skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.

Abstract Malaria continues to be among the most lethal infectious diseases. Immediate barriers include the detection of low-parasitemia levels in asymptomatic individuals, which act as a reservoir for future infections, and the emergence of multidrug-resistant strains in malaria-endemic, under-resourced regions. The development of technologies for field-deployable devices for early detection and targeted drugs/vaccines is an ongoing challenge. In this respect, the identification of hemozoin during the Plasmodium growth cycle presents a unique opportunity as a biomarker for malaria infection. The last decade has witnessed the development of numerous opto-/magnetic- based ultrasensitive hemozoin sensing technologies with tremendous potential of rapid and accurate malaria diagnosis and drug testing. The unique information in hemozoin formation can also shed light on the development of targeted drugs. Here, we present a comprehensive perspective on state-of-the-art hemozoin-based methodologies for detecting and studying malaria. We discuss the challenges (and opportunities) to expedite the translation of the technology as a point-of-site tool to assist in the global eradication of malaria infection.

Abstract In the technologically changing world, the demand for ultra‐reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions.

Abstract The antenna is one of the key building blocks of many wearable electronic devices, and its functions include wireless communications, energy harvesting, and radiative wireless power transfer. In an effort to realize lightweight, autonomous, and battery‐less wearable devices, we demonstrate a reconfigurable antenna design for 5G wearable applications that requires ultra‐low driving voltages (0.4–0.6 V) and operates over a high frequency range (3.3–3.8 GHz). For smart glasses applications, previous antenna designs were ‘fixed’ and mounted on the eyeglass frame itself. Here, we demonstrate a reconfigurable design that could be achieved on the lens itself using an anisotropic liquid crystal (LC) material. We demonstrate how liquid crystal alignment and electric field patterns strongly influence the tuning capabilities of these antennas in the gigahertz range and present a smart, reconfigurable spiral antenna system with a liquid crystal substrate.

Neuromuscular disorders (NMDs) affect various parts of a motor unit, such as the motor neuron, neuromuscular junction, and muscle fibers. Abnormal spontaneous activity (SA) is detected with electromyography (EMG) as an essential hallmark in diagnosing NMD, which causes fatigue, pain, and muscle weakness. Monitoring the effects of NMD calls for new smart devices to collect and classify EMG. Delay-based Reservoir Computing (DRC) is a neuromorphic algorithm with high efficiency in classifying sequential data. This work proposes a new DRC-based algorithm that provides a reference for medical education and training and a second opinion to clinicians to verify NMD diagnoses by detecting SA in muscles. With a sampling frequency of Fs = 64 kHz, we have classified SA with EMG signals of 1 s of muscle recordings. Furthermore, the DRC model of size N = 600 nodes has successfully detected SA signals against normal muscle activity with an accuracy of up to 90.7%. The potential of using neuromorphic processing approaches in point-of-care diagnostics, alongside the supervision of a clinician, provides a more comprehensive and reliable clinical profile. Our developed model benefits from the potential to be implemented in physical hardware to provide near-sensor edge computing.

Miniaturized, flexible, and biocompatible neural probes have the potential to circumvent the brain's foreign body response, but the problem of surgical implantation remains. Herein, a probe intended for implantation in the rat hippocampus is coated in four bioresorbable stiffeners to determine which is most effective in aiding insertion. The stiffeners (sucrose, maltose, silk fibroin, and alginate) are evaluated through mechanical, chemical, and dissolution tests. After coating with silk fibroin, the buckling force of the neural probe increases from 0.31 to 75.99 mN. This goes in accordance with subsequent successful insertion tests. Fourier transform infrared spectroscopy results demonstrate the increase in β‐sheet content of silk fibroin samples after treatment (e.g., water annealing) and show relevant changes due to dehydration of the alginate hydrogel. Both qualitative and quantitative dissolution studies in artificial cerebrospinal fluid illustrate that alginate and silk fibroin outlasts the disaccharide stiffeners. In this work, a variety of multidisciplinary analyses are carried out to find the best bioresorbable stiffener for deep brain implantable devices with the highest buckling force, longest dissolution time, and the most tunable structure. For the first time, an alginate hydrogel is used as a stiffener to aid insertion, expanding its usefulness beyond neural tissue engineering.

Rapid advances in wearable sensing technology have demonstrated unprecedented opportunities for artificial intelligence. In comparison with the traditional hand‐held electrolarynx, a wearable and intelligent artificial throat with sound‐sensing ability is a more comfortable and versatile method to assist disabled people with communication. Herein, a piezoresistive sensor with a novel configuration is demonstrated, which consists of polystyrene (PS) spheres as microstructures sandwiched between silver nanowires and reduced graphene oxide layers. In fact, changes in the device's conducting patterns are obtained by spay‐coating the various weight ratios and sizes of the PS microspheres, which is a fast and convenient way to establish microstructures for improving sensitivity. The wearable artificial throat device also exhibits high sensitivity, fast response time, and ultralow intensity level detection. Moreover, the device's excellent mechanical–electrical performance allows it to detect subtle throat vibrations that can be converted into controllable sounds. In this case, an intelligent artificial throat is achieved by combining a deep learning algorithm with a highly flexible piezoresistive sensor to successfully recognize five different words (help, sick, patient, doctor, and COVID) with an accuracy exceeding 96%. Herein, new opportunities in voice control as well as other human‐machine interface applications are opened.

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG.

The two most important defining aspects of L1 national identity are language and social belonging that are manifested through the individual’s inclinations toward the mother tongue or the official language and the sociocultural heritage of the country in which people are living. Moreover, based on the available literature, L2 identity may also exert an influence over the L1 national identity; however, this claim has not been securitized through valid large-scale and comprehensive surveys. Therefore, this study sought to shed light on the relationship between various second language identity dimensions (SLID) and L1 national identity. A sample of 1018 Iranian EFL learners who were selected based on the purposive snowball sampling filled out a researcher-made and validated Multidimensional L2 Identity Questionnaire (MLIQ) and a National Identity Questionnaire (NIQ). Data analysis using multiple regression revealed that the constructed SLID model could significantly contribute to the L1 national identity. The results also showed that the following four dimensions of SLID were significant predictors of national identity: transitive vs. intransitive, convergent vs. divergent, homogeneous vs. heterogeneous, and active vs. passive dimensions. Among these, active vs. passive and convergent vs. divergent dimensions had strong contributions to explaining the degree of the L2 national identity. These findings can help EFL teachers and learners develop a positive L2 identity with balanced dimensions that also promotes L1 national identity

Manuscripts are the written heritage and represent the scientific, cultural and social identity of nations. The manuscript of Kas al-Karam, written by Moinuddin Ahmad ibn Mohsen, nicknamed "Ruh", is numbered 5816 and has 209 pages in the library of Ayatollah Marashi Najafi in Qom. This manuscript belongs to the Safavid period (1056-1107) whose poems are Masnavi and have a mystical content. The text of this version is legible and in Nasta'liq script. The manuscript of Kas al-Karam is a single copy and a selection of mystical poems by poets such as Masnavi Manavi, Hadiqah al-Hafiqa Sanai and Asrarnameh by Farid al-Din Attar Neyshabouri and several other poets. In the poems of the manuscript of Kas al-Karam, ancient Persian words, Arabic words can be seen. The present article uses a descriptive-analytical method and relying on library sources to examine the author's view of the creation of man and love of God. The philosophy of creation of man and the world is one of the most fundamental issues that human beings have always sought to discover and recognize.

Reservoir computing has demonstrated high-level performance, however efficient hardware implementations demand an architecture with minimum system complexity. The authors propose a rotating neuron-based architecture for physically implementing all-analog resource efficient reservoir computing system.

Abstract This research presents the application of glycine amino acid for establishing firm cell‐substrate interaction instead of expensive adhesion proteins, peptides and peptide derivatives. The glycine amino acid is chemically functionalized on the coverslip to achieve self‐assembled nanostructure. Glycine self‐assembly on NaCl treated coverslips is initiated with SiONa+:COO− linkage while their nanostructure is achieved with formation of glycine chain through NH3+:COO− covalent linkage between the adjacent molecules. The functionalization steps are confirmed by Fourier‐transform infrared spectroscopy (FTIR) investigation. The atomic force microscopy (AFM) and scanning electron microscopy (SEM) investigations reveal that glycine growth initiates at 4 Hours (H) post‐treatment while maximum growth appears after 8H‐10H. Both the vertical and horizontal growth of nanostructures show dependence on functionalization periods. Various levels of glycine functionalized surface show different levels of baby hamster kidney (BHK‐21) cell adhesion and proliferation efficiency with maximum performance for 10H functionalized surface. The adhesion and proliferation performance of 10H glycine functionalized surface shows negligible difference when compared with glycine‐aspartic acid (RGD) functionalized surface. Finally, growth curves obtained from both glycine and RGD functionalized surface reveal exponential growth phage up to 48H followed by stationary phage between 48H and 72H while death of many cells appears from 72H to 96H. Thus, this research concluded that glycine functionalized surface is equally effective for cell adhesion and proliferation.

Flexible sensors for hand gesture recognition and human–machine interface (HMI) applications have witnessed tremendous advancements during the last decades. Current state‐of‐the‐art sensors placed on fingers or embedded into gloves are incapable of fully capturing all hand gestures and are often uncomfortable for the wearer. Herein, a flake‐sphere hybrid structure of reduced graphene oxide (rGO) doped with polystyrene (PS) spheres is fabricated to construct the highly sensitive, fast response, and flexible piezoresistive sensor array, which is ultralight in the weight of only 2.8 g and possesses the remarkable curved‐surface conformability. The flexible wrist‐worn device with a five‐sensing array is used to measure pressure distribution around the wrist for accurate and comfortable hand gesture recognition. The intelligent wristband is able to classify 12 hand gestures with 96.33% accuracy for five participants using a machine learning algorithm. To showcase our wristband, a real‐time system is developed to control a robotic hand via the classification results, which further demonstrates the potential of this work for HMI applications.

Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.

Abstract Wearable devices can be used for monitoring vital physical and physiological signs remotely, as well as for interacting with computers. Widespread adoption of wearables is somewhat hindered by the duration time they can be used without re‐recharging. To ensure uninterrupted operation, these devices need a constant and battery‐less energy supply. Scavenging energy from the wearable's surroundings is, therefore, an essential step towards achieving genuinely autonomous and self‐powered devices. While energy harvesting technologies may not completely eliminate the battery storage unit, they can ensure a maximum duration of use. Piezoelectric energy harvesting is a promising and efficient technique to generate electricity for powering wearable devices in response to body movements. Consequently, we systematically survey the range of technologies used for scavenging energy from the human body, with a particular focus on the upper‐limb area. According to our review and in comparison to other upper limb locations, highest power densities can be achieved from piezoelectric transducers located on the wrist. For short and fast battery charging needs, we therefore review the range of materials, architectures and devices used to scavenge energy from these upper‐limb areas. We provide comparisons as well as recommendations and possible future directions for harvesting energy using this promising technique.

Quantum computing promises an exponentially higher computational power than classical computers; although all the building blocks have become available, certain constraints still prevent quantum advantage. The fundamental challenge in building a practical quantum computer is integrating thousands of highly coherent qubits with the control and readout electronics. The need for a high‐coherence qubit drives the effort for quantum error correction algorithms to create fault‐tolerant quantum systems. Error correction becomes tangible in a quantum processor only in large numbers of qubits. Thus, the other challenge is reducing the number of physical interconnects (coaxial lines) between the quantum–classical interface and bulky room‐temperature electronics. To interface thousands of qubits, interconnects can be reduced by bringing the control and readout electronics near the quantum processor. Cryogenic complementary metal–oxide–semiconductor (CMOS) technology has been an ideal candidate for this purpose. Integrated control and readout at cryogenic temperatures require low power dissipation circuit designs and techniques such as frequency‐division multiplexing (FDM) due to the finite cooling power of a dilution refrigerator. Herein, an overview of each building block in a superconducting quantum computer is provided, focusing on scalability. Furthermore, this article is concluded with an outlook discussing current challenges and future directions for the scalable superconducting control and readout.

Reliability and controllability for a new scheme of gate-all-around field effect transistor (GAA-FET) with a silicon channel utilizing a sectorial cross section is evaluated in terms of Ion/Ioff current ratio, transconductance, subthreshold slope, threshold voltage roll-off, and drain induced barrier lowering (DIBL). In addition, the scaling behavior of electronic figures of merit is comprehensively studied with the aid of physical simulations. The electrical characteristic of proposed structure is compared with a circular GAA-FET, which is previously calibrated with an IBM sample at the 22 nm channel length using 3D-TCAD simulations. Our simulation results show that sectorial cross section GAA-FET is a superior structure for controlling short channel effects (SCEs) and to obtain better performance compared to conventional circular cross section counterpart.

Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms. However, these systems lack an extensive large-scale feature extraction (LSFE) analysis through multiple benchmark data sets. LSFE analysis is required to identify and utilize the most relevant features and channels for emotion recognition and ASD prediction. Considering these challenges, for the first time, we have analyzed and evaluated an extensive feature set to select the optimal features using LSFE and feature selection algorithms (FSA). A set of up to eight most suitable channels was identified using different best-case FSA. The subject-wise importance of channels and features is also identified. The proposed method provides the best-case accuracies, precision, and recall of 95, 92, and 90%, respectively, for emotions prediction using a linear support vector machine (LSVM) classifier. It also provides the best-case accuracy, precision, and recall of 100% for ASD classification. This work utilized the largest number of benchmark data sets (5) and subjects (99) for validation reported till now in the literature. The LSVM classification algorithm proposed and utilized in this work has significantly lower complexity than the DNN, convolutional neural network (CNN), Naïve Bayes, and dynamic graph CNN used in recent ASD and emotion prediction systems.

Advancements in the semiconductor industry have enabled wearable devices to be used for a wide range of applications, including personalised healthcare. Novel energy harvesting technologies are therefore necessary to ensure that these devices can be used without interruption. Crystalline silicon photovoltaic (PV) cells provide high energy density to electronic loads. However, the optimization of these cells is a complex task since their optical performance is coupled to the surroundings, while their electrical performance is influenced by the intrinsic PV characteristics and parasitic losses. Without doubt, simulation tools provide the necessary insight to PV cell performance before device fabrication takes place. However, the majority of these tools require expensive licensing fees. Thus, the aim of this article is to review the range of non-commercial PV simulation tools that can be used for wearable applications. We provide a detailed procedure for device modelling and compare the performance of these tools with previously published experimental data. According to our investigations, non-commercial 3D tools such as PC3D provide accurate simulation results that are only ≈1.7% different from their commercial counterpart.

In this paper, we model statistical correlation between overshooting effect and propagation delay time in nano-CMOS technology considering the influence of intrinsic parameter fluctuations caused by discreteness of charge and granularity of matter. The impact of input slew rate, output capacitive load, and supply voltage on this statistical correlation is comprehensively studied. Moreover, we propose two alternative approaches which are capable of reproducing the statistical correlation as well as mean and standard deviation of both propagation delay time and overshoot voltage. We evaluate the accuracy of these alternative approaches against accurate Monte-Carlo simulations. It is shown that the statistical correlations are almost preserved using these alternative approaches.

Implantable and wearable electronic devices can improve the quality of life as well as the life expectancy of many chronically ill patients, provided that certain biological signs can be accurately monitored. Thanks to advancements in packaging and nanofabrication, it is now possible to embed various microelectronic and micromechanical sensors such as gyroscopes, accelerometers, and image sensors into a small area on a flexible substrate and at a relatively low cost. Furthermore, these devices have been integrated with wireless communication technologies to enable the transmission of both signals and energy. However, to ensure that these devices can truly improve a patient’s quality of life, new preventative, diagnostic, and therapeutic devices that can provide hassle-free, long-term, continuous monitoring will need to be developed, which must rely on novel energy harvesting solutions that are non-obstructive to its wearer. So far, research in the field has focused on materials, new processing techniques, and one-off devices. However, existing progress is not sufficient for future electronic devices to be useful in any new application, and a great demand exists toward scaling up the research toward circuits and systems. Few interesting developments in this direction indicate that special attention should be given toward the design, simulation, and modeling of energy harvesting techniques while keeping system integration and power management in consideration.

Electrical neurostimulation has been used successfully as a technique in both research and clinical contexts for over a century. Despite significant progress, inherent problems remain, hence there has been a drive for novel neurostimulation modalities including ultrasonic, magnetic, and optical, which have the potential to be less invasive, have enhanced biointegration, deeper stimulus penetration from the probe, and higher spatiotemporal resolution. Optogenetics—the optical stimulation of genetically photosensitized neurons, enables highly precise genetic targeting of the stimulus. Specifically, it allows for selective optical excitation and inhibition via different wavelengths. As such, optogenetics has become a prominent tool for neuroscience. Herein, the complementarity between different forms of neurostimulation is explored with a focus on cranial magnetic and optogenetic stimulation. Magnetic stimulation is complementary to optogenetics in that it does not require an electrochemical tissue interface like in the case of electrical stimulation. Furthermore, if incorporated onto the same probe as one with light emitters, its stimulation field can be orthogonal to the light emission field—allowing for complementary stimulus fields. Herein, dual optogenetic and magnetic modalities are proposed that can unite to yield a powerful and versatile tool for neural engineering.

Aiming at the self-association feature of the Hopfield neural network, we can reduce the need for extensive sensor training samples during human behavior recognition. For a training algorithm to obtain a general activity feature template with only one time data preprocessing, this work proposes a data preprocessing framework that is suitable for neuromorphic computing. Based on the preprocessing method of the construction matrix and feature extraction, we achieved simplification and improvement in the classification of output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons by constructing a feature matrix, which changed the weights of different categories to classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion process, which helps improve the classification accuracy and avoids falling into the local optimal value caused by single sensor data. Experimental results show that the framework has high classification accuracy with necessary robustness. Using the proposed method, the classification and recognition accuracy of the Hopfield neuromorphic algorithm on the three classes of human activities is 96.3%. Compared with traditional machine learning algorithms, the proposed framework only requires learning samples once to get the feature matrix for human activities, complementing the limited sample databases while improving the classification accuracy.

Abstract Climate change, population growth, urbanization, and interactions thereof may alter the water supply‐demand balance and lead to shifts in water shortage characteristics at different timescales. This study proposes an approach to improve the vulnerability assessments of U.S. river basins to the shortage at the interannual to decadal timescales by characterizing shifts in intensity, duration, and frequency (IDF) of water shortage events from current (1986–2015) to future (2070–2099) periods. The results indicate that under the driest future climate projection, the frequency and intensity of over‐year (D > 12 months) events at the monthly scale and decadal (D > 10 years) events at the annual scale tend to increase in the Southwest, Southern, middle Great Plain, and Great Lakes regions. Conversely, the frequency of interannual (D 1 year) and multi‐year (D > 3 years) events at the annual scale is likely to increase in the West Coast regions. Besides, river basins with a higher rate of aridification are likely to experience more frequent over‐year (D > 12 months) events, while river basins with a decrease in aridification were projected to undergo more frequent interannual (D

This paper presents a high precision adaptive slope compensation circuit for for DC -DC converter in wearable devices. Compared with the traditional adaptive slope compensation circuit, the comparator is used to sample the output voltage and input voltage, which greatly improves the accuracy.ln this paper, the circuit is designed in UMC 0.18-p,m CMOS Technology and verified by Virtuoso Spectre Circuit Simulator. The simulation results show that the accuracy of the adaptive slope compensation circuit in this paper can reach more than 96%.

Compared with von Neumann's computer architecture, neuromorphic systems offer more unique and novel solutions to the artificial intelligence discipline. Inspired by biology, this novel system has implemented the theory of human brain modeling by connecting feigned neurons and synapses to reveal the new neuroscience concepts. Many researchers have vastly invested in neuro-inspired models, algorithms, learning approaches, operation systems for the exploration of the neuromorphic system and have implemented many corresponding applications. Recently, some researchers have demonstrated the capabilities of Hopfield algorithms in some large-scale notable hardware projects and seen significant progression. This paper presents a comprehensive review and focuses extensively on the Hopfield algorithm's model and its potential advancement in new research applications. Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance to build their own artificial intelligence projects.

To ensure maximum comfort for the wearer, electronic components that include energy harvesters need to be mechanically conformable. In this context, we demonstrate a versatile, cost-effective and efficient method for fabricating graphene supercapacitor electrodes using Laser Induced Graphene (LIG). A CO2 laser beam instantly transforms the irradiated polyethersulfone polymer (PES) into a highly porous carbon structure. The LIG method was used to deposit graphene layers on graphite sheets to produce the supercapacitor electrodes. Graphene formation and morphology were examined and confirmed using several techniques including Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX) spectroscopy, Raman Spectroscopy and Fourier transform infrared spectroscopy (FTIR). Moreover, the electrochemical characterization was performed in different electrolytes (NaOH and KOH). At 5 mV s-1, the LIG electrode achieved 165 mF cm-2 and 250 mF cm-2 in NaOH and KOH electrolytes, respectively. Consequently, we show that a wearable symmetric supercapacitor device with LIG electrodes achieved 98.5 mF cm-2 at 5 mV s-1 in KOH electrolyte. The device demonstrated an energy density of 11.3 μWh.cm-2 with power density of 0.33 mWcm-2 at 0.5 mA cm-2. The retention of capacitance was 75% after 2000 cycles, with outstanding performance for the comparable graphene-based electrodes. These results further validate the use of LIG for developing flexible energy harvesters for wearable applications.

Further to China’s plan that was introduced in 2017 for attracting more students into engineering, many Chinese universities have started to explore new teaching methods that can be adopted into their programs. This shift was geared towards developing student-centred teaching materials rather than traditional teacher centred instruction. In this manuscript, we compare two different methods of instruction for a course on energy harvesting using embedded systems. We describe the learning materials and showcase the impact that project-based learning has had on a cohort of Chinese students that were enrolled in a joint master’s program between the University of Electronic Science and Technology of China (UESTC) and the Royal Institute of Technology (KTH). KTH has made remarkable progress in the teaching of embedded systems technology for energy harvesting applications, with great emphasis on active as well as collaborative learning. We demonstrate two examples of projects that Chinese students have completed in KTH and present evaluative data regarding their experiences. Our results show that KTH’s approach in teaching this module has had a positive impact on student learning, with an average of 80% of students think that teaching in KTH is conducive to students’ independent exploration.

This paper proposes a 16-bit 6-channel high-voltage successive approximation register (SAR) ADC with an optimized 5 + 5 + 6 segmented capacitor array. The lower 10 bits of the capacitor array are all composed of unit capacitors without any calibration unit. Without calibration, the lower 10 bits of the capacitor array can ensure 10-bit conversion accuracy. Every of the upper 6 bits of the capacitor array contains a linearity calibration unit. The linearity error of the upper 6 bits is calibrated by the linearity calibration unit. The 16-bit is manufactured by a $0.6~\mu m$ standard COMS process, and the total chip area of 6-channel ADC including pads is 6.6mm $\times6.6$ mm. As for single channel SAR ADC, the area is 0.9mm $\times2.0$ mm. The measurement results show that the effective conversion accuracy of the SAR ADC reaches 13 bits by using novel differential nonlinearity (DNL) and integral nonlinearity (INL) calibration methods. The power is 80mW, corresponding to a Figure of Merit (FOM) of 48 pJ/conv.-step.

Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

Reservoir expansion is commonly considered an adaptation strategy to attenuate water shortage conditions. In many locations in the United States, there are ongoing discussions about the effectiveness and feasibility of reservoir expansion with regard to the growing drought conditions and a consequent significant decrease in surface water. This study investigates if the expansion of the existing Unites States reservoirs should be still considered an effective and adequate management solution to cope with water shortages. To this end, we have defined three reservoir expansion metrics to assess the efficiency, feasibility, and usefulness of increasing the storage capacity of 304 reservoirs across the conterminous United States (CONUS). The efficiency metric is defined as the ratio of reservoir average storage to maximum active storage. The feasibility metric is defined as the ratio of reservoir average annual inflow to maximum active storage and the usefulness metric is described as the ratio of the reservoir average annual excess inflow (average annual inflow–maximum active storage) to the average intensity of water shortages. The finding indicates that most reservoirs in Colorado and Utah currently have high or very high efficiency metrics meaning that these reservoirs are, on average, more than half full while most reservoirs in Texas have low or medium efficiency metrics indicating that these reservoirs are, on average, less than half full. Additionally, the feasibility metrics indicate that reservoir expansion in most western and southern states may not be fruitful because the average annual inflow to reservoirs is less than their maximum active storage over the historical period. Nevertheless, the usefulness metrics show that reservoir expansion can be a useful adaptation strategy to mitigate or attenuate water shortages for some reservoirs in California and Colorado while it cannot considerably decrease the intensity of water shortages in Texas. Findings from this study highlight the utility of the assessment of reservoir expansion at a regional scale considering both available freshwater as an input to reservoirs and the potential water shortage conditions as the main trigger.

While urban areas are being threatened by water shortage due to climate change and rapid population growth, effects of urban development patterns on future municipal water shortage are rarely investigated. We address this aspect of urbanization by assessing the impacts of sprawl vs. high-density patterns on future changes in the sub-annual water shortage intensity-duration-frequency (IDF) relationships. The City of Fort Collins, Colorado, water supply system is chosen as a representative region that is rapidly developing over the last decades. The future water supply is estimated using the Soil and Water Assessment Tool (SWAT) driven with a hot-dry climate model from the statistically downscaled Coupled Model Intercomparison Project, phase 5 (CMIP5) projections. Future water demand is projected using the Integrated Urban Water Model (IUWM) under both sprawl and high-density development patterns. The demonstration study reveals that urban areas under the sprawl development pattern are likely to experience water shortage events with higher intensity, duration, and frequency compared to the high-density pattern. Characterizing impacts of urban development patterns on future water shortage conditions is required for sustainable water management and smart urban growth and can help urban planners and water managers to develop an adaptive path to meet future water demand and decrease the vulnerability of municipal water supply systems to shortage.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Abstract Characterization of shifts in regional hydroclimatic conditions helps reduce negative consequences on agriculture, environment, economy, society, and ecosystem. This study assesses shifts in regional hydroclimatic conditions across the conterminous United States in response to climate change over the 21st century. The hydrological responses of five downscaled climate models from the Multivariate Adaptive Constructed Analogs data set ranging from the driest to wettest and least warm to hottest were simulated using the variable infiltration capacity (VIC) model. Shifts in regional hydroclimatic conditions at 8‐digit hydrologic unit scale (HUC8) were evaluated by the magnitude and direction of movements in the Budyko space. HUC8 river basins were then clustered into seven unique hydroclimatic behavior groups using the K‐means method. A tree classification method was proposed to illustrate the relationships between hydroclimatic behavior groups and regional characteristics. The results indicate that hydroclimatic responses may vary from a river basin to another, but basins in the same neighborhood follow a similar movement in the Budyko space. The systematic hydroclimatic behavior of river basins is highly associated with their regional landform, climate, and ecosystem characteristics. Most HUC8s with mountain, plateau, and basin landform types will likely experience less arid conditions. However, most HUC8s with Plain landform type behave differently according to the regional ecosystem and climate. This study provides a potential roadmap of shifts in regional hydroclimatic conditions of U.S. river basins, which can be used to improve regional preparedness and ability of various sectors to mitigate or adapt to the impacts of future hydroclimate change.

Density functional theory (DFT) has been used to investigate doped armchair germanene nanoribbons (AGeNRs) doped by low-concentration metallic atoms (Pt, Ag, Au, In and Sn). The structural stability and electronic properties of these doped nano-structures have been analyzed. The formation energy of the examined ribbons shows that they are thermodynamically stable. Examination of E-k band structures and density of state (DOS) has shown that depending on the type of metal atom, different energy bands can be seen around the Fermi level. Doping of the nano-ribbon by Pt and Sn in N = 7 only reduces the band gap compared to the pristine structure and the nano-ribbon stays semiconducting. However replacing the In, Ag, and Au atoms in AGeNR leads to the semiconducting-metal transition. Moreover, metallic doping of the ribbon in N = 8, yields an increase of the band gap and a transfer is observed from metal to semiconductor.

Circuit techniques, sensors, antennas and communications systems are envisioned to help build new technologies over the next several years. Advances in the development and implementation of such technologies have already shown us their unique potential in realizing next-generation sensing systems. Applications include wearable consumer electronics, healthcare monitoring systems, and soft robotics, as well as wireless implants. There have been some interesting developments in the areas of circuits and systems, involving studies related to low-power electronics, wireless sensor networks, wearable circuit behaviour, security, real-time monitoring, connectivity of sensors, and Internet of Things (IoT). The direction for the current technology is electronics systems on large area electronics, integrated implantable systems and wearable sensors. So far, the research in the field has focused on materials, new processing techniques and one-off devices, such as diodes and transistors. However, current technology is not sufficient for future electronics to be useful in new applications; a great demand exists to scale up the research towards circuits and systems. Recent developments indicate that, in addition to fabrication technology, special attention should also be given to design, simulation and modeling of electronics, while keeping sensing system integration, power management, and sensors network under consideration.

Electronic contact lenses are used for noninvasively monitoring vital human signs and medical parameters. However, maintaining a secure communications connection and a self‐sustainable power source are still looming challenges. Herein, a proof‐of‐concept electronic contact lens is demonstrated that includes a spiral antenna with its wireless circuit unit for data telemetry, a rectifier circuit for power conditioning, and a micro‐light‐emitting diode (μLED) as a load. The spiral antenna with its rectifying circuit is designed considering operation in the industrial, scientific, and medical (ISM) band of 2.4 GHz. The spiral coil with an inner diameter of 10 mm, an outer diameter of 12 mm, and a wire width of 0.2 mm is fabricated on a donut‐shaped flexible polyimide substrate. For biocompatibility purposes, polyimide is used as the contact lens substrate and polydimethylsiloxane (PDMS) is used for encapsulation. A 3D‐printed eye model is developed for accurately shaping the curvature of the PDMS‐encapsulated contact lens. The reflection coefficient (S11) of the fabricated antenna is tested in different conditions and on an eye model to mimic the liquid condition of the human eye. In a wide range of conditions, a minimum of −20 dB reflection coefficient (S11) is obtained.

In this paper, we consider the electrical performance of a circular cross section gate all around-field effect transistor (GAA-FET) in which gate dielectric coverage with high-k dielectric (HfO2) over the channel region has been varied. Our simulations show the fact that as high-k dielectric coverage over the channel increases, ION/IOFF ratio and transconductance over drain current (gm/ID) will be enhanced. Moreover, we investigate the impact of channel length scaling on these devices. The obtained results show that subthreshold slope (SS), drain induced barrier lowering (DIBL) and threshold voltage (VTH) roll-off will be reduced as a result of scaling. In this work TCAD simulator was concisely calibrated against experimental data of a GAA-FET from IBM. The Schrödinger equation is solved in the transverse direction and quantum mechanical confinement effects are taken into account.

The single electron transistor (SET) is a nanoscale switching device with a simple equivalent circuit. It can work very fast as it is based on the tunneling of single electrons. Its nanostructure contains a quantum dot island whose material impacts on the device operation. Carbon allotropes such as fullerene (C60), carbon nanotubes (CNTs) and graphene nanoscrolls (GNSs) can be utilized as the quantum dot island in SETs. In this study, multiple quantum dot islands such as GNS-CNT and GNS-C60 are utilized in SET devices. The currents of two counterpart devices are modeled and analyzed. The impacts of important parameters such as temperature and applied gate voltage on the current of two SETs are investigated using proposed mathematical models. Moreover, the impacts of CNT length, fullerene diameter, GNS length, and GNS spiral length and number of turns on the SET’s current are explored. Additionally, the Coulomb blockade ranges (CB) of the two SETs are compared. The results reveal that the GNS-CNT SET has a lower Coulomb blockade range and a higher current than the GNS-C60 SET. Their charge stability diagrams indicate that the GNS-CNT SET has smaller Coulomb diamond areas, zero-current regions, and zero-conductance regions than the GNS-C60 SET.

This paper presents a statistics-optimized organization technique to achieve better element matching in successive approximation register (SAR) analog-to-digital converter (ADC) in smart sensor systems. We demonstrate the proposed technique ability to achieve a significant improvement of around 23 dB on a spurious free dynamic range (SFDR) of the ADC than the conventional, testing with a capacitor mismatch σu = 0.2% in a 14-bit SAR ADC system. For the static performance, the max root mean square (rms) value of differential nonlinearity reduces from 1.63 to 0.20 LSB and the max rms value of integral nonlinearity (INL) reduces from 2.10 to 0.21 LSB. In addition, it is demonstrated that by applying grouping optimization and strategy optimization, the performance boosting on the SFDR can be effectively achieved. Such a great improvement on the resolution of the ADC only requires an off-line pre-processing digital part.

This paper presents a novel inductor-less switched capacitor (SC) DC-DC converter, which generates simultaneous dual-output voltages for implantable electronic devices. Present dual output converters are limited to fixed ratio gain, which degrade conversion efficiency when the input voltage changes. The proposed power converter offers both step-up and step-down conversion with 4-phase reconfigurable logic. With an input voltage of 1 V provided by photovoltaic (PV) cells, the proposed converter achieves step-up, step-down, and synchronised voltage conversions in four gain modes. These are 1.5 and 0.5 V for normal mode, 2 and 1 V for high mode, 2 V for double boost mode, as well as 3 and 2 V for super boost mode with the ripple variation of 14-59 mV. The converter circuit has been simulated in standard 0.18-μm CMOS technology and the results agree with the state-of-the-art SC converters. However, our proposed monolithically integrated PV powered circuit achieves a conversion efficiency of 85.26% and provides an extra flexibility in terms of gain, which is advantageous for future implantable applications that have a range of inputs. This research is, therefore, an important step in achieving truly autonomous implantable electronic devices.

In this paper, we estimate the value at risk of Tehran stock exchange (TSE) index by using GARCH family models in short and long trading positions. Because of asymmetric behavior of returns for long and short positions in TSE, for enhanced accuracy of model, we apply asymmetric normal and t-student distribution functions. By developing Sener et. al (2012) measurement for considering trading positions in performance assessment of parametric models, we show that EGARCH and GJRGARCH models with asymmetric normal and t-student distribution functions are more accurate than other models. Also complementary forecast ability test explain that, with a benchmark model such as GJRGARCH, other models do not have equal mean error, so the asymmetric distribution functions in EGARCH and GJRGARCH models improve their ranks.

Due to the rapid development of technology and emerging of severe competitive environments, the main objective of decentralized distribution networks operated by different providers is to maximize their profits in the network. One of the key tools to improve network performance in distribution networks is game theory and cooperative games. The main objective of this study is to investigate the role of small and medium industries in the development of the country's economic activity and to achieve a collaborative model to organize decentralized networks effectively. This makes these industries consider their main variables such as capacity, time and cost as important elements in today's competitive environments and try to increase their service levels to customers and promote the products according to customers' demand .On the other hand, in the real world due to various reasons such as sanctions, politics issues, earthquake, and war, time, the amount of flow, and cost are changed. Thus, to overcome unforeseen issues, it is important for suppliers to know how to cooperate to gain more payoffs in the network in order. In this study a mathematical problem for these industries is provided that considers various scenarios in uncertain situations. Then, to explain and demonstrate the applicability of the model, three numerical examples in these industries are discussed and the performances of cooperative and non-cooperative approaches are compared. The results show that cooperative games lead to an increase in players' payoffs and the utility function has boosted the effectiveness of cooperative.

This paper presents the electrical characteristics of a short channel Silicon on Insulator (SOI) transistor with a graphene layer. The graphene sheet is used at the bottom of the channel close to the source side and a proportionally heavily p-type retrograde doping implanted in nearly middle of the channel. To increase the gate electrostatic control over the channel we incorporated a high-K material i.e. HfO2 as the gate oxide insulator. Due to Graphene growth and Retrograde Doping in the Channel, we called this structure “GRDC-SOI” transistor. Because graphene sheet has low band gap and high mobility, we used it to increase the on-state current. Engineered p-type retrograde doping utilized for both decreasing off-state current and increasing on-state current. These dopants cause impurity scattering in the depth of the channel and deflect electron movements and decrease off-current. On the other hand, these dopants which are located almost in the middle of the channel can play the role of base in an NPN Bipolar Junction Transistor (BJT), and turn it on and exceed the on-state current. An immense comparison among our proposed device and a device similar to GRDC-SOI but without Graphene sheet (RDC-SOI) and a conventional structure shows that our proposed device has superior electrical characteristics in terms of ION/IOFF ratio, transconductance, subthreshold slope, leakage current, breakdown voltage and short channel effects like hot carriers injection and DIBL. Our analyses demonstrate that GRDC-SOI transistor can open a window for utilizing Graphene material in digital circuits and system on chip applications. Keywords: Graphene, SOI transistor, Retrograde doping, High-K material

This study presents preliminary results about the multi-sensory recognition of indoor daily activities and fall detection, to monitor the well-being of older people at risk of physical and cognitive chronic health conditions. Five different sensors, continuous wave (CW) radar, frequency-modulated CW (FMCW) radar, and inertial measurement unit comprising an accelerometer, gyroscope, and magnetometer were used to simultaneously collect data from 20 subjects performing 10 activities. Rather than using all of the available sensors, it is more efficient and economical to select part of them to maximise the classification accuracy and avoid unnecessary computation to process information if it is not salient. Each individual sensor and several sensor combinations are trained with a quadratic-kernel support vector machine classifier. In addition, they are validated with an improved statistical approach, which uses data from unknown participants to test model rather than random cross-validation to verify if the model generalises well for unknown subjects. Furthermore, the most suitable sensor combinations are derived for each specific group of tested subjects selected (e.g. the oldest, youngest, tallest, and shortest sub-groups of participants out of the entire group).