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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.

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.

The study and measurement of the magnetic field from the skeletal muscle is called Magnetomyography (MMG). These magnetic fields are produced by the same ion currents which give rise to the electrical signals that are recorded with electromyography (EMG). For non-invasive measurements, the electric properties of subcutaneous tissue, i.e., most importantly, have a strong influence on the recorded signals. This paper presents a computational model to study the volume conduction effect with the finite-difference time-domain simulations using Sim4Life. The effects of 1 mm fat on the recorded electrical and magnetic signals from the skin surface have been evaluated in both EMG and MMG. The results indicate that due to 1 mm fat, the electrical signals decrease over 60% through traveling across layers between the muscle and skin surface, while these layers are transparent to the magnetic field. In a similar simulation procedure, when the new fibers are recruited, the interference among electrical signals makes the strength of recorded signals behave non-linearly proportional to the increasing number of active muscle fibers. Sim4Life simulations show that the recorded magnetic signals do not have the same trajectory as electrical signals. Hence, the changes in EMG signals caused by volume conduction effect can result in signal misinterpretations.

Electroencephalogram (EEG) play a vital role in the prediction of neurological disorders including epilepsy, narcolepsy, migraine, etc. The impedance between the electrodes and the skin interface should be within specific range in order to acquire good quality EEG signals. This work focuses on an intelligent, wearable device capable of measuring electrode-skin interface impedance (ESI) in parallel to the EEG acquisition in non-invasive manner. The proposed device includes an analog front end (AFE), back-end micro-controller (BEM), and ESI measuring unit. A novel technique is used for measurement of ESI by connecting known value resistances in parallel at the input terminals of the low noise amplifier (LNA) of AFE and measuring the change in the output signal. This unique technique is suitable in wearable medical devices as it is safe to use, results in high-quality EEG signals, and consumes negligible power. A PCB prototype is developed using the available commercial components. The designed prototype has a small form factor (52mm x 53mm), is lightweight, and easily behind the ear wearable.

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

This paper proposes a multi-sensing Human Activity Recognition framework, which uses Neuromorphic computing to processing from Sensors and Radars of different type signals for data fusion and classification. At this point, Inertial Measurement Unit sensors and Universal Software-defined Radio Peripheral, and Radar devices are used to collect human activities signals separately. The feature extraction and selection process the sensors signal to dimension reduction without time factor by design an attention mechanism. And then, following Expectation-Maximization calculation to achieve a binary feature pattern that fits the discrete Hopfield neural network input. Depend on the Neuromorphic computing of associative memory function and similarity calculation to the neurons’ feedback output. It finally achieves human activity recognition with one-shot learning. There are explores multi-sensing human activity recognition between limited dataset and ensures accuracy without dropping. The technique can be extended to include more hardware signal processing to the system.

A versatile lossy mode resonance (LMR) sensor is developed for Epinephrine monitoring based on the state-of-the-art molecular imprinting technique over an optical fiber. The optical absorption concept of the LMR-based sensor is proven by incorporating an amino silane-functionalized graphene oxide (SiO2-NH2@GO) as an adhesive layer for grafting Epinephrine imprinting polydopamine (Ep-IPDA) as a sensing layer onto the optical fiber curved surface. The accurate structural and morphological characterization confirmed the rough crystalline and spherical particles for Ep-IPDA and SiO2-NH2-GO onto optical fiber curved surface, while spectroscopic analysis confirms the formation of imprinting polymer and desirable absorbance characteristics. The optimized probe exhibits an excellent performance with the maximum sensitivity within two intervals of 0.3-1 µM and 1-90 µM along with the linearity coefficient of R2= 0.99 possessing a low limit of detection of 0.07 µM. Additionally, this sensor selectively detects Ep in the presence of other species and showed good recovery in human urine and injection samples. This work opens a new avenue for developing bio-inspired LMR-based optical fiber biosensors and could be further extended to detect other catecholamine neurotransmitters.

In this work, a different insight into the absorption characteristics of the chalcogenide thin film over the visible-wavelength range is studied in detailed. A comparative study of the lossy mode resonance (LMR) dips stem from the evanescent-wave absorption is theoretically and experimentally investigated towards refractive index measurement in aqueous environment. The absorbing layer of thermally evaporated arsenic trisulfide chalcogenide (As2S3) thin film coated onto the low OH etched optical fiber (ChOF) is prepared. X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy, were used to characterize the chemical species, the crystal structure, and the morphology of the degradation layer confirming the As2S3, thin-film deposition on the curved optical fiber surface. The LMR dips condition hinging on the adoption of the chalcogenide layer was comprehensively explored by referring to the absorption spectra as a function of the wavelength and its thicknesses. Taking the advantage of the chalcogenide absorptive features within the visible spectral range, the results verified that coating even a thin subwavelength thickness of As2S3 chalcogenide on a bare has the ability to provide liquid refractometer with a versatile absorbing features. Our results point to a new path for the development of LMR-based chemical sensors, environmental sensors, or to be combined to the integrated micro-photonics device biosensors.

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.

MagnetoMyoGraphy (MMG) is a method of studying muscle function via weak magnetic fields generated from human active organs and tissues. The correspondence between MMG and electromyography means directly derived from the Maxwell-Amp\`ere law. Here, upon briefly describing the principles of voltage distribution inside skeletal muscles due to the electrical stimulation, we provide a protocol to determine the effects of the magnetic field generated from a time-changing action potential propagating in a group of skeletal muscle cells. The position-dependent and the magnetic field behaviour on account of the different currents in muscle fibres are performed in temporal, spectral and spatial domains. The procedure covers identification of the fibre subpopulations inside the fascicles of a given nerve section, characterization of soleus skeletal muscle currents, check of axial intracellular currents, calculation of the generated magnetic field ultimately. We expect this protocol to take approximately 2-3 hours to complete for the whole finite-element analysis., Comment: To be submitted to Nature Protocols. 37 Pages and includes 10 figures

Spintronics, the use of spin of an electron instead of its charge, has received huge attention from research communities for different applications including memory, interconnects, logic implementation, neuromorphic computing, and many other applications. Here, in this paper, we review the works within spintronics, more specifically on spin-orbit torque (SOT) within different research groups. We also provide researchers an insight into the future potentials of the SOT-based designs. This comprehensive review paper covers different aspects of SOT-based design from device and circuit to architecture level as well as more ambitious and futuristic applications of such technology., Comment: 17 pages, 13 figures

This paper presents the technological advancements in the field of flexible magnetic sensors for robotics applications. Various magnetic devices (e.g. Hall, GMR, AMR and TMR) have been studied and their suitability for flexible application has been presented. Further, the system level integration of magnetic sensors in robotics is briefly discussed. With rapid development in flexible electronics, a robot with multi-functional conformable electronic skin will be possible in the foreseeable future. This will also open new avenues for a wide range of other applications including wearable electronics and interactive electronic-skin for robots and prosthesis.

This paper presents a flexible electronic skin (e-skin) module developed by heterogeneous integration of graphene based touch sensors and CMOS chips having basic capacitance-to-voltage converter (CVC) circuitry to acquire the sensor response. Graphene touch sensor was firstly transfer printed on a flexible polyvinyl chloride (PVC) substrate, thereafter the CMOS chip with 4×4 array of large transistors was integrated on the same PVC substrate. Then, resulting module was tested to verify the performance. The heterogeneous integration of graphene and Si based devices on the same flexible substrate is an interesting new approach that can be scaled up, opening a new opportunity for obtaining large area e-skin for robotic applications.

This paper investigates high achieving and low achieving students’ perception of factors of importance in their achievements and failures in English course in Ardebil city. To accomplish the purpose cluster random sampling was utilized and with 324 students (181 males & 134 female students) through a researcher-made questionnaire. Using t-tests and ANOVA, the items of the questionnaire were analyzed. The findings revealed that from the high and low achieving students’ points of view the following factors were believed to be more influential in high school students’ low achievement: factors related to the content, teachers-based factors, factors related to the instructional instruments, factors related to the family, and the students-based factors. It was also shown that between the low achieving and high achieving students there were statistically significant differences in terms of their perceptions of the student-based factors (p= 0.015) and factors related to the family (p=0.014).

The current study aimed to investigate and explore the challenges of English language teaching in Iran from Ardebil students’ points of view and bring about opportunities to call out the students’ opinions and consequently make some reforms in English language teaching based on their needs. Cluster random sampling was utilized and the participants of the study were 324 students (181 males & 134 female students). The required data were gathered during the fall term of 2012 through a researcher-made questionnaire. It consisted of 5 open-ended questions which investigated the students’ perceptions of the efficiency and deficiency of English language teaching in Iran and required them to give some suggestions on how to improve the present situation of English language teaching in Iran in order to take more advantages of it. The instrument was first piloted before it was administered. The open-ended items were analyzed quantitatively and qualitatively and their results suggested that from students’ perspectives, English language is important in all aspects of life and it is the most important language of the world; however, we cannot take the most advantage of it due to numerous problems and we should have some major and minor changes in different aspects it in order to take more advantage of it.