Nanosystems, Edge Computing, and the Next Generation Computing Systems

Breaking barriers: The potential of nanosystems in antituberculosis therapy

Tuberculosis (TB), caused by Mycobacterium tuberculosis, continues to pose a significant threat to global health. The resilience of TB is amplified by a myriad of physical, biological, and biopharmaceutical barriers that challenge conventional therapeutic approaches. This review navigates the intricate landscape of TB treatment, from the stealth of latent infections and the strength of granuloma formations to the daunting specters of drug resistance and altered gene expression. Amidst these challenges, traditional therapies often fail, contending with inconsistent bioavailability, prolonged treatment regimens, and socioeconomic burdens. Nanoscale Drug Delivery Systems (NDDSs) emerge as a promising beacon, ready to overcome these barriers, offering better drug targeting and improved patient adherence. Through a critical approach, we evaluate a spectrum of nanosystems and their efficacy against MTB both in vitro and in vivo. This review advocates for the intensification of research in NDDSs, heralding their potential to reshape the contours of global TB treatment strategies.

Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams

Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility's control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.

Sensing of Catecholamine in Human Urine Using a Simple Colorimetric Assay Based on Direct Melanochrome and Indolequinone Formation

We used the first enzyme-free synthesis and stabilization of soluble melanochrome (MC) and 5,6-indolequinone (IQ) derived from levodopa (LD), dopamine (DA), and norepinephrine (NE) oxidation to develop a simple colorimetric assay for catecholamine detection in human urine, also elucidating the time-dependent formation and molecular weight of MC and IQ using UV-Vis spectroscopy and mass spectrometry. The quantitative detection of LD and DA was achieved in human urine using MC as a selective colorimetric reporter to demonstrate the potential assay applicability in a matrix of interest in therapeutic drug monitoring (TDM) and in clinical chemistry. The assay showed a linear dynamic range between 5.0 mg L^-1 and 50.0 mg L^-1, covering the concentration range of DA and LD found in urine samples from, e.g., Parkinson's patients undergoing LD-based pharmacological therapy. The data reproducibility in the real matrix was very good within this concentration range (RSD_av% 3.7% and 6.1% for DA and LD, respectively), also showing very good analytical performances with the limits of detection of 3.69 ± 0.17 mg L^-1 and 2.51 ± 0.08 mg L^-1 for DA and LD, respectively, thus paving the way for the effective and non-invasive monitoring of dopamine and levodopa in urine from patients during TDM in Parkinson's disease.

Dynamics of Quantum Networks in Noisy Environments

Noise exists inherently in realistic quantum systems and affects the evolution of quantum systems. We investigate the dynamics of quantum networks in noisy environments by using the fidelity of the quantum evolved states and the classical percolation theory. We propose an analytical framework that allows us to characterize the stability of quantum networks in terms of quantum noises and network topologies. The calculation results of the framework determine the maximal time that quantum networks with different network topologies can maintain the ability to communicate under noise. We demonstrate the results of the framework through examples of specific graphs under amplitude damping and phase damping noises. We further consider the capacity of the quantum network in a noisy environment according to the proposed framework. The analytical framework helps us better understand the evolution time of a quantum network and provides a reference for designing large quantum networks.

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing-an emerging paradigm to overcome the shortcomings of cloud-based computing-could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator-a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

4th Order LC-Based Sigma Delta Modulators

Due to the characteristic of narrow band conversion around a central radio frequency, the Sigma Delta Modulator (ΣΔM) based on LC resonators is a suitable option for use in Software-Defined Radio (SDR). However, some aspects of the topologies described in the state-of-the-art, such as noise and nonlinear sources, affect the performance of ΣΔM. This paper presents the design methodology of three high-order LC-Based single-block Sigma Delta Modulators. The method is based on the equivalence between continuous time and discrete time loop gain using a Finite Impulse Response Digital-to-Analog Converter (FIRDAC) through a numerical approach to defining the coefficients. The continuous bandpass LC ΣΔM simulations are performed at a center frequency of 432 MHz and a sampling frequency of 1.72 GHz. To the proposed modulators a maximum Signal-to-Noise Ratio (SNR) of 51.39 dB, 48.48 dB, and 46.50 dB in a 4 MHz bandwidth was achieved to respectively 4th Order Gm-LC ΣΔM, 4th Order Magnetically Coupled ΣΔM and 4th Order Capacitively Coupled ΣΔM.

Sensitivity Enhancement of Hybrid Two-Dimensional Nanomaterials-Based Surface Plasmon Resonance Biosensor

In this work, we designed structures based on copper nanosubstrate with graphene and two-dimensional transition metal dichalcogenides (TMDC) in order to achieve an ultrasensitive surface plasmon resonance biosensor. This system contains seven components: SF11 triangular prism, BK-7 glass, Chromium (Cr) adhesion layer, thin copper film, layers of one of the types of transition metal dichalcogenides: MoS₂, MoSe₂, WS₂ or WSe₂ (defined as MX₂), graphene, sensing layer with biomolecular analyte. Copper was chosen as a plasmonic material because it has a higher conductivity than gold which is commonly used in plasmonic sensors. Moreover, copper is a cheap and widespread material that is easy to produce on a large scale. We have carried out both theoretical and numerical sensitivity calculations of these kinds of structures using the Goos-Hänchen (GH) shift method. GH shift is lateral position displacement of the p-polarized reflected beam from a boundary of two media having different indices of refraction under total internal reflection condition and its value can be retrieved from the phase change of the beam. The SPR signal based on the GH shift is much more sensitive compared to other methods, including angular and wavelength scanning, due to much more abrupt phase change of the SPR reflected light than its intensity ones. By optimizing the parameters of the SPR sensing substrate, such as thickness of copper, number of layers of 2D materials and excitation wavelength, we theoretically showed an enhanced sensitivity with a detection limit 10^-9 refractive index unit (RIU).

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

Finger Bending Sensing Based on Series-Connected Fiber Bragg Gratings

Smart wearable devices are occupying an increasingly important position in scientific research and people's life fields. As an indispensable component of smart wearable devices, sensors play a crucial role in their sensing and feedback capabilities. In this paper, we investigate the bending gesture sensing for the most dexterous part of human anatomy, the finger. Based on series-connected fiber Bragg gratings (FBGs), recognition of finger bending posture is achieved by MATLAB modeling and the cubic spline interpolation.

Measurement of Film-Elastomer Interface Adhesion by Continuous Buckling

Measurement of interfacial properties between thin films and elastomers is investigated. As a prototype, the interface adhesion between a graphite nanoflake and an elastic polymer is determined by topography imaging of the induced graphite buckles using atomic force microscopy. A theoretical analysis is carried out to establish the relationship among interface adhesion, elastic strain energy, and buckle surface area. The strain energy of the graphite is obtained by employing an elastic plate deflection theory. To introduce the buckles, different methods are applied, including thermal contraction, bending, and stretching, and different substrate materials, namely, polydimethylsiloxane and polystyrene, are used. The uncertainty in measuring the interface adhesion is discussed. These investigations provide a promising approach to characterize the interfacial properties of multilayer samples.

Enhanced Sensitivity and Detection of Near-Infrared Refractive Index Sensor with Plasmonic Multilayers

In this work, the multilayer of the surface plasmon resonance (SPR) sensor was optimized to achieve the maximum sensor sensitivity. By optimizing the thickness of the silver layer (Ag) and dielectric films (TiO2 and AlAs), the optimum sensitivity of the SPR sensor could be obtained. The performance of the SPR sensor proposed was compared with control simulations utilizing zinc oxide (ZnO) and molybdenum oxide (MoO3). The numerical results indicate that the figure-of-merits (FOM) of the SPR sensor was achieved around 150/RIU, corresponding to the sensor sensitivity of 162.79°/RIU with the optimized thicknesses of the TiO2, Ag, and AlAs layers of 140 nm, 60 nm, and 25 nm, respectively. This refractive index sensor shows the FOM to have high detection accuracy and high sensitivity that lead to finding potential application in bio-chemical detection with a small volume of liquid used in biological diagnosis.

Integrated non-volatile plasmonic switches based on phase-change-materials and their application to plasmonic logic circuits

Integrated photonic devices or circuits that can execute both optical computation and optical data storage are considered as the building blocks for photonic computations beyond the von Neumann architecture. Here, we present non-volatile hybrid electro-optic plasmonic switches as well as novel architectures of non-volatile combinational and sequential logic circuits. The electro-optic switches consist of a plasmonic waveguide having a thin layer of a phase-change-material (PCM). The optical losses in the waveguide are controlled by changing the phase of the PCM from amorphous to crystalline and vice versa. The phase transition process in the PCM can be realized by electrical threshold switching or thermal conduction heating via external electrical heaters or the plasmonic waveguide metal itself as an integrated heater. We have demonstrated that all logic gates, a half adder circuit, as well as sequential circuits can be implemented using the plasmonic switches as the active elements. Moreover, the designs of the plasmonic switches and the logic operations show minimum extinction ratios greater than 20 dB, compact designs, low operating power, and high-speed operations. We combine photonics, plasmonics and electronics on the same platform to design an effective architecture for logic operations.

In-Line Mach-Zehnder Interferometers Based on a Capillary Hollow-Core Fiber Using Vernier Effect for a Highly Sensitive Temperature Sensor

In this paper, we propose a highly sensitive temperature sensor based on two cascaded Mach-Zehnder interferometers (MZIs) that work using the Vernier effect. The all-fiber MZIs were assembled by splicing a segment of capillary hollow-core fiber (CHCF) between two sections of multimode fibers (MMFs). This cascaded configuration exhibits a temperature sensitivity of 1.964 nm/°C in a range from 10 to 70 °C, which is ~67.03 times higher than the sensitivity of the single MZI. Moreover, this device exhibits a high-temperature resolution of 0.0153 °C. A numerical analysis was carried out to estimate the devices' temperature sensitivity and calculate the magnification of the sensitivity produced by the Vernier effect. The numerical results have an excellent agreement with the experimental results and provide a better insight into the working principle of the MZI devices. The sensor's performance, small size, and easy fabrication make us believe that it is an attractive candidate for temperature measurement in biological applications.

Optical Machine Learning Using Time-Lens Deep Neural NetWorks

As a high-throughput data analysis technique, photon time stretching (PTS) is widely used in the monitoring of rare events such as cancer cells, rough waves, and the study of electronic and optical transient dynamics. The PTS technology relies on high-speed data collection, and the large amount of data generated poses a challenge to data storage and real-time processing. Therefore, how to use compatible optical methods to filter and process data in advance is particularly important. The time-lens proposed, based on the duality of time and space as an important data processing method derived from PTS, achieves imaging of time signals by controlling the phase information of the timing signals. In this paper, an optical neural network based on the time-lens (TL-ONN) is proposed, which applies the time-lens to the layer algorithm of the neural network to realize the forward transmission of one-dimensional data. The recognition function of this optical neural network for speech information is verified by simulation, and the test recognition accuracy reaches 95.35%. This architecture can be applied to feature extraction and classification, and is expected to be a breakthrough in detecting rare events such as cancer cell identification and screening.

Recent Progress in Solution-Based Metal Oxide Resistive Switching Devices

Metal oxide resistive switching memories have been a crucial component for the requirements of the Internet of Things, which demands ultra-low power and high-density devices with new computing principles, exploiting low cost green products and technologies. Most of the reported resistive switching devices use conventional methods (physical and chemical vapor deposition), which are quite expensive due to their up-scale production. Solution-processing methods have been improved, being now a reliable technology that offers many advantages for resistive random-access memory (RRAM) such as high versatility, large area uniformity, transparency, low-cost and a simple fabrication of two-terminal structures. Solution-based metal oxide RRAM devices are emergent and promising non-volatile memories for future electronics. In this review, a brief history of non-volatile memories is highlighted as well as the present status of solution-based metal oxide resistive random-access memory (S-RRAM). Then, a focus on describing the solution synthesis parameters of S-RRAMs which induce a massive influence in the overall performance of these devices is discussed. Next, a precise analysis is performed on the metal oxide thin film and electrode interface and the recent advances on S-RRAM that will allow their large-area manufacturing. Finally, the figures of merit and the main challenges in S-RRAMs are discussed and future trends are proposed.

In-vacuum active colour sensor and wireless communication across a vacuum-air interface

In situ sensing with wireless digital-data transfer is a potential processing scheme that works very closely to the location of an event monitored by a sensor and converts the sensor’s raw output into digitized and informative small-volume bits, as suggested by recent proposals for edge computing and the Internet of Things (IoT). Colour perception may be a target of in situ sensor data acquisition; however, in contrast to from other sensing devices, colour sensors that detect visible light signals are usually located away from light-emitting sources, collecting light transmitting through the space and attenuating it in some manner. For example, in a vacuum chamber whose gas pressure is much less than the ambient atmosphere in which the sensors usually work, there are many veiled light sources, such as discharge plasma, for various industrial purposes including nanoscale manufacturing. In this study, we designed an in-vacuum colour sensor that can work with analogue-to-digital conversion and transfer data by wireless communication; this sensor is active in a low-pressure plasma chamber, detecting light signals and transferring them to a personal computer located outside the vacuum chamber. In addition to detecting lights with controlled spectra from outside successfully, we achieved complete operation of our in-vacuum active sensor for plasma emissions generated at 100 Pa. Comparing the signals with data from simultaneous monitoring by a monochromator, we established that the recorded signals arose from the plasma, confirming successful direct detection of low-pressure plasma emissions without any filtering effects between the sensor and the target object.

Optical Realization of Wave-Based Analog Computing with Metamaterials

Recently, the study of analog optical computing raised renewed interest due to its natural advantages of parallel, high speed and low energy consumption over conventional digital counterpart, particularly in applications of big data and high-throughput image processing. The emergence of metamaterials or metasurfaces in the last decades offered unprecedented opportunities to arbitrarily manipulate the light waves within subwavelength scale. Metamaterials and metasurfaces with freely controlled optical properties have accelerated the progress of wave-based analog computing and are emerging as a practical, easy-integration platform for optical analog computing. In this review, the recent progress of metamaterial-based spatial analog optical computing is briefly reviewed. We first survey the implementation of classical mathematical operations followed by two fundamental approaches (metasurface approach and Green’s function approach). Then, we discuss recent developments based on different physical mechanisms and the classical optical simulating of quantum algorithms are investigated, which may lead to a new way for high-efficiency signal processing by exploiting quantum behaviors. The challenges and future opportunities in the booming research field are discussed.

A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction

Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. This paper proposes a classification, detection, and perspective correction process that is suitable for real-world usage, when considering the data used for training a state-of-the-art model for the extraction of a chart in real-world photography. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.

Nanoscale Patterning of Carbon Nanotubes: Techniques, Applications, and Future

Carbon nanotube (CNT) devices and electronics are achieving maturity and directly competing or surpassing devices that use conventional materials. CNTs have demonstrated ballistic conduction, minimal scaling effects, high current capacity, low power requirements, and excellent optical/photonic properties; making them the ideal candidate for a new material to replace conventional materials in next-generation electronic and photonic systems. CNTs also demonstrate high stability and flexibility, allowing them to be used in flexible, printable, and/or biocompatible electronics. However, a major challenge to fully commercialize these devices is the scalable placement of CNTs into desired micro/nanopatterns and architectures to translate the superior properties of CNTs into macroscale devices. Precise and high throughput patterning becomes increasingly difficult at nanoscale resolution, but it is essential to fully realize the benefits of CNTs. The relatively long, high aspect ratio structures of CNTs must be preserved to maintain their functionalities, consequently making them more difficult to pattern than conventional materials like metals and polymers. This review comprehensively explores the recent development of innovative CNT patterning techniques with nanoscale lateral resolution. Each technique is critically analyzed and applications for the nanoscale-resolution approaches are demonstrated. Promising techniques and the challenges ahead for future devices and applications are discussed.

A Parasitic Resistance-Adapted Programming Scheme for Memristor Crossbar-Based Neuromorphic Computing Systems

Memristor crossbar arrays without selector devices, such as complementary-metal oxide semiconductor (CMOS) devices, are a potential for realizing neuromorphic computing systems. However, wire resistance of metal wires is one of the factors that degrade the performance of memristor crossbar circuits. In this work, we propose a wire resistance modeling method and a parasitic resistance-adapted programming scheme to reduce the impact of wire resistance in a memristor crossbar-based neuromorphic computing system. The equivalent wire resistances for the cells are estimated by analyzing the crossbar circuit using the superposition theorem. For the conventional programming scheme, the connection matrix composed of the target memristance values is used for crossbar array programming. In the proposed parasitic resistance-adapted programming scheme, the connection matrix is updated before it is used for crossbar array programming to compensate the equivalent wire resistance. The updated connection matrix is obtained by subtracting the equivalent connection matrix from the original connection matrix. The circuit simulations are performed to test the proposed wire resistance modeling method and the parasitic resistance-adapted programming scheme. The simulation results showed that the discrepancy of the output voltages of the crossbar between the conventional wire resistance modeling method and the proposed wire resistance modeling method is as low as 2.9% when wire resistance varied from 0.5 to 3.0 Ω. The recognition rate of the memristor crossbar with the conventional programming scheme is 99%, 95%, 81%, and 65% when wire resistance is set to be 1.5, 2.0, 2.5, and 3.0 Ω, respectively. By contrast, the memristor crossbar with the proposed parasitic resistance-adapted programming scheme can maintain the recognition as high as 100% when wire resistance is as high as 3.0 Ω.