Conventional Detection Schemes Research Articles

χ (2)-induced artifact overwhelming the third-order signal in 2D Raman-THz spectroscopy of non-centrosymmetric materials.

Through comprehensive data analysis, we demonstrate that a χ(2)-induced artifact, arising from imperfect balancing in the conventional electro-optic sampling detection scheme, contributes significantly to the measured signal in 2D Raman-THz spectroscopy of non-centrosymmetric materials. The artifact is a product of two 1D responses, overwhelming the desired 2D response. We confirm that by analyzing the 2D Raman-THz response of an x-cut beta barium borate crystal. We furthermore show that this artifact can be effectively suppressed by implementing a special detection scheme. We successfully isolate the desired third-order 2D Raman-THz response, revealing a distinct cross-peak feature, whose frequency position suggests the coupling between two crystal phonons.

Detection and Localization of Stealthy Cyberattacks in Cyber-Physical Discrete Event Systems

The widespread use of cyber-physical systems (CPS) not only brings new opportunities with it but also introduces the risk of cyber attacks on the system. One of the most dangerous and sophisticated types of attacks are the stealthy attacks that can hide their influence and are not detectable by conventional anomaly detection schemes. In this paper, a detection method for the replay attack and the covert attack on discrete event systems is proposed. Unexpected behavior is introduced into the transmitted signals by the integration of permutation matrices into the sensor and actuator channels. The permutation disrupts the stealthiness of an attacker and allows the detection of cyber attacks. Moreover, an attack localization scheme is proposed, which allows to determine which signals are attacked in case that the attacker only attacks a subset of communication channels and not the whole system.

Development and application of a rapid scheme for detection of respiratory virus nucleic acid

This study aimed to develop a portable, accurate and easy-to-operate scheme for rapid detection of respiratory virus nucleic acid. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the effect of extraction-free respiratory virus treatment reagent (RTU) on viral nucleic acid treatment and the effect of ultra-fast fluorescence quantitative PCR instrument (FQ-8A) on nucleic acid amplification, respectively. RTU and FQ-8A were combined to develop a rapid detection scheme for respiratory virus nucleic acid, and the positive detection rate was judged by Ct value using a fluorescence quantitative PCR instrument, and the accuracy of the scheme in clinical samples detection was investigated. The results showed that RTU had comparable sensitivity to the automatic nucleic acid extraction instrument, its extraction efficiency was comparable to the other 3 extraction methods when extracting samples of different virus types, but the extraction time of RTU was less than 5 min. FQ-8A had good consistency in detection respiratory syncytial virus (RSV) and adenovirus (ADV) compared with the control instrument ABI-7500, with kappa coefficients of 0.938 (P < 0.001) and 0.887 (P < 0.001), respectively, but the amplification time was only about 0.5 h. The RTU and FQ-8A combined rapid detection scheme had a highly consistent detection rate with the conventional detection scheme, with a sensitivity of 91.70% and specificity of 100%, and a kappa coefficient was 0.944 (P < 0.001). In conclusion, by combining RTU with FQ-8A, a rapid respiratory virus nucleic acid detection scheme was developed, the whole process could be completed in 35 min. The scheme is accurate and easy-to-operate, and can provide important support for the rapid diagnosis and treatment of respiratory virus.

A Resilient Protection Scheme for Common Shunt Fault and High Impedance Fault in Distribution Lines Using Wavelet Transform

Distribution networks are most prone to high impedance faults (HIFs). These types of faults occur whenever an energized conductor makes a contact with some surfaces that are poor conductors such as sand, concrete, branches of trees, etc. The consequential fault current has an absolute magnitude similar to that of load current therefore, the overcurrent relays usually fail to operate correctly during these faults. In the past few decades, several schemes have surfaced for detecting HIFs. The studies conducted in this article suggest an application of discrete wavelet transform to obtain discriminative features of faulty current signals and the development of a threshold-based scheme to discriminate between faulty and nonfaulty situations. The first-level approximate wavelet coefficients are extracted and used to calculate the standard deviation features using a recursive moving window. With the help of these features, faulted phase is also identified simultaneously apart from fault detection. The efficacy of the scheme is tested on a modified IEEE-13 bus test feeder system incorporated with distributed generators. The algorithm has been compared with the conventional HIF detection schemes and the results proved the efficiency and robustness of the proposed scheme to detect the fault under a variety of fault conditions. The main contributions of the proposed scheme are viz., it can correctly differentiate between HIF/LIF and switching events and works well on both grid-connected or islanding mode. Finally, validation with real field fault events captured by digital fault recorder confirms that the proposed framework can assist utility engineers in detecting the HIF/LIF and switching events securely.

A lightweight IoT firmware vulnerability detection scheme based on homology detection

In recent years, with the rapid development of IoT technology, hundreds of millions of IoT devices have been manufactured and applied, and the subsequent IoT attacks have become more and more severe. The complex and diverse architecture of IoT devices, coupled with the lack of security development specifications by IoT device manufacturers, and the widespread misuse and abuse of code, lead to the proliferation of IoT vulnerabilities. The conventional IoT vulnerability detection scheme is expensive to operate, and the implementation technology is complex, which is difficult to be fully promoted. This paper proposes a lightweight IoT firmware vulnerability detection scheme based on homology detection. The processing is converted into a feature vector, which effectively reduces the platform dependence. Combined with the database technology, the storage and retrieval efficiency is increased, and the same-origin vulnerability detection is realized by calculating the cosine similarity of the vector. The experimental results show that this scheme can effectively identify the vulnerabilities in firmware.

ScatterVerif: Verification of Electronic Boards Using Reflection Response of Power Distribution Network

The globalization of electronic systems’ fabrication has made some of our most critical systems vulnerable to supply chain attacks. Implanting spy chips on the printed circuit boards (PCBs) or replacing genuine components with counterfeit/recycled ones are examples of such attacks. Unfortunately, conventional attack detection schemes for PCBs are ad hoc, costly, unscalable, and error prone. This work introduces a holistic physical verification framework for PCBs, called ScatterVerif , based on the characterization of the PCBs’ power distribution network. First, we demonstrate how scattering parameters, frequently used for impedance characterization of RF circuits, can characterize the entire PCB with a single measurement. Second, we present how a class of machine learning algorithms, namely the Gaussian mixture model, can be applied to the measurements to automatically classify/cluster the genuine and tampered/counterfeit PCBs. We show that these attacks affect the overall impedance of a PCB differently in various frequency ranges, hence the conventional impedance measurements using a constant-frequency electrical stimulus might leave the attack undetected. We conduct extensive experiments on counterfeit and tampered devices and demonstrate that these attacks can be detected with high confidence. Finally, we show that the acquired data from the power distribution network characterization can also be deployed for fingerprinting genuine PCBs.

Tabu Search Aided Multi-Track Detection Scheme for Bit-Patterned Media Recording

A tabu search (TS) aided multi-track detection scheme for bit patterned magnetic recording is proposed. The proposed detection scheme adopts the simplified multi-track detector with a two-dimensional generalized partial response (GPR) target, uses a priori information, and employs a reliability test and a TS detector. The proposed detection scheme processes signals in three stages. First, the single-track detector with a one-dimensional GPR target detects the data for the main-track and side-tracks. Second, the reliability test is performed to detect bits with low reliability in the side-tracks by comparing the distance between the readback signal and the estimated and reconstructed readback signal. If the distance is greater than a specific value, the TS detector is conducted to improve the reliability of bits with low reliability in the side-tracks. Lastly, the simplified multi-track detector detects the main-track data using a priori information from the outputs of the TS or single-track detectors for the side-track data. The proposed and conventional detection schemes are compared in terms of the bit error rate performances. Simulation results show that the proposed detection scheme with slightly additional complexity has more than 3 dB gains compared to the conventional detection schemes.

Computational single-objective scanning light sheet (cSOLS)

Single-objective scanning light sheet (SOLS) imaging has fueled major advances in volumetric bioimaging because it supports low phototoxic, high-resolution imaging over an extended period. The remote imaging unit in the SOLS does not use a conventional epifluorescence image detection scheme (a single tube lens). In this paper, we propose a technique called the computational SOLS (cSOLS) that achieves light sheet imaging without the remote imaging unit. Using a single microlens array after the tube lens (lightfield imaging), the cSOLS is immediately compatible with conventional epifluorescence detection. The core of cSOLS is a Fast Optical Ray (FOR) model. FOR generates 3D imaging volume (40 × 40 × 14 µm3) using 2D lightfield images taken under SOLS illumination within 0.5 s on a standard central processing unit (CPU) without multicore parallel processing. In comparison with traditional lightfield retrieval approaches, FOR reassigns fluorescence photons and removes out-of-focus light to improve optical sectioning by a factor of 2, thereby achieving a spatial resolution of 1.59 × 1.92 × 1.39 µm3. cSOLS with FOR can be tuned over a range of oblique illumination angles and directions and, therefore, paves the way for next-generation SOLS imaging. cSOLS marks an important and exciting development of SOLS imaging with computational imaging capabilities.

Experimental demonstration: Differential detection of 10 Gbps optical OOK signal over turbulent channel

In this paper, we experimentally demonstrate the bit error rate (BER) performance of fixed threshold differential detection for both 5 Gbps and 10 Gbps On-Off keying (OOK) signals over a turbulent channel, which induces slowly varying intensity fluctuation on the signals. The experimental results are also validated using numerical simulations. This study is significant for the terrestrial free-space optical (FSO) communication system in which the channel induces similar intensity fluctuation due to atmospheric turbulence. To show the efficiency of the differential detection scheme for the OOK signal two proof-of-principle systems are realized. In the first system, the effect of channel-induced intensity variation on the data is realized by passing a differentially coded OOK signal through a Mach–Zehnder modulator (MZM), which is driven by a low frequency sinusoidal electrical signal emulating slowly varying channel fluctuation. The amplitude of the sinusoid has been varied to create different levels of intensity fluctuation, mimicking different turbulent conditions in the optical link. In the second setup, we measure the BER performance of differential detection assisted OOK transmission over a short-range (10)m FSO channel, which is affected by turbulence-induced intensity fluctuations. The different turbulences are created by different heating levels of a blower, which injects hot air across the FSO channel. Experimental results show that in the presence of intensity fluctuation, differential detection of OOK signal using a fixed threshold offers a better BER performance at higher optical to signal ratio (OSNR) values compared to conventional direct detection (DD) scheme. Results obtained from numerical simulation of OOK transmission over turbulent FSO channels also confirm the similar behavior. Both simulation and experimental results show that differential detection can offer a BER of 10−3 at 5 Gbps and 10 Gbps data rates with slow varying channel-induced intensity fluctuation when the DD scheme fails to produce the same BER values.

An Improved SIC-Based Detection Scheme for Non-Uniform Constellations in ATSC 3.0 MIMO

This paper presents an alternative approach to real-valued signal representation and proposes an improved successive interference cancellation (SIC)-based detection scheme using the alternative signal representation for non-uniform constellations (NUCs) in Advanced Television Systems Committee (ATSC) 3.0. Since I/Q polarization interleaving in ATSC 3.0 multiple-input multiple-output (MIMO) precoding performs a non-linear operation, it makes it difficult to use conventional suboptimal detection schemes based on interference cancellation. Therefore, the alternative signal representation is designed to easily apply various suboptimal detection schemes. Further, to obtain more robustness and lower complexity, a suboptimal detection scheme based on the alternative signal representation and interference cancellation with QR decomposition is proposed. The proposed detection scheme exploits the structural properties of NUCs and jointly detects the real and imaginary parts as a block. In addition, by considering one or more candidate symbols in the interference cancellation for the proposed detection scheme, the performance and the complexity are trade-offs according to the number of the considering candidate symbols. Simulation results show that the proposed detection scheme with a slight increase of the complexity significantly outperforms a linear detection under fixed and mobile channels.

Vulnerability Identification and Remediation of FDI Attacks in Islanded DC Microgrids Using Multiagent Reinforcement Learning

This article proposes a novel approach to uncover deficiencies of the existing cyber-attack detection schemes and thereby to serve as a foundation for establishing more reliable cybersecure solutions, with particular application in dc microgrids. For this purpose, a multiagent deep reinforcement learning (RL)-based algorithm is proposed to automatically discover the vulnerable spots in the conventional index-based cyberattack detection schemes and automatically generate coordinated stealthy destabilizing false data injection (FDI) attacks on cyber-protected islanded dc microgrids. To enable a continuous action space for the trained RL agents and enhance the algorithm’s precision and convergence rate, deep deterministic policy gradient is incorporated. Using this approach, susceptibility of a state-of-the-art detection scheme to several different coordinated FDI attacks on the distributed communication links is identified. The proposed algorithm is also enhanced with a sniffing feature to enable maintaining the stealthy attacks even under the sudden disconnection of any of the compromised links. To address the discovered deficiencies within the index-based detection scheme, a complementary multiagent RL detection algorithm using deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -network algorithm is integrated, which provides a more reliable overall identification performance. Taking into account the communication delays and load changes, the effectiveness of the proposed algorithm is verified by the experimental tests.

Using snapshot measurements to identify high-emitting vehicles

Policy makers have long been interested in detecting ‘high-emitters’, a supposedly smallfraction of vehicles that make disproportionally large contributions to total fleet emissions. However, existing identification schemes often exclusively rely on snapshot measurements (i.e. emissions within less than a second), and thus simply identify vehicles with high instantaneous emissions, instead of vehicles with high average emissions over a driving period as regulated by emission standards. We design a comprehensive scheme to address this challenge by combining fleetwide remote sensing measurements with detailed second-by-second emission measurements from individual vehicles. We first determine the trip-average NO x emission rates of individual vehicles in a Euro-5 diesel fleet measured across European locations; this allows, second, to calculate the fraction and emission contributions of high-emitters based on trip-average emission. We demonstrate that the identification of high-emitters is quite uncertain as long as it is based on single snapshots only; but 80% of the high-emitters can be identified with over 75% precision with five or more repeated measurements of the same vehicle. Compared to the conventional detection schemes, our scheme can increase the identified high-emitters and associated emission reductions by over 140%. Our method is validated and shown to be superior to the conventional interpretation of snapshot measurements.

Location-aware spectrum sensing for cognitive visible light communications over multipath channels

Accurate spectrum sensing is crucial for cognitive visible light communication (CVLC). However, due to multiple light emitting diodes (LEDs) and indoor reflections, a CVLC channel often shows a multipath characteristic which is difficult for a secondary user (SU) to estimate before spectrum sensing, thereby leading to a degraded sensing accuracy. To tackle this issue, we propose a location-aware spectrum sensing scheme for the CVLC system, where its multipath channel can be effectively estimated based on the location information of an SU. By considering different a priori information, we propose three primary user (PU) detection algorithms. Specifically, we first propose a location-aware likelihood-ratio-test detection (LLD) algorithm to achieve an optimal sensing performance by using a priori knowledge of signal and noise variances. To reduce the computational complexity of LLD, we propose a more efficient location-aware semi-blind detection (LSD) algorithm, which requires no a priori knowledge of signal variance. Further, to tackle noise uncertainty, we propose a location-aware near-blind detection (LND) algorithm, which does not require either signal or noise variance. To evaluate the performance of the proposed algorithms, we also develop their respective analytical models based on the multipath channel. It is found that the analytical models can accurately match the results obtained by simulations, and LLD and LSD can improve detection probability by 10% compared with the conventional energy detection (ED) scheme. Moreover, by incorporating LSD with cyclic-prefix-based detection (CD), the detection probability can be further improved by ∼20% and 8% compared with ED and CD, respectively. Finally, LND outperforms all the other algorithms when the noise uncertainty is over 0.5 dB, and it is found that both LSD and LND are robust to receiver tilt and LSD is robust to location error.

Characterization of ultrafast processes at metal/solution interfaces: Towards femtoelectrochemistry

The essential part of electrochemistry is charge transfer. To understand this process in great detail, one needs to probe the relevant kinetics and dynamics on time scales spanning from femtoseconds to seconds or even longer. Although a conventional electrochemical detection scheme is sufficient for nanosecond or slower processes, it does not offer high enough time resolution for probing ultrafast processes, such as solvent reorganization, electron tunneling, and surface isomerization, that occur on faster, for example picosecond or femtosecond, timescales. These are indispensable parameters in the advanced charge transfer theories. In this review, some recent studies using ultrashort lasers to explore the ultrafast dynamics at the metal/solution interface are reviewed. The focus is on optical pump-probe and optical pump-push with electrochemical probe schemes. The connection of these studies with conventional electrochemistry and the limitations of these detection schemes are discussed.

On the modeling of amplitude-sensitive electron spin resonance (ESR) detection using voltage-controlled oscillator (VCO)-based ESR-on-a-chip detectors.

In this paper, we present an in-depth analysis of a voltage-controlled oscillator (VCO)-based sensing method for electron spin resonance (ESR) spectroscopy, which greatly simplifies the experimental setup compared to conventional detection schemes. In contrast to our previous oscillator-based ESR detectors, where the ESR signal was encoded in the oscillation frequency, in the amplitude-sensitive method, the ESR signal is sensed as a change of the oscillation amplitude of the VCO. Therefore, using VCO architecture with a built-in amplitude demodulation scheme, the experimental setup reduces to a single permanent magnet in combination with a few inexpensive electronic components. We present a theoretical analysis of the achievable limit of detection, which uses perturbation-theory-based VCO modeling for the signal and applies a stochastic averaging approach to obtain a closed-form expression for the noise floor. Additionally, the paper also introduces a numerical model suitable for simulating oscillator-based ESR experiments in a conventional circuit simulator environment. This model can be used to optimize sensor performance early on in the design phase. Finally, all presented models are verified against measured results from a prototype VCO operating at 14 inside a 0.5 magnetic field.

Non-equilibrium coupling of a quartz resonator to ions for Penning-trap fast resonant detection

The coherent coupling between a quartz electro-mechanical resonator at room temperature and trapped ions in a 7 T Penning trap has been demonstrated for the first time. The signals arising from the coupling remain for integration times in the orders of seconds. From the measurements carried out, we demonstrate that the coupling allows detecting the reduced-cyclotron frequency (ν +) within times in the order of the decay-time constant of the energized resonator (below 10 ms), regardless of the data-acquisition time-window in use, and providing an improved resolution compared to conventional electronic detection schemes. A resolving power ν +/Δν + = 2.4 × 107 has been reached in single measurements. In this publication we present the first results, emphasizing the novel features of the quartz resonator as fast non-destructive ion-trap detector together with different ways to analyze the data and considering aspects like precision, resolution and sensitivity.

In Situ X-ray Absorption Spectroscopy and Droplet-Based Microfluidics: An Analysis of Calcium Carbonate Precipitation.

Droplet-based microfluidic systems are ideally suited for the investigation of nucleation and crystallization processes. To best leverage the features of such platforms (including exquisite time resolution and high-throughput operation), sensitive and in situ detection schemes are needed to extract real-time chemical information about all species of interest. In this regard, the extension of conventional (UV, visible, and infrared) optical detection schemes to the X-ray region of the electromagnetic spectrum is of high current interest, as techniques such as X-ray absorption spectroscopy (XAS) provide for the element-specific investigation of the local chemical environment. Accordingly, herein, we report for the first time the integration of millisecond droplet-based microfluidics with XAS. Such a platform allows for the sensitive acquisition of X-ray absorption data from picoliter-volume droplets moving at high linear velocities. Significantly, the high-temporal resolution of the droplet-based microfluidic platform enables unprecedented access to the early stages of the reaction. Using such an approach, we demonstrate in situ monitoring of calcium carbonate precipitation by extracting XAS spectra at the early time points of the reaction with a dead time as low as 10 ms. We obtain insights into the kinetics of the formation of amorphous calcium carbonate (ACC) as a first species during the crystallization process by monitoring the proportion of calcium ions converted into ACC. Within the confined and homogeneous environment of picoliter-volume droplets, the ACC content reaches 60% over the first 130 ms. More generally, the presented method offers new opportunities for the real-time monitoring of fast chemical and biological processes.

Deep Learning-Based Bootstrap Detection Scheme for Digital Broadcasting System

In the advanced television systems committee (ATSC) 3.0 system, the concept of flexibility is significant for supporting backward compatibility within the same ATSC 3.0 system. However, since the conventional bootstrap signal detection scheme is difficult to support the flexibility, the conventional bootstrap signal detection scheme should be newly designed according to the change of version. In this paper, a convolution neural network (CNN) model for bootstrap signal detection in ATSC 3.0 is proposed to maintain the flexibility of bootstrap. Additionally, for minimizing the loss of error performance of CNN-based bootstrap detection scheme, this paper proposes two dimensional alternate array to utilize the correlation of the adjacent bootstrap symbol and proposes the offline learning method using the bootstrap signal corrupted by noise to improve the error performance.

Faulty Phase Identification for Transmission Line with Metal Oxide Varistor-protected Series Compensator

The nonlinear operation of metal oxide varistor (MOV)-protected series compensator in transmission lines introduces complications into fault detection approaches. The accuracy of a conventional fault detection schemes is adversely affected by continuous change of the system impedance and load current at the point of a series compensation unit. Thus, this study suggests a method for detecting the faulted phase in MOV-protected series-compensated transmission lines. Primarily, the fault feature is identified using the covariance coefficients of the current samples during the fault period and the current samples during the pre-fault period. Furthermore, a convenience fault detection index is established by applying the cumulative sum technique. Extensive validation through different fault circumstances is accomplished, including different fault positions, resistances, and inception times. The experimental results show that the proposed method performs well with high resistance or impedance faults, faults in noisy conditions, and close-in and far-end faults. The proposed method is simple and efficient for faulty phase detection in MOV-protected series-compensated transmission lines.

Coupling of silver nanoparticle-conjugated fluorescent dyes into optical fiber modes for enhanced signal-to-noise ratio

We present the optical coupling of the silver nanoparticles (AgNPs)-conjugated dye molecule into fiber optical modes for detecting fluorescence with the enhanced signal-to-noise (S/N) ratio. This near field coupling of the excited state of organic dye (FAM) molecules into the fiber multimodes occurs by immobilizing them on the exposed surface of fiber core, permitting the coupled light to be guided along the fiber for detection. This fiber based scheme is the first attempt to single out the fluorescence using fiber modes not for carrying excitation light but only for collecting emission light via the dye-fiber coupling. The emission-selective coupling into fiber modes turns out to be effective in reducing the unwanted background noise arising from both the false detection of excitation light and bulk autofluorescence.This scheme differs from the previously reported fluorescence sensors based on waveguides where guided modes at λex excite dye molecules via their evanescent fields. In addition, the local fields enhanced by AgNPs in close proximity to FAM molecules on the fiber core surface increase the rates of dye excitation and radiative decay/AgNP supported surface plasmon coupled emission. While focusing on demonstrating the proof-of-concept of the scheme presented, we obtain the maximum of 4.2-fold enhancement of the signal-to-noise (S/N) ratio in detecting fluorescence as compared to a conventional fluorescence detection scheme. The results presented in the fiber-based scheme may find an application where high S/N ratio fluorescence based biochemical assay is required in a small-sized device with remote sensing capability.