Search Results (15)

The efficacy of low-carbon power systems is reduced by the high energy demand of conventional CO₂ capture processes, the loss of recoverable thermal energy, and power-quality disturbances associated with variable renewable generation. This study suggests an integrated framework that integrates membrane-assisted CO₂ capture, thermoelectric waste-heat recovery, photovoltaic generation, battery energy storage, and a grid-connected converter to address these issues. During the capture of CO₂, the waste heat is converted into electrical energy using a thermoelectric generator and integrated with the photovoltaic and battery outputs through a common DC link. A conventional phase-locked loop is not necessary for reference-signal extraction, DC-offset rejection, harmonic compensation, and power management, as an Enhanced Notch Filter-Based Second-Order Generalized Integrator (ENF-SOGI) controller is employed. The effectiveness of the proposed system is demonstrated by simulation and experimental studies conducted under variable irradiance, nonlinear loading, distorted-load, and distorted-grid-voltage conditions. The membrane unit achieves a CO₂ capture efficiency of approximately 92%, with a specific energy consumption of 1.2 GJ/tCO₂. The controller reduces the source-current total harmonic distortion from 24.3% to approximately 1.2–1.3%, limits its experimental variation to ±5 V, and maintains the DC-link voltage at approximately 600 V. Consequently, the proposed architecture is designed to facilitate low-carbon grid operation by integrating a unified energy-management system that includes high-efficiency CO₂ separation, the productive recovery of waste heat from the capture process, increased renewable energy utilization, and IEEE-compliant source-current quality. Full article

The M21 epoxy matrix is a toughened material designed to enhance the fracture resistance of carbon fiber-reinforced polymers (CFRPs). This study presents an experimental characterization of the shear and interlaminar properties required for validating computational damage models of hybrid laminated composite panels manufactured with the M21 material system. In-plane shear behavior was evaluated using ±45 (PM45) tests, while interlaminar fracture properties were characterized through double cantilever beam (DCB) and end-notched flexure (ENF) tests. The results demonstrate that hybrid laminates exhibit high interfacial fracture toughness, with notably increased resistance observed in woven–woven and unidirectional–woven interface pairs. Parametric studies identified cohesive strength and fracture energy as the dominant parameters governing delamination behavior in numerical simulations. Corresponding values were extracted for each interface type, enabling accurate representation of damage initiation and propagation in finite element models. To the authors’ knowledge, this work provides the first experimental dataset for the listed M21-based hybrid unidirectional–woven and woven–woven interfaces, establishing a benchmark for future modeling and simulation of toughened composite structures. Full article

The electric network frequency (ENF) serves as a vital criterion in geographical localization because its frequency fluctuations remain consistent within the same power grid. However, the performance of existing ENF-based audio geo-localization methods is limited when dealing with real-world scenarios, such as short audio durations and noisy environments. Moreover, the size of available ENF data is still small. To address these issues, we propose a novel audio inter-grid geo-localization method utilizing real-time online multimedia data. First, we construct the China-Online-Data dataset using online data, which integrates enhancement and harmonic selection to reduce noise and improve ENF estimation accuracy. Subsequently, we propose an ENF-based Dual-Channel Geo-Localization Network (DC-GLNet), which leverages both time and time-frequency domain information to improve feature extraction and classification performance. Experimental results demonstrate that the proposed method outperforms existing methods, particularly in short audio scenarios, achieving superior accuracy for inter-grid geo-localization. Full article

The detection of audio tampering plays a crucial role in ensuring the authenticity and integrity of multimedia files. This paper presents a novel approach to identifying tampered audio files by leveraging the unique Electric Network Frequency (ENF) signal, which is inherent to the power grid and serves as a reliable indicator of authenticity. The study begins by establishing a comprehensive Chinese ENF database containing diverse ENF signals extracted from audio files. The proposed methodology involves extracting the ENF signal, applying wavelet decomposition, and utilizing the autoregressive model to train effective classification models. Subsequently, the framework is employed to detect audio tampering and assess the influence of various environmental conditions and recording devices on the ENF signal. Experimental evaluations conducted on our Chinese ENF database demonstrate the efficacy of the proposed method, achieving impressive accuracy rates ranging from 91% to 93%. The results emphasize the significance of ENF-based approaches in enhancing audio file forensics and reaffirm the necessity of adopting reliable tamper detection techniques in multimedia authentication. Full article

Vegetation phenology reflects the response mechanisms in ecology and climate change, so it is important that the parameters used to study vegetation phenology are accurate. Previous studies mainly focused on phenological changes. However, because the extraction methods used in those investigations led to inconsistencies in setting vegetation phenological parameters, a more accurate approach needs to be developed. To resolve this issue, we select five methods to extract the start of the growing season (SOS) and the end of the growing season (EOS) from the normalised difference vegetation index (NDVI3g) data. The five chosen methods are the second-order derivative method (Method 1), the first-order derivative method (Method 2), the 0.2 dynamic threshold method (Method 3), the 0.5 dynamic threshold method (Method 4), and the fixed threshold method (Method 5). Our study area is the Northern Hemisphere (above 30°N), and our study period is 1982 to 2015. After applying the five methods, we evaluate the consistency of the vegetation phenological parameters. The results show that (1) regardless of the method used, the average changes in phenological parameters are consistent; however, the SOS and EOS under Methods 1, 3 and 5 are up to 30 days earlier than those under Methods 2 and 4. (2) Under all five methods, the SOS trend mainly shows an advance, but the trend is substantially higher under Methods 1, 3 and 4 than under Methods 2 and 5 from 45°N to 60°N. The distribution of the EOS trend under different methods is consistent. (3) Under the tested extraction methods, the SOS trends of evergreen needleleaf forests (ENF) and mixed forests (MF) have significant differences (p < 0.05), whereas, the EOS trend for different vegetation types is consistent. (4) By analysing the consistency of the phenological parameters between remote sensing data and ground data under different methods, we now know that Methods 3 and 4 are the most accurate for extracting the SOS and EOS, respectively. The above results can provide a reference for the accurate extraction of phenological parameters above 30°N. Full article

In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of temporal features, and do not fully exploit the effective information in the shallow ENF features, which leads to low accuracy of audio tamper detection. Therefore, this paper proposes a new method for digital audio tampering detection based on the deep temporal–spatial feature of ENF. To extract the temporal and spatial features of the ENF, firstly, a highly accurate ENF phase sequence is extracted using the first-order Discrete Fourier Transform (DFT), and secondly, different frame processing methods are used to extract the ENF shallow temporal and spatial features for the temporal and spatial information contained in the ENF phase. To fully exploit the effective information in the shallow ENF features, we construct a parallel RDTCN-CNN network model to extract the deep temporal and spatial information by using the processing ability of Residual Dense Temporal Convolutional Network (RDTCN) and Convolutional Neural Network (CNN) for temporal and spatial information, and use the branch attention mechanism to adaptively assign weights to the deep temporal and spatial features to obtain the temporal–spatial feature with greater representational capacity, and finally, adjudicate whether the audio is tampered with by the MLP network. The experimental results show that the method in this paper outperforms the four baseline methods in terms of accuracy and F1-score. Full article

The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time ($T_{ro}$) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the $T_{ro}$ to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach. Full article

With recent advancements in artificial intelligence (AI) and next-generation communication technologies, the demand for Internet-based applications and intelligent digital services is increasing, leading to a significant rise in cyber-attacks such as Distributed Denial-of-Service (DDoS). AI-based DoS detection systems promise adequate identification accuracy with lower false alarms, significantly associated with the data quality used to train the model. Several works have been proposed earlier to select optimum feature subsets for better model generalization and faster learning. However, there is a lack of investigation in the existing literature to identify a common optimum feature set for three main AI methods: machine learning, deep learning, and unsupervised learning. The current works are compromised either with the variation of the feature selection (FS) method or limited to one type of AI model for performance evaluation. Therefore, in this study, we extensively investigated and evaluated the performance of 15 individual FS methods from three major categories: filter-based, wrapper-based, and embedded, and one ensemble feature selection (EnFS) technique. Furthermore, the individual feature subset’s quality is evaluated using supervised and unsupervised learning methods for extracting a common best-performing feature subset. According to our experiment, the EnFS method outperforms individual FS and provides a universal best feature set for all kinds of AI models. Full article

Harposporium anguillulae, an endoparasitic nematophagous fungus (ENF), is a model fungus from which the genus Harposporium was established. It can infect nematodes via ingested conidia. In this paper, the morphology and nematode–fungus interaction between Panagrellus redivivus and H. anguillulae were observed by scanning electron microscopy (SEM). The secondary metabolites of H. anguillulae were also studied. Seven metabolites were purified and identified from an ethyl acetate extract of broth and a methanol extract of mycelium. These include a new polyketone 5-hydroxy-3-(hydroxymethyl)-6-methyl-2H-pyran-2-one (1) and six known metabolites (17R)-17-methylincisterol (2), eburicol (3), ergosterol peroxide (4), terpendole C (5), (3β,5α,9β,22E)-3,5-dihydroxy-ergosta-7,22-dien-6-one (6), and 5α,6β-epoxy-(22E,24R)-ergosta-8,22-diene- 3β,7α-diol (7). These metabolites were assayed for their activity against plant root-knot nematode, Meloidogyne incognita, and the results showed that terpendole C (5) had weak nematicidal activity but also that other compounds did not have evident activity at a concentration of 400 μg mL⁻¹. Compound 1 exhibited an attractive effect towards P. redivivus. Full article

In the present work, a numerical model based on the cohesive zone modeling (CZM) approach has been developed to simulate mixed-mode fracture of co-consolidated low melt polyaryletherketone thermoplastic laminates by considering fiber bridging. A modified traction separation law of a tri-linear form has been developed by superimposing the bi-linear behaviors of the matrix and fibers. Initially, the data from mode I (DCB) and mode II (ENF) fracture toughness tests were used to construct the R-curves of the joints in the opening and sliding directions. The constructed curves were incorporated into the numerical models employing a user-defined material subroutine developed in the LS-Dyna finite element (FE) code. A numerical method was used to extract the fiber bridging law directly from the simulation results, thus eliminating the need for the continuous monitoring of crack opening displacement during testing. The final cohesive model was implemented via two identical FE models to simulate the fracture of a Single-Lap-Shear specimen, in which a considerable amount of fiber bridging was observed on the fracture area. The numerical results showed that the developed model presented improved accuracy in comparison to the CZM with the bi-linear traction–separation law (T–SL) in terms of the predicted strength of the joint. Full article

The rapid advancement in artificial intelligence (AI) and wide deployment of Internet of Video Things (IoVT) enable situation awareness (SAW). The robustness and security of IoVT systems are essential for a sustainable urban environment. While blockchain technology has shown great potential in enabling trust-free and decentralized security mechanisms, directly embedding cryptocurrency oriented blockchain schemes into resource-constrained Internet of Video Things (IoVT) networks at the edge is not feasible. By leveraging Electrical Network Frequency (ENF) signals extracted from multimedia recordings as region-of-recording proofs, this paper proposes EconLedger, an ENF-based consensus mechanism that enables secure and lightweight distributed ledgers for small-scale IoVT edge networks. The proposed consensus mechanism relies on a novel Proof-of-ENF (PoENF) algorithm where a validator is qualified to generate a new block if and only if a proper ENF-containing multimedia signal proof is produced within the current round. The decentralized database (DDB) is adopted in order to guarantee efficiency and resilience of raw ENF proofs on the off-chain storage. A proof-of-concept prototype is developed and tested in a physical IoVT network environment. The experimental results validated the feasibility of the proposed EconLedger to provide a trust-free and partially decentralized security infrastructure for IoVT edge networks. Full article

Methods for inspecting the integrity of audio recordings become a necessity. The evolution of technology allowed the manufacturing of small, performant, recording devices and significantly decreased the difficulty of audio editing. Any person that participates in a conversation can secretly record it, obtaining their own version of the audio captured using their personal device. The recordings can be easily edited afterwards to change the meaning of the message. The challenge is to prove if recordings were tampered with or not. A reliable solution for this was the highly acclaimed Electrical Network Frequency (ENF) criterion. Newer recording devices are built to avoid picking up the electrical network signal because, from the audio content point of view, it represents noise. Thus, the classic ENF criterion becomes less effective for recordings made with newer devices. The paper describes a novel sonic watermarking (i.e., the watermark is acoustically summed with the dialogue) solution, based on an ambient sound that can be easily controlled and is not suspicious to listeners: the ticking of a clock. This signal is used as a masker for frequency-swept (chirp) signals that are used to encode the ENF and embed it into all the recordings made in a room. The ENF embedded using the proposed watermark solution can be extracted and checked at any later moment to determine if a recording has been tampered with, thus allowing the use of the ENF criterion principles in checking the recordings made with newer devices. The experiments highlight that the method offers very good results in ordinary real-world conditions. Full article

The mechanics of damage and fracture process in unidirectional carbon fiber reinforced polymer (CFRP) composites subjected to shear loading (Mode II) were examined using the experimental method of the three-point end-notch flexure (3ENF) test. The CFRP composite consists of [0^o]₁₆ with an insert film in the middle plane for a starter defect. A 3ENF test sample with a span of 50 mm and interface delamination crack length of 12.5 mm was tested to yield the load vs. deformation response. A sudden load drop observed at maximum force value indicates the onset of delamination crack propagation. The results are used to extract the energy release rate, G_IIC, of the laminates with an insert film starter defect. The effect of the starter defect on the magnitude of G_IIC was examined using the CFRP composite sample with a Mode II delamination pre-crack. The higher magnitude of G_IIC for the sample with insert film starter defect was attributed to the initial straight geometry of the notch/interface crack and the toughness of the resin at the notch front of the fabricated film insert. The fractured sample was examined using a micro-computerized tomography scanner to establish the shape of the internal delamination crack front. Results revealed that the interface delamination propagated in a non-uniform manner, leaving a curved-shaped crack profile. Full article

Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework. Full article

Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks. Full article