Behavior Of Devices Research Articles

Efficiency scaling of inductively coupled plasmas for radio frequency propulsion systems

AbstractA generalized inductively coupled plasma (ICP) efficiency model is derived, which may be used to predict plasma generation efficiency with many types of radio frequency (RF) electric propulsion system. The analytical ICP discharge model is used to examine device scaling, and investigate trends in ICP performance as a function of geometry and system inputs. The ICP efficiency model is applied to an analytical RF cathode model to predict device behavior. Results from the updated RF cathode model show improvements in agreement with experimental data.

Inverse modeling of electromagnetic emissions using grey wolf optimization

The paper proposes an inverse modeling approach for evaluating radiated electromagnetic compatibility (EMC) in electronic components and circuits. To imitate the electromagnetic behavior of real devices, the suggested methodology generates an equivalent model using elementary dipoles. The Grey Wolf Optimization (GWO) algorithm solves an inverse problem to identify dipole parameters such as locations, intensities, and orientations. This technique improves model accuracy and robustness through a two-phase exploration and exploitation process that allows for effective parameter tuning while reducing simulation time and computational resources. Recent research in near-field measurement techniques illustrates the importance of reliable predictive methodologies in EMC evaluation. This method provides significant information on the impacts of power systems on surrounding electronic circuits, making it a useful tool for EMC testing. It also promotes the development of immunity models for high-frequency applications. Overall, this methodology simplifies the examination procedure, resulting in higher design efficiency for a wide range of electronic systems.

Synergistic Approaches to Enhance IoT Intrusion Detection: Balancing Features through Combined Learning

The Internet of Things (IoT) plays a crucial role in ensuring security by preventing unauthorized access, malware infections, and malicious activities. IoT monitors network traffic as well as device behaviour to identify potential threats and take appropriate mitigation measures. However, there is a need for an IoT Intrusion Detection system with enhanced generalization capabilities, leveraging deep learning and advanced anomaly detection techniques. This study presents an innovative approach to IoT IDS that combines SMOTE-Tomek link and BTLBO, CNN with XGB classifier which aims to address data imbalances, improve model performance, reduce misclassifications, and improve overall dataset quality. The proposed IoT IDS system, using the IoT-23 dataset, achieves 99.90% accuracy and a low error rate, all while requiring significantly less execution time. This work represents a significant step forward in IoT security, offering a robust and efficient IDS solution tailored to the changing challenges of the interconnected world.

Experimental and Numerical Studies of the Temperature Field in a Dielectrophoretic Cell Separation Device Subject to Joule Heating.

Technologies for rapid and high-throughput separation of rare cells from large populations of other types of cells have recently attracted much attention in the field of bioengineering. Among the various cell separation technologies proposed in the past, dielectrophoresis has shown particular promise because of its preciseness of manipulation and noninvasiveness to cells. However, one drawback of dielectrophoresis devices is that their application of high voltage generates Joule heat that exposes the cells within the device to high temperatures. To further explore this problem, this study investigated the temperature field in a previously developed cell separation device in detail. The temperature rise at the bottom of the microfluidic channel in the device was measured using a micro-LIF method. Moreover, the thermofluidic behavior of the cell separation device was numerically investigated by adopting a heat generation model that takes the electric-field-dependent heat generation term into account in the energy equation. Under the operating conditions of the previously developed cell separation device, the experimentally obtained temperature rise in the device was approximately 20 °C, and the numerical simulation results generally agreed well. Next, parametric calculations were performed with changes in the flow rate of the cell sample solution and the solution conductivity, and a temperature increase of more than 40 °C was predicted. The results demonstrated that an increase in temperature within the cell separation device may have a significant impact on the physiological functions of the cells, depending on the operating conditions of the device.

Detecting and Analyzing Network Attacks: A Time-Series Analysis Using the Kitsune Dataset

Network security is a critical concern in today’s digital world, requiring efficient methods for the automatic detection and analysis of cyber attacks. This study uses the Kitsune Network Attack Dataset to explore network traffic behavior for IoT devices under various attack scenarios, including ARP MitM, SYN DoS, and Mirai Botnet. Utilizing Python-based data analysis tools, we preprocess and analyze millions of network packets to uncover patterns indicative of malicious activities. The study employs packet-level time-series analysis to visualize traffic patterns and detect anomalies specific to each attack type. Key findings include high packet volumes in attacks such as SSDP Flood and Mirai Botnet, with the Mirai Botnet attack involving multiple IP addresses and lasting over 2 hours. Notable attack-specific behaviors include high traffic on port -1 and targeted traffic on specific ports like 53195. The SYN DoS and Mirai Botnet attacks are characterized by their prolonged durations, suggesting significant disruption. Overall, the study highlights distinctive attack patterns and underscores the importance of understanding these characteristics to enhance detection and response mechanisms.

Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Thermally induced vibration of photovoltaic honeycomb-based-thermoelectric hybrid device

This paper presents a comprehensive investigation of thermally induced vibration (TIV) of elastically constrained photovoltaic honeycomb-based-thermoelectric hybrid device under space thermal environment. Based on four-variable refined quasi-3D higher-order theory as well as virtual boundary spring technique, the equation of motion of the hybrid device is derived by using Lagrange equation. The nonlinear temperature profile is obtained via finite difference scheme and the TIV responses are solved by employing Newmark approach. The effects of boundary condition, size of honeycomb cell, damping effect, thickness of insulation layer and radius of curvature on TIV of the hybrid device are studied. Results show that the TIV response of the hybrid device can be greatly weakened by replacing the dense thermoelectric leg with honeycomb-based thermoelectric leg. Designing the honeycomb cell as re-entrant architecture possesses a better suppression effect on TIV response. The amplitude of TIV reduces with increasing the thickness-length ratio and decreasing the height-length ratio of honeycomb cell. A larger thickness of insulation layer can weaken the TIV response. The oscillation of TIV response can be eliminated by fabricating hybrid device as circular cylindrical and hyperbolic parabolic shallow shells. The stiffer the boundary conditions, the less prone to TIV behavior of the hybrid device. The introduction of damping effect can suppress TIV response, but it simultaneously causes the occurrence of thermal snap. In general, the research provides several design references to weaken the TIV response of the hybrid device under space heat flux.

Electrical properties and conduction mechanisms of ε-GaSe films for selector and phase-change memory applications

Owing to the pressing requirement for advanced memory solutions driven by explosive data growth, nonvolatile and fast-operating 1S1R structured XPoint memory has become a prominent focus, with recent interest shifting towards the 1S structured selector-only memory. The use of chalcogenide-based materials in these technologies constrains the application of such memories because of their complex quaternary compositions. This study investigated the previously unreported potential of GaSe, a simple binary Se-based chalcogenide, for applications as a selector and phase-change memory (PCM). In this study, ε-GaSe thin films were deposited using thermal evaporation and subsequently annealed in a Se atmosphere for 1 and 2 h. These ε-GaSe thin film devices incorporated metal–insulator–metal construction to exhibit both selector and PCM characteristics. The ε-GaSe device that was Se-annealed for 1 h demonstrated superior performance with a lower threshold voltage and off current, high selectivity as a selector, and reduced set voltage and reset power as a PCM. These characteristics render the proposed device a highly promising candidate for dual selector/PCM applications. To explain the electrical behavior of the ε-GaSe device, we propose the conduction mechanism model depending on the Se-annealing time, which is related to the change in the physical properties of ε-GaSe.

Hierarchical optimal operation for bipolar DC distribution networks with remote residential communities

To facilitate the seamless integration of renewable energy, bipolar DC distribution networks (Bi-DCDNs) have been widely adopted in various applications, including the Shenzhen Future Building, the Boeing 787 aircraft, and DC LED lighting systems in Singapore. Bi-DCDNs incorporate diverse flexible devices to improve both economic efficiency and security. However, a comprehensive coordination framework considering the regulatory heterogeneity among these flexible devices remains absent. Hence, this paper proposes a hierarchical coordination framework for flexible devices in Bi-DCDNs. More specifically, the upper-level model considers the operational differences of the DC transformer (DCT) in various modes to determine the optimal switching scheme for reducing losses; In the lower-level model, the control parameters of the DCT, energy storage systems (ESSs), and DC electrical springs (DCESs) are coordinated to enhance voltage quality. Furthermore, to accurately capture the steady-state behavior of flexible devices, the hierarchical framework incorporates the Newton-Raphson power flow method. This method formulates a steady-state model for multiple flexible devices and demonstrates the impact of different control modes of DCT on power flow. Subsequently, a genetic algorithm is used to solve the proposed model, ensuring that suboptimal decisions made at the upper level are rectified at the lower level, and vice versa. The numerical results indicate that the proposed framework achieves the optimal operation for both reduced losses and enhanced voltage quality in Bi-DCDNs. Furthermore, it exhibits advantageous applications for Bi-DCDNs with additional DCTs for remote residential communities.

Directing Nanoparticle Organization in Response to Diverse Chemical Inputs.

Signaling cascades are crucial for transducing stimuli in biological systems, enabling multiple stimuli to regulate a downstream target with precisely controlled timing and amplifying signals through a series of intermediary reactions. Developing a robust signaling system with such capabilities would be pivotal for programming complex behaviors in synthetic DNA-based molecular devices. However, although "software" such as nucleic acid circuits could potentially be harnessed to relay signals to DNA-based nanostructure hardware, such explorations have been limited. Here, we develop a platform for transducing a variety of stimuli via messenger-mediated reactions to regulate the release and reloading of gold nanoparticles (AuNPs) in a 3D DNA framework. In the first step, an in vitro transcription circuit is engineered to sense and amplify chemical stimuli, including arbitrary DNA sequences and proteins, producing RNA. In the second step, the RNA releases the DNA-coated AuNPs from the DNA framework via a strand displacement reaction. AuNP reloading is controlled by a separate step driven by degradation of the RNA. Our platform holds promise for applications requiring dynamic multiagent control over DNA-based devices, offering a versatile tool for advanced molecular device engineering.

Role of sulphur in resistive switching behavior of natural rubber-based memory

The rising environmental awareness has spurred the extensive use of green materials in electronic applications, with bio-organic materials emerging as attractive alternatives to inorganic and organic materials due to their natural biocompatibility, biodegradability, and eco-friendliness. This study showcases the natural rubber based resistive switching memory devices and how varying sulphur concentrations (0 - 0.8 wt.%) in natural rubber thin films impact the resistive switching characteristics. The natural rubber was formulated and processed into a thin film deposited on an ITO substrate as the bottom electrode and with an Ag film as the top electrode. The addition of sulphur modifies the degree of crosslinking in the natural rubber thin film, from which the concentration of -C=C- group and density of defect site (S+) are affected, and hence the resistive switching behavior of the memory device. The devices exhibit bipolar resistance with symmetric switching characteristics which are attributed to the formation of conductive paths facilitate by electron transport along -C=C- and S+ defect sites between the two electrodes. Notably, a sample with 0.2 wt.% sulphur exhibits a high ON/OFF ratio (104), a large memory window (5.5 V), prolonged data retention (10 years), and reliable endurance (120 cycles). These findings highlight the potential of natural rubber as a promising material for eco-friendly resistive-switching random access memory applications.&#xD.

The Fabrication of Polyimide-Based Tunable Charge Traps Ternary Memristors Doped with Ni-Co Coated Carbon Composite Nanofibers.

In the dynamic fields of information science and electronic technology, there is a notable trend towards leveraging carbon materials, favored for their ease of synthesis, biocompatibility, and abundance. This trend is particularly evident in the development of memristors, benefiting from the unique electronic properties of carbon to enhance device performance. This study utilizes sensitized chemical evaporation and spin-coating carbonization techniques to fabricate nickel-cobalt coated carbon composite nanofibers (SC-NCMNTs). Novel polyimide (PI) matrix composite memory devices were fabricated using in situ polymerization technology. Transmission electron microscopy (TEM) and micro-Raman spectroscopy analyses validated the presence of dual interface structures located between the Ni-Co-MWNTs, carbon composite nanofibers, and PI matrix, revealing a significant number of defects within the SC-NCMNTs/PI composite films. Consequently, this results in a tunable charge trap-based ternary resistive switching behavior of the composite memory devices, exhibiting a high ON/OFF current ratio of 104 and a retention time of 2500 s at an operating voltage of less than 3 V. The mechanism of resistive switching is thoroughly elucidated through a comprehensive charge transport model, incorporating molecular orbital energy levels. This study provides valuable insights for the rational design and fabrication of efficient memristors characterized by multilevel resistive switching states.

Transmission electron microscopy of epitaxial semiconductor materials and devices

Abstract The transmission electron microscope (TEM) is a powerful imaging, diffraction and spectroscopy tool that has revolutionized the field of microscopy. It has contributed to numerous breakthroughs in various scientific disciplines. TEM-based techniques can offer atomic resolution as well as elemental analysis, which benefit the study of epitaxial semiconductors and their related optoelectronic devices on the atomic scale. The design and optimization of the device performance depend on three key factors: the control of strain at nanometer scale, control of the formation and propagation of defects as well as the control of local electronic properties. Manipulation and optimization are only possible if the key factors can be characterized precisely. Herein, the TEM techniques for strain analysis, defect characterization and bandgap evaluation are reviewed and discussed. Lately, with the development of in-situ TEM techniques, researchers have been able to observe dynamic processes and study the behaviour of materials and devices under realistic conditions (in gaseous atmosphere or in liquids, at elevated or cryogenic temperatures, under strain, bias or illumination) in real-time with extremely high spatial resolution. This review explores the impact and significance of in-situ TEM in the field of semiconductors.

Role of Trapping in Non‐Volatility of Electrochemical Neuromorphic Organic Devices

AbstractArtificial Neural Networks (ANN) require a better platform to reduce their energy consumption and achieve their full potential. Electrochemical devices like the Electrochemical Neuromorphic Organic Device (ENODe) stand out as a potential building block for ANNs, due to their lower energy demand, in addition to their biocompatibility and access to multiple and stable memory levels. However, the non‐volatile effect observed in these devices is not yet fully understood. Hence, here we propose a 2D drift‐diffusion model that is capable to reproduce the device behavior. The model relies on the assumption of trapping sites for cations, which are increasingly filled or emptied during subsequent pre‐synaptic pulses. The model is verified by experiments on devices with varying post‐synaptic dimensions. Overall, the results provide a framework to discuss ENODe operation and design strategies for ENODes with well‐controlled memory states.

Efficient Estimation of Synthetic Indicators for the Assessment of Nonlinear Systems Quality

The availability of synthetic indicators of the degree and type of nonlinearity in systems is used in various fields to assess system quality or to highlight possible malfunctions. Different distortion or damage indexes are synthetic measures designed (and standardized) to evaluate the frequency trend of specific aspects resulting from the nonlinear behavior of the system under consideration. The different measures of deviation from linear behavior quantitatively consider the system and its nonlinearity characteristics; they were defined according to practically feasible measurement methodologies and the various aspects of the system’s nonlinearity that needed to be highlighted. In parallel, techniques for representing and modeling nonlinear systems have been defined, capable of describing the system in a more general way, attempting to capture its input–output characteristics by varying the level of stress to which the system is subjected. Numerous modeling techniques have been proposed, aimed at representing the nonlinear behavior of physical devices. In this paper, after an extensive description of the Hammerstein model identification technique based on swept sinusoidal signals, we show how the nonlinear model of the system can be used to obtain accurate estimates of the parameter aimed at describing the nonlinearity characteristics of the system. This extensive description makes it possible to point out that the same Hammerstein model can be obtained not only from a single type of excitation, but it is shown that the identification technique can be extended to input signals of different types. The description of the method also makes clear the motivation behind the introduction of the proposed original technique for estimating, from a single measurement, the model parameters of the nonlinear system—and from these the synthetic estimators—relative to multiple values of the input signal amplitude, thus enabling a considerable increase in the estimation efficiency of these parameters. The proposed technique is verified with both synthetic and laboratory experiments, demonstrating the effectiveness of the method in evaluating nonlinear system parameters, distortion estimates, and parameters defined for an early detection of defects of the structure.

Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.

Light‐State Rectification Behaviors Induced by Interlayer Excitons in Mixed‐Dimensional Single‐Crystalline Perovskite Heterostructures

AbstractStacking different layers into van der Waals heterostructures (vdWHs) provides an attractive approach to integrating intrinsic prominent properties of the constituents. Mixed‐dimensional vdWHs that can interact with external fields are particularly intriguing because they host interlayer excitons (IXs) and thereby exhibiting rich physical phenomena and novel device behaviors. However, previous studies of IXs have primarily focused on TMDs systems, leaving the excitonic phenomena and correlated physics in perovskite heterostructures poorly understood. Here, the 2D‐3D ((BA)2PbBr4‐CsPbBr3) single‐crystalline perovskite vdWHs are designed to discover the modulation effects of IX states on the optical and optoelectronic properties. Interestingly, the vdWHs exhibit a newly emerging IXs emission at 546 nm and an obvious broadening phenomenon in FWHM. The vdWHs‐based photodetectors display novel light‐state rectification behaviors that can change the output photocurrents from fully off states to rectified states after illumination, resulting in a high rectification ratio up to 24342 and an obvious enhanced on/off ratio of 7000. The attractive results are attributed to the presence of IX states, which facilitate carrier hopping at the interfaces. This work establishes mixed‐dimensional single‐crystalline perovskite heterolayers as a highly adaptable platform for exciton research and applications.

Implementation of an experimental set-up for insertion loss measurements of sonic crystal acoustic screens

The use of metamaterials for the implementation of noise control devices in the context of environmental acoustics is a topic that has gained more and more attention in recent years. The complex design of such devices and the difficulty of their construction in real-world applications complicates the acquisition of experimental data related to their acoustical performance. Numerical simulation tools are used to simulate the behaviour of these novel noise control devices in their design phase and help to validate the designs by comparing them with experimental data. This data, usually acquired under controlled conditions, holds significant importance in confirming the increasingly precise numerical models that have been developed over the years to simulate the behaviour of these physical systems. In this paper, an experimental set-up that enables acquisition of acoustical performance data for a particular metamaterial-based noise control device, consisting of networks of isolated scatterers, is presented. This prototype possesses several distinctive characteristics that render it particularly interesting, including its adaptability, ease of use, speed of obtaining results, and, particularly, its economic viability in relation to the tests that can be conducted in an anechoic chamber.

Body heat energy driven knitted thermoelectric garments with personal cooling

Wearable, thermoelectric (TE) devices have gained significant interest in recent years as they can both generate output power from converting body heat into electrical energy and be utilized in personal cooling through the Peltier effect. Currently, researched wearable TE cooling devices focus on devices that can be attached to the human body in specific locations and use heat sinks or elongated thermoelectric pillars to increase their effectiveness, making them hard and bulky. In addition, these devices are attached, not integrated, to wearable garments, making their manufacturing a multi-step process. To address this issue, we developed a knitted TE garment through a single-step manufacturing process that offers energy generation and personal cooling capabilities. The functioning of the cooling device was mathematically modelled and analyzed to understand its scalability, optimization, and to show its potential. This garment features excellent thermoelectric generation of 7.48 mV and 0.46 mA on the human body, and the ability to cool the human body up to 1.5 °C. The observations proved that the mathematical model could predict the behavior of the TE device closely. The results showed that the proposed method of knitted TE cooling garment manufacturing is a viable and scalable process.

Advancing healthcare systems: A tri‐tier architecture by using data communication, AI data generative and regulation and compliance standards

AbstractTraditional healthcare systems have suffered from different data communication, security, data processing, and compliance issues. The traditional systems are also not well equipped to handle the new technologies like Artificial Intelligence (AI) by enabling more accurate diagnostics, personalized treatment plans, and improved patient outcomes. The existing data communication and security protocols and compliance are also not fully implemented to tackle the system's challenges. This article proposes a Tri‐Tier architecture by using data communication, AI data generative, and regulation and compliance tiers. The data communication tier is based on advanced sensing and monitoring technologies like cloud and edge‐based systems integrated with security detection mechanisms. The edge and cloud layer provides the all functions of the perception layer like smart sensing, visual sensing, and monitoring services, and can control the device's perception and behaviour. The second tier provides the AI data generative functionalities to handle real‐time synthetic medical images for predictive analytics to enhance patient care. This tier also automates routine tasks, such as administrative work and data analysis, which can free up healthcare professionals to focus on more complex tasks. The last regulation and compliance tier is responsible for handling the standards and compliance for healthcare systems. Experiments are conducted to test the data communication and security level of the proposed architecture. The results showed the suitability of existing solutions and synchronization with the proposed architecture.