The development of Internet of things has spawned the vehicular ad hoc network (VANET), which facilitates the safe and comfortable driving. The communications in VANET should be protected to deter against message leakage and modifications. To solve the security issues in VANET communications, we present a dynamic and efficient authenticated group key agreement (AGKA) protocol SC-AGKA with conditional privacy employing the self-certified cryptosystem, and prove its security based on the computational Diffie-Hellman problem. Our SC-AGKA protocol establishes a group key among multiple group users and achieves conditional privacy for the group users. Based on our SC-AGKA protocol, we propose an authentication and group key agreement protocol applying the design in VANET. Additionally, we also notify that through performance comparisons, our protocol has higher security and efficiency in computation and communication compared to other AGKA designs.
In this study, novel Lorentz-like fractional-order dynamical systems are proposed, offering potential applications across various engineering domains. Based on a threedimensional system of the Lorentz-like type of integer order, new nonlinear dynamic systems of fractional order are constructed for four, five, and six state variables. These systems can describe real convective processes in fractal media characterized by a memory effect. For these systems, equilibrium points and stability conditions are determined using the theorem on local asymptotic stability of fractional order systems. Utilizing the frequency domain approximation method, Matlab-Simulink models were developed for novel chaos generators characterized by a fractional order index of 0.95. Through the utilization of Multisim software, we designed electronic circuits to validate the physical feasibility of our proposed systems. The simulation results obtained from both Matlab and Multisim exhibit excellent agreement, reinforcing the reliability of our proposed models. To demonstrate the synchronization of two unidirectionally coupled 3.8d chaotic systems, Matlab-Simulink models were created in two versions. The first version assumes an identical fractional index for both the master and slave systems, while the second version involves different fractional indices for the two systems. These systems were further employed for the chaotic masking of a harmonic signal. An electronic circuit implementing the chaotic masking process in Multisim is also presented. The results obtained from this proposed scheme demonstrate the success of the approach in accomplishing the encryption and decryption procedures effectively.
Today modern web site accelerated by scripts, but the foundation, web page its self is still a static structure. Document Object Model (DOM) represents the structure of web page. Here we show a new approach: It is possible to put timetree and DOM together to shape a new structure named Time Object Model. TOM represents not only a static page but also a dynamic stream. We believe the best way for using TOM is to embed it into a HTML page in real time without changing the existence, it is the only way works now.
In this paper, comprehensive double-directional channel measurements at 300 GHz in various usage scenarios in corridor environments, such as Access, Device-to-device (D2D), and Backhaul over 40 different receiver (Rx) positions using an in-house developed channel sounder, are presented. The measurement results are analyzed and validated by ray tracing (RT) simulation. The quasi-optical propagation properties at 300 GHz make an accurate estimation of relatively simple propagation in a corridor environment possible by using ray optics theory. However, even though non-trivial quadruple-bounce specular reflection paths can be identified in both scenarios, propagation phenomena other than reflection exist irrespective of the Rx positions. Thus, to model the propagation mechanism appropriately, a quasi-deterministic (QD) channel model comprising deterministic and random components is also proposed. The results generated using the proposed model are found to agree well with our prior observations and measurement results. Finally, the paper concludes by characterizing and comparing the channel for all the investigated scenarios in terms of path loss (PL) and large-scale parameters (LSP). On analyzing the measurement results using synthesized power spectra, proposed QD model, and evaluated PL and LSP it is observed that the Access and D2D scenarios share almost similar propagation mechanisms. Furthermore, in the Access and Backhaul scenario the LoS is observed to be affected by the unresolvable ceiling-reflected components. This study, across three different scenarios, can aid the design of next-generation communication systems operating in the THz spectrum.
Controlling the charging and discharging procedures of Lithium-Ion Batteries is of paramount importance as violating safety constraints, such as current deviations, can lead to significant damage to the battery or circuit or interruption in service. Thus, it is crucial to employ a robust controller capable of handling uncertainties and unexpected scenarios. PI controllers have become prevalent in recent years for managing battery dynamics, but they exhibit limited robustness in unpredictable situations. In this paper, we propose a Reinforcement Learning (RL) driven control method as a substitute for the PI controller. The agent is trained using a co-simulation approach with simultaneous employment of Python and Matlab, ensuring an accurate estimation of the environment and, consequently, enhanced performance. A prototype of the proposed controller is developed using dSPACE rapid control prototyper. The performance is compared with the benchmark controller (PI) across different fault scenarios, considering three criteria: overshoot, undershoot, and stabilization time. The comparative analysis reveals that, in most scenarios, the RL agent outperforms the PI controller, exhibiting a remarkable 50% reduction in both overshoot and undershoot compared to the benchmark controller. This research contributes to advancing battery control systems by introducing an RL-based controller that proves to be a more robust alternative, delivering improved performance in the face of uncertainties and fault scenarios.
Enabling artificial intelligence native end-to-end systems in ultra-wideband sub-terahertz spectrum faces several challenges. The particularly complex channel variations and nonlinear behavior of analog components of the transceivers are major obstacles to the over-the-air adaptation of these systems. In this paper, we investigate an edge-based bidirectional long-shortterm memory neural network capable of predicting the channel gain variations in Non-Line-of-Sight conditions. We aim to enable end-to-end autoencoders with a predictive model for scheduling the training phase when the power is above the receiver sensitivity and there are no large fading variations. Otherwise, the training of the end-to-end system will likely fail. With only 16 BiLSTM cells our model is capable of inferring the channel gain variations with a worst-case root mean squared error lower than 0.0547 (i.e., 1.1% compared to the normalized channel gain range). Also, with lower computational complexity, our model decreased the propagation of the error compared to traditional recurrent neural networks and deep-learning-based forecasting models.
This work proposes a novel combination of behavioural-tracking sensors and immersive virtual reality in a gamified proof-of-concept prototype, which demonstrates affective treatment concepts for hypervigilance symptoms. A number of limitations have been identified in current approaches, prompting more advanced techniques that efficiently target hypervigilance at an individual patient level. In response, we developed a virtual reality first-person shooter that responds to inertial user behaviour in a way that aims to combat detrimental symptoms, proposed as an exploratory investigation into innovative technology and its potential to maximise cognitive behavioural therapy outcomes for hypervigilance treatment. The prototype is evaluated through interactive user studies with 22 participants, gathering a large volume of qualitative data regarding participant experiences and opinions after use. Rigorous thematic analysis finds that participants can independently identify the cognitive behavioural therapy purpose of the intervention without prior knowledge of such intentions, and relate efficacious approaches from the literature to their own experiences. Despite prospective apprehension, themes also demonstrate widespread adherence and acceptance of such approaches to hypervigilance treatment, alongside perceived effectiveness both of experienced outcomes and future potential. These results support the validity of combining such technologies in the context of cognitive behavioural therapy interventions, such that the standard of future interventions may be improved.
Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-generation (6G) systems and will facilitate mission-critical scenarios. A design accounting for stringent reliability and latency requirements for URLLC systems poses a challenge for both industry and academia. Recently, unmanned aerial vehicles (UAV) have emerged as a potential candidate to support communications in futuristic wireless systems due to providing favourable channel gains thanks to Lineof-Sight (LoS) communications. However, usage of UAV in cellular infrastructure increases interference in aerial and terrestrial user equipment (UE) limiting the performance gain of UAV-assisted cellular systems. To resolve these issues, we propose low-complexity algorithms for intercell interference coordination (ICIC) using cognitive radio when single and multi-UAVs are deployed in a cellular environment to facilitate URLLC services. Moreover, we model BS-to-UAV (B2U) interference in downlink communication, whereas in uplink we model UAV-to-BS (U2B), UAV-to-UAV (U2U), and UE-to-UAV (UE2U) interference under perfect/imperfect channel state information (CSI). Results demonstrate that the proposed perfect ICIC accounts for fairness among UAV especially in downlink communications compared to conventional ICIC algorithms. Furthermore, in general, the proposed UAV-sensing assisted ICIC and perfect ICIC algorithms yield better performance when compared to conventional ICIC for both uplink and downlink for the single and multi-UAV frameworks. INDEX TERMS URLLC, multi-UAV, cognitive radio, intercell interference coordination (ICIC).
In this work, we propose a model that describes the temporal evolution of the threshold voltage and trapped charge density in Thin-Film Transistors (TFTs) under dynamic conditions, paving the way for the characterization of memory transistors. The model is expressed as a first order differential equation for the trapped charge density, which is controlled by a time constant and an independent term proportional to the drain current. The time dependent threshold voltage is introduced in a previously developed compact model for TFTs with special consideration to the contact effects. The combination of both models and the use of an evolutionary parameter extraction procedure allow for reproducing the experimental dynamic behavior of TFTs. The results of the model and the evolutionary procedure have been validated with published experimental data of pentacene-based transistors. The procedure is able to simultaneously reproduce three kinds of experiments with different initialization routines and constraints in each of them: output and transfer characteristics with hysteresis and current transients characteristics.
Text generation is an important method to generate high quality and available product description from product title. For the product description generation for online E-commerce application, the main problem is how to improve the quality of generated text. In other words, how we judge the quality of text. If all texts are already positive and available, then we find it impossible to manually judge which text is the better text for a product. So if we cannot judge which is a better text manually, we cannot improve the quality of generated text. In E-commerce, product description is to attract shoppers and improve sales. So we design a method to improve the quality of generated text based on user buying behaviour. Online result shows that our approach improve the sales of products by improving the text quality.
In this paper, we explore an indoor downlink cooperative hybrid visible light communication (VLC)/radio frequency (RF) scenario using a relay node to reduce system outage probability. In particular, information can be transmitted to the end user either directly through the VLC link or via the relay node. To re-transmit the decoded information to the end user through the RF link the relay utilizes harvested energy from the source light emitting diode (LED) at the ceiling. We derive the analytical expression for the outage probability of the relayaided hybrid VLC/RF system, considering the randomness of location and receiver orientation for both the relay and the end user. Furthermore, we investigate the effects of the direct current (DC) bias, data rate threshold, and different distributions for the location and orientation of the end user and relay on the outage probability of the system.
The value of colorless, directionless, and contentionless (CDC-)ROADM (reconfigurable optical add/drop multiplexer) nodes is strongly contested in the optical networking community. In this work, we compare known ROADM node designs incorporating different switching elements and account for their total nodal switching state support (in consideration of both channel routing and add/drop). This allows us to quantify the impact of directional/contentional accessibility constraints to add/drop transceivers. By considering the network node entity as a permutation network among its ingress/egress ports for all wavelength channels, which covers both through routing and add/drop assignments, we tabulate the node’s switching capacity, or total allowable connection states, per different ROADM architecture, hardware constraints, and finite number of add/drop transceivers. We further introduce the impact of idle wavelength channels on fiber links, as well as bidirectional routing assignments. Our switching capacity enumerations demonstrate that CDC-ROADM outperforms other designs, but parallel contentional aggregation hardware (partially contentional) and directional transceivers (permanently assigned to port directions) offer competitive performance under certain scenarios (at lower and higher number of deployed transceivers, or a combination of both). These findings suggest that design alternatives to the “difficult to implement” CDC-ROADM exist, with nearly equivalent switching capacity, and additional system considerations must be taken into account for ROADM design selection such as hardware availability, cost, impact of traffic churn, and disaster recovery with over-provisioned add/drop transceivers.
Speech overlap, which occurs when multiple people speak simultaneously, poses a significant challenge in audio and speech processing. The presence of overlapping speech segments significantly degrades the performance of technologies such as Automatic Speech Recognition (ASR), speaker identification, and diarization systems. This degradation in performance becomes more significant in diverse acoustic environments with background noise and reverberation. To effectively address this issue, we introduce BiConNet. This novel dual-branch architecture combines the strengths of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) for robust detection of overlapping speech in diverse acoustic conditions. The CNN branch is used for frame-level spectral feature extraction, while the BiLSTM branch captures temporal dependencies from both forward and backward directions. Features from both branches are concatenated, resulting in a robust feature representation. We also examined the impact of Mel Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GTCC), and Power Normalized Cepstral Coefficients (PNCC) as spectral-based features on BiConNet's performance. To validate its effectiveness in various acoustic environments, we developed a constructed data set derived from the GRID corpus, including conversations with different gender combinations and recording conditions, such as clean, noisy, reverberant, and combined noise and reverberation conditions. Experimental results show that BiConNet outperforms various state-of-the-art methods in detecting overlapping speech segments under these conditions. Furthermore, our analysis of computational efficiency reveals that BiConNet provides competitive training and inference times, demonstrating its practicability for real-world applications.
Stringent physical requirements need to be met for the high performing surface-electrode ion traps used in quantum computing, sensing, and timekeeping. In particular, these traps must survive a high temperature environment for vacuum chamber preparation and support high voltage rf on closely spaced electrodes. Due to the use of gold wirebonds on aluminum pads, intermetallic growth can lead to wirebond failure via breakage or high resistance, limiting the lifetime of a trap assembly to a single multi-day bake at 200 • C. Using traditional thick metal stacks to prevent intermetallic growth, however, can result in trap failure due to rf breakdown events. Through high temperature experiments we conclude that an ideal metal stack for ion traps is Ti20nm/Pt100nm/Au250nm which allows for a bakeable time of roughly 86 days without compromising the trap voltage performance. This increase in the bakable lifetime of ion traps will remove the need to discard otherwise functional ion traps when vacuum hardware is upgraded, which will greatly benefit ion trap experiments.
This study advances indoor environment modeling by focusing on the optimal placement of sensors. Our approach involves creating a detailed environment model from a 3D point cloud by identifying spatial boundaries and furniture in indoor spaces, which are then represented as a series of polygons. To validate our method, we compare its performance against ground truth data, demonstrating high accuracy in both simple and complex environments. The core of our study is a comprehensive experiment that evaluates the effectiveness of three evolutionary nature-inspired genetic and three metaheuristic iterative optimization algorithms in solving the sensor placement problem in a complex environment scenario. We perform a statistical analysis to understand the impact of the choice of optimization algorithm and the number of sensors on the achieved spatial coverage. This analysis provides insights into the comparative effectiveness of various evolutionary algorithms in enhancing sensor network design within intricate indoor spaces. In particular, the Artificial Bee Colony algorithm consistently delivered superior results.
Cryptography has become an essential tool in information security, preserving data confidentiality, integrity, and availability. However, despite rigorous analysis, cryptographic algorithms may still be susceptible to attack when used on real-world devices. Side-channel attacks (SCAs) are physical attacks that target cryptographic equipment through quantifiable phenomena such as power consumption, operational times, and EM radiation. These attacks are considered to be a significant threat to cryptography since they compromise the integrity of the algorithm by obtaining the internal cryptographic key of a device by seeing its physical implementation. The literature on SCAs has focused on real-world devices, yet with the growing popularity of sophisticated devices like smartphones, fresh approaches to SCAs are necessary. One such approach is electromagnetic side-channel analysis (EM-SCA), which gathers information by listening to electromagnetic (EM) radiation. EM-SCA has been demonstrated to recover sensitive data like encryption keys and has the potential to identify malicious software, retrieve data, and identify program activity. This study aims to evaluate how well EM-SCA compromises encryption under various application scenarios, as well as examine the role of EM-SCA in digital forensics and law enforcement. Regarding this, addressing the susceptibility of encryption algorithms to EM-SCA approaches can provide digital forensic investigators with the tools they desire to overcome the challenges posed by strong encryption, allowing them to continue playing a crucial role in law enforcement and the justice system. Furthermore, this paper seeks to define the current state of EM-SCA in terms of attacking encryption, the encryption algorithms and encrypted devices that are most vulnerable and resistant to EM-SCA, and the most promising EM-SCA on encryption approaches. This study will provide a comprehensive analysis of EM-SCA in the context of law enforcement and digital forensics and point towards potential directions for further research.
In order to low-frequency stabilize the electric field integral equation (EFIE) when discretized with divergence conforming B-spline based basis and testing functions in an isogeometric approach, we propose a corresponding quasi-Helmholtz preconditioner. To this end, we derive i) a loop-star decomposition for the B-spline basis in the form of sparse mapping matrices applicable to arbitrary polynomial orders of the basis as well as to open and closed geometries described by single-or multipatch parametric surfaces (as an example non-uniform rational Bsplines (NURBS) surfaces are considered). Based on the loopstar analysis, we show ii) that quasi-Helmholtz projectors can be defined efficiently. This renders the proposed low-frequency stabilization directly applicable to multiply-connected geometries without the need to search for global loops and results in betterconditioned system matrices compared to directly using the loopstar basis. Numerical results demonstrate the effectiveness of the proposed approach.
In order to accurately compute scattered and radiated fields in the presence of arbitrary excitations, a lowfrequency stable discretization of the right-hand side (RHS) of a quasi-Helmholtz preconditioned electric field integral equation (EFIE) on multiply-connected geometries is introduced, which avoids an ad-hoc extraction of the static contribution of the RHS when tested with solenoidal functions. To obtain an excitation agnostic approach, our approach generalizes a technique to multiply-connected geometries where the testing of the RHS with loop functions is replaced by a testing of the normal component of the magnetic field with a scalar function. To this end, we leverage orientable global loop functions that are formed by a chain of Rao-Wilton-Glisson (RWG) functions around the holes and handles of the geometry, for which we introduce cap surfaces that allow to uniquely define a suitable scalar function. We show that this approach works with open and closed, orientable and non-orientable geometries. The numerical results demonstrate the effectiveness of this approach.