Steganography in multimedia aims to embed secret data into an innocent looking multimedia cover object. This embedding introduces some distortion to the cover object and produces a corresponding stego object. The embedding distortion is measured by a cost function that determines the detection probability of the existence of the embedded secret data. A cost function related to the maximum embedding rate is typically employed to evaluate a steganographic system. In addition, the distribution of multimedia sources follows the Gibbs distribution which is a complex statistical model that restricts analysis. Thus, previous multimedia steganographic approaches either assume a relaxed distribution or presume a proposition on the maximum embedding rate and then try to prove it is correct. Conversely, this paper introduces an analytic approach to determining the maximum embedding rate in multimedia cover objects through a constrained optimization problem concerning the relationship between the maximum embedding rate and the probability of detection by any steganographic detector. The KL-divergence between the distributions for the cover and stego objects is used as the cost function as it upper bounds the performance of the optimal steganographic detector. An equivalence between the Gibbs and correlated-multivariate-quantized-Gaussian distributions is established to solve this optimization problem. The solution provides an analytic form for the maximum embedding rate in terms of the WrightOmega function. Moreover, it is proven that the maximum embedding rate is in agreement with the commonly used Square Root Law (SRL) for steganography, but the solution presented here is more accurate. Finally, the theoretical results obtained are verified experimentally.

This work presents a methodology for the joint calibration and synchronization of two arrays of microphones and loudspeakers. The problem is modeled as estimation of the rigid motion of one array with respect to the other, as well as estimation of the synchronization mismatch between the two. The proposed method uses dedicated signals emitted by the loudspeakers of the two arrays to compute a set of time of arrival (TOA) estimates. Through a simple transformation, estimated TOAs are converted into a set of linearly independent time difference of arrival (TDOA) measurements, which are modeled by a system of nonlinear equations in the unknown parameters of interest. A maximum likelihood estimate is then given as the solution to a nonlinear weighted least squares (NWLS) problem, which is optimized applying a parallelizable variant of Particle Swarm Optimization (PSO). In this paper, we also derive the Cramer-Rao lower bound (CRLB), and benchmark it against the proposed method in a series of Monte Carlo (MC) simulations. Results show that the proposed method attains high-performance comparable to the CRLB.

Added substance Manufacturing (AM) of metallic designs is a warm cycle of layer by layer metal added substance fabricating measure produces parts straightforwardly from 3D CAD models. In this assembling interaction confined electrochemical affidavit joins with the added substance producing technique to make metal parts at room temperature. In this paper, the attainability of Mask-less Electrochemical Additive Manufacturing (ECAM), as a non-warm interaction is considered. Layer by layer testimony has been finished utilizing the electrochemical tips to make nickel microstructures. All the while beat wave qualities and their impacts on affidavit have been considered. Confined electrodeposition (LED) was investigated as an AM the interaction with high power over measure boundaries and yield boundaries. The confinement of electrodeposition is completed by utilizing Ultra microelectrodes (UME) and low tossing power electrolytes. Variety in some cycle boundaries, for example, voltage and terminal hole are found to have a high impact on yield boundaries like thickness. The reproductions can anticipate the yield width of affidavit of analyses with a blunder of 8- 30%, so it can possibly apply as an added substance-producing strategy of complex three-dimensional (3D) parts on the microscale.

Abstract: In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush–Kuhn–Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.

Efficient and compact frequency converters are essential for frequency stabilization of terahertz sources. In this paper, we present a 3.5-THz, x6-harmonic, integrated Schottky diode mixer operating at room temperature. The designed frequency converter is based on a single-ended, planar Schottky diode with a sub-micron anode contact area defined on a suspended 2-um ultra-thin GaAs substrate. The dc-grounded anode pad was combined with the radio frequency E-plane probe, which resulted in an electrically compact circuit. At 200 MHz intermediate frequency, a mixer conversion loss of about 59 dB is measured resulting in a 40 dB signal-to-noise ratio in a phase-locking application. Using a quasi-static diode model combined with electromagnetic simulations, good agreement with the measured results was obtained. Harmonic frequency converters without the need of cryogenic cooling will help in the realization of highly sensitive space and air-borne heterodyne receivers.

Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.

Trust models play an important role in Internet of Things (IoT) as it provides a means of finding whether a given device can provide a service to a satisfactory level as well as a means for identifying potentially malicious devices in the network. Context awareness in trust models allows a trustor to filter and aggregate evidence by their relevance to the current situation. Context awareness is important in the formulation of trust in IoT networks due to their heterogeneity and due to the dynamic changes in the capabilities of IoT devices. In this paper,we have proposed a new type of context-based attack on context aware trust models for IoT systems. An adversary is able to manipulate the context and impact a target group of IoT devices, while other devices in non-targeted groups are not even aware of the attack. We have demonstrated the effectiveness of this new type of attack on six previously proposed trust models. Through practical simulations and theoretical proofs, we show that the adversaries can launch such context-based attacks against a targeted group of IoT devices in the network. The paper also proposes a new trust management system that can mitigate such context-based attacks.

Complementarity has become an essential concept in energy supply systems. Although there are some other metrics, most studies use correlation coefficients to quantify complementarity. The standard interpretation is that a high negative correlation indicates a high degree of complementarity. However, we show that the correlation is not an entirely satisfactory measure of complementarity. As an alternative, we propose a new index based on the mathematical concept of the total variation. For two time series, the new index φ is one minus the ratio of the total variation of the sum to the sum of the two series’ total variation. We apply the index first to an auto-regressive (AR) process and then to various Colombian electric system series. The AR case clearly illustrates the limitations of the correlation coefficient as a measure of complementarity. We then evaluate complementarity across various space-time scales in the Colombian power sectors, considering hydro and wind projects. The complementarity assessment on a broad temporal and geographical scale helps analyze large power systems with different energy sources. The case study of the Colombian hydropower systems suggests that φ is better than ρ because (i) it considers scale, whereas ρ, being non-dimensional, is insensitive to the scale and even to the physical dimensions of the variables; (ii) one can apply φ to more than two resources; and (iii) ρ tends to overestimate complementarity.

Deep clustering incorporates embedding into clustering to find a lower dimensional space appropriate for clustering. In this paper, we propose a novel deep clustering framework with self-supervision using pairwise data similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoderâ\euro™s latent space. In the second phase, we propose to employ pairwise data similarities to create a K-dimensional space that is capable of accommodating more complex cluster distributions, hence providing more accurate clustering performance. K is the number of clusters. The autoencoderâ\euro™s latent space obtained in the first phase is used as the input of the second phase. The effectiveness of both phases is demonstrated on seven benchmark datasets by conducting a rigorous set of experiments. The DCSS code is available: https://github.com/Armanfard-Lab/DCSS

Modeling the electromagnetic response of carbon nanotube (CNT) reinforced composites is inherently a three dimensional (3D) multi-scale problem that is challenging to solve in real-time for nondestructive evaluation applications. This article presents a fast and accurate full-wave electromagnetic solver based on a multi-layer dyadic Green’s function approach. In this approach, we account for the effects of the dielectric slab, where the CNTs are embedded, without explicitly discretizing its interfaces. Due to their large aspect ratios, the CNTs are modeled as arbitrary thin wires (ATWs), and the method of moment (MoM) formulation with distributed line impedance is used to solve for their coupled currents. The accuracy of the inhouse solver is validated against commercial method of moment (MoM) and finite element method (FEM) solvers over a broad range of frequencies (from 1 GHz to 10 THz) and for a wide range of dielectric slab properties. Examples of 100nm long vertical and horizontal CNTs embedded in a 1 μm thick lossy dielectric substrate are presented. The in-house solver provides more than 50 ✕ speed up while solving the vertical CNT, and more than 570 ✕ speed up while solving the horizontal CNT than a commercial MoM solver over the GHz to THz frequency range.

Paper is based on a model that is built to detect malicious URLs using machine learning techniques.

Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and non-alcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

To minimize the curtailment of renewable generation and incentivize grid-scale energy storage deployment, a concept of combining stationary and mobile applications of battery energy storage systems built within renewable energy farms is proposed. A simulation-based optimization model is developed to obtain the optimal design parameters such as battery capacity and power ratings by solving a multi-objective optimization problem that aims to maximize the economic profitability, the energy provided for transportation electrification, the demand peak shaving, and the renewable energy utilized. Two applications considered for the stationary energy storage systems are the end-consumer arbitrage and frequency regulation, while the mobile application envisions a scenario of a grid-independent battery-powered electric vehicle charging station network. The charging stations receive supplies from the energy storage system that absorbs renewable energy, contributing to a sustained DC demand that helps with revenues. Representative results are presented for two operation modes and different sets of weights assigned to the objectives. Substantial improvement in the profitability of combined applications over single stationary applications is shown. Pareto frontier of a reduced dimensional problem is obtained to show the trade-off between design objectives. This work could pave the road for future implementations of the new form of energy storage systems.

We explore the effects of various receiver coil dimensions and configurations on power transfer efficiency and cost of operation, using advanced simulation tools. We demonstrate that the spatial distribution of the magnetic field leads to a non-monotonic dependence of the coupling coefficient on coil size. Thus, an optimal coil size, where the coupling coefficient peaks, should be regarded a crucial design parameter which affects the entire system performances. The incorporation of our findings into a multi-objective optimization algorithm is also discussed.

Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.

Type-IV wind turbines can experience torsional vibrations in the drivetrain structure. This can lead to additional stress on turbine components and a quality reduction of the power delivered to the grid. The vibrations are mostly induced by fast variations of the electromagnetic torque, which depends on the control of a back-to-back converter. A number of studies have already presented methods to mitigate the drivetrain vibrations. However, the research was dedicated to cases when the converter, interfacing a wind turbine to the grid, operates based on a grid-following control. A wind turbine can be also interfaced to a grid-forming converter. In this case, a back-to-back converter control creates a strong link between the electromagnetic torque and grid dynamics, so the abovementioned problem remains relevant. Therefore, this paper presents a solution to damp torsional vibrations in the direct drive of a Type-IV wind turbine, interfaced to the electrical power grid with a voltage source converter based on a grid-forming control. The damping of the drivetrain vibrations relies on the input shaping method implemented using a zero-vibration filter. Simulation results prove the effectiveness of the method to damp drivetrain vibrations during grid frequency variations. In addition to that, damping impact on system behavior with respect to other parameters is analyzed and its mitigation is discussed.

The paper presents MEMS pressure sensor chip utilizing novel electrical circuit with bipolar-junction transistor-based (BJT) differential amplifier with negative feedback loop (PDA-NFL). Pressure sensor chips with two circuits have been manufactured and tested: the first chip uses circuit with vertical n-p-n (V-NPN) BJTs and the second – circuit with horizontal p-n-p (L-PNP) BJTs. The demonstrated approach allows for increase of pressure sensitivity while keeping the same chip size. It also can be used for chip size reduction and increase of pressure overload capability while maintaining the same pressure sensitivity. Significant reduction of both noise and temperature instability of output signal has been demonstrated using transistor amplifier with negative feedback loop.

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, longterm tracking due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. In this paper we propose a solution to the moving-target handoff problem in the absence of GPS. The proposed solution uses a nonlinear complimentary filter for self-pose estimation using only an IMU, a particle filter for relative pose estimation between UAS using a relative range measurement, visual target tracking using a gimballed camera when the target is close to the handoff UAS, and track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present extensive simulation results that demonstrates the effectiveness of our approach and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.