Multi-feature SAR ship classification aims to build models that can process, correlate, and fuse information from handcrafted features and deep features. Although handcrafted ones provide rich expert knowledge, current fusion methods do not thoroughly investigate the relatively important of handcrafted features with deep features, feature contribution imbalance, and the way features learn collaboratively. In this paper, a novel multi-feature collaborative fusion network with deep supervision (MFCFNet) is proposed to effectively realize handcrafted feature and deep feature fusion in SAR ship classification task. Specifically, our framework mainly includes two types of feature extraction branches, a knowledge supervision and collaboration module, and a feature fusion and contribution assignment module. The former module improves the feature map quality learned by each branch through auxiliary feature supervision, and introduces synergy loss to facilitate the information interaction between deep features and handcrafted features. The latter utilizes an attention mechanism to adaptively balance the importance among various features, and to assign the corresponding feature contribution to the total loss function based on the generated feature weights. We conduct extensive experimental and ablation studies on two public OpenSARShip-1.0 and FUSAR-Ship datasets, and the results show that MFCFNet is effective and outperforms single deep feature and multi-feature models based on previous Internal FC Layer and Terminal FC Layer fusion, and exhibits better performance than the current state-of-the-art methods.

The performance of an underlay coordinated direct and relay transmission (CDRT) based non-orthogonal multiple access (NOMA) network is limited by the random nature of the transmit powers due to the interference temperature limit (ITL) imposed by the primary network. In this paper, we show that combining the signals at both near and far users can significantly enhance throughput. Considering the practical scenario of imperfect successive interference cancellation, the performance of near and far user nodes is analyzed in terms of throughput and outage probability. The traditional case of static ITL is considered first. Then, analysis is presented for the case when the ITL depends on the primary channel state information, and it is shown that the gain in performance can be very large over static ITL. Closed-form expressions for NOMA power pportioning are presented for a special case. Further, it is shown that how optimum parameters can be chosen to maximize the performance. Simulations validate the derived analytical expressions.

This paper proposes a framework using artificial intelligence for recognizing human mental stress based on physiological data. It comprises three stages; at first, emotion detection has been carried out from the facial image using a deep learning-based framework employing convolutional neural networks (CNN). Secondly, electrocardiogram (ECG) signals have been acquired with the help of the developed sensor-based system integrating Arduino UNO with AD8232 sensor. Finally, preprocessing has been performed and extracted features from the ECG signals are fed to support vector machines (SVM) for identification of arrhythmia and hence diagnosis of mental stress. All three aspects are connected together via the overall output calculation. A score is added in accordance to the output for each of these three stages. The range of the final score (i.e., the overall output) decides the level of mental stress suffered by the person. The proposed method has been trained on FER-2013 for emotion detection and on MIT-BIH for arrhythmia detection. It is found that the performance outperforms other machine learning and deep learning models without much compromise in accuracy. The proposed method can perform classification based on the patient’s current condition and gives an insight of the degree and time duration of stress of the patient.

Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.

Elsevier -Journal article(Pre-print) -This research is a revolutionary Machine learning approach on modeling the scientific process enabling the most valuable scientific participation. SA-index, an index from this is a new horizon toward indexing the credibility of scientific journals via ML model of the Knowledge system. This is valuable for research databases including PubMed, web of science(please rich me).

By applying rotor angle sensorless control methods the costs of the electrical drive can be decreased while its reliability is increased. Traditionally, the design of both the rotor angle estimator and the drive controller require a detailed motor model and manual tuning leading to significant effort by human experts. In this work, a rotor anisotropy-based sensorless control scheme for interior permanent magnet synchronous motors (IPMSM) is proposed that enables sensorless control with minimum tuning effort and a priori motor knowledge. Due to these characteristics the scheme is particularly suitable for self-commissioning or low-cost drive applications. The scheme contains a finite control set model predictive current controller (FCS-MPCC) with an additional inequality constraint that enables the identification of the motor model in the stator-fixed coordinate system by using the last three measurement samples. Utilizing an automatic system identification procedure, the motor model is determined online in a data-driven fashion. The identified motor model serves both as prediction model of the FCS-MPCC and as  baseline for the rotor angle estimation via an eigenvalue decomposition approach. Challenging experimental investigations at standstill up to the medium speed range prove the applicability of the proposed approach. Here,  highly dynamic speed and current transients can be handled by the proposed method.

Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that obtains location estimation with the inertial measurement unit (IMU). However, one of its most significant drawbacks is the accumulation of its measurement error. This paper proposes a visible light positioning (VLP)-integrated PDR system, which could achieve real-time and accurate indoor positioning using IMU and the camera sensor of our smartphone. A multi-frame fusion method is proposed in the encoding and decoding process of the system, reaching 98.5% decoding accuracy with a 20-bit-long ID at the height of 2.1 m, which allows the variation in the shutter speeds of cameras and heights of the LED. Meanwhile, absolute locations and step length could be calibrated with the help of a single light-emitting diode (LED), promising average accuracy within 0.5 meters in a 108-meter walk.

A differential derivation method is demonstrated to high-accurately calculate the refractive index curves by solving the mode coupling problems in theoretical calculations. Because of the existence of mode coupling, the finite element method cannot lock the calculator in the interested mode, especially at the crossing part of two refractive index curves. Either imaginary crossing or real crossing will result in an inaccurate calculation of the refractive index curve. The calculation accuracy in the crossing parts is improved by using the differential derivation method, and the effectiveness is verified by calculating the refractive index curves for the HE11, HE12, TE01, and EH11 modes of a custom-designed fiber.

The 2x-thru calibration is frequently used in the characterization of printed circuit board (PCB) transmission lines. Since the reference impedance of the calibrated S parameters are exactly given by the characteristic impedance, ZLINE, of the trace at the center of THRU, accurate estimation of this impedance becomes crucial to the accuracy of the renormalized S parameters. In this paper, we compare three different methods of estimating ZLINE: the mid-point TDR method, the initial-value TDR method, and the analytic model fitting method. Through a simulation test case, it is demonstrated that (i) the initial-value method gives accurate high-frequency responses, but inaccurate low-frequency and time-domain responses, (ii) the mid-point TDR gives inaccurate results in both time and frequency domains, and (iii) the analytic model method produces the most accurate results.

The last few years have seen a steep increase in blockchain interoperability research. Most solutions connect public blockchains; hence, the main cross-chain use case is token transfer. By-design platform transparency, tamper-resistance, and auditability make blockchains an infrastructure candidate for Central Bank Digital Currencies (CBDCs), but bridging CBDCs is an important missing piece in general. In this paper, we leverage an asset transfer protocol, ODAP/SATP, to define an extendable and dependable blockchain interoperability middleware that can bridge CBDC from Hyperledger Fabric to EVM-based permissioned blockchains. The key interoperation enabler in the solution is a shared asset definition enforced by both sides of the bridge, accompanied by a mapping between Fabric Identities and Ethereum addresses for Identity management. We implement our design for the CBDC use case utilizing Hyperledger Cactus. Through a preliminary performance evaluation, we show that the underlying ledgers heavily influence the latency of the solution, not the bridging components.

In a debris mitigation mission, a crucial phase of the proximity operation for a space manipulator is chasing a capture point on a noncooperative target satellite. Knowing the uncertain position and velocity of the target, a learning-based online trajectory planner offers a robust solution to the chasing problem. This paper uses the concept of transfer learning to develop an online trajectory generator for the task of capturing a moving target with an uncertain space manipulator. We divide this complex task into multiple sub-tasks and order them based on their difficulty level. We employ the Deep Deterministic Policy Gradient (DDPG) algorithm to learn each sub-task individually. The DDPG is a deep reinforcement learning approach that provides the ability to work with continuous states and actions by approximating the action-value function and the policy with neural networks. We propose a novel method to transfer the knowledge gained in an easier sub-task to a more difficult one in the form of expert policy and transition memories. State and action representation has a crucial impact on learning performance, which is comprehensively studied in this paper for the task of capturing a moving target. Considering the learning performance, we show the existence of an optimal state representation, which is not necessarily the minimal representation of the system. We compare different action representations of a manipulator, i.e., joint space and workspace velocities, and demonstrate the superiority of the workspace actions. Finally, the developed transfer learning approach is implemented on a planar space manipulator with an onboard 2-link arm to generate trajectories that can capture a target randomly moving with the maximum speed of the manipulator’s end effector. To show the efficacy of the approach, its results are compared with the case where the agent learns the task from scratch.

Cellular Vehicle-to-Everything (C-V2X) has become one of the highest anticipated technologies for vehicular safety network. In LTE C-V2X Basic Safety Messages (BSMs) are transmitted on radio resources that are allocated with a periodic resource reusability. This allocation is based on a semi persistent sensing-based scheduling scheme (SPS) algorithm. But, due to this reuse of periodic resources, the possibility of loss of consecutive packets between the same vehicle pair is significant.  This study dis-cusses different approaches proposed to solve this consecutive loss problem. Based on this investigation, this article suggests an efficient One-Shot based solution with a new control parameter, that performs superior to the state-of-the-art solution that is standardized in SAE J3161/1 which this article analyzes and shows to have limitation in case of high-density scenario.

Hardware Trojan (HT) is the most critical threat due to outsourcing of Integrated circuit designing phases. Therefore, a new technique is proposed that utilizes structural and SCOAP features to detect HT from the gate-level netlist using Light Gradient Boosting (Light GBM). Further, a model agnostic Shapley additive explanations (SHAP) is employed to identify each feature global and local impact on model prediction. Moreover, a quartile-based feature selection method is proposed, which uses SHAP to identify the optimal feature set by keeping low retraining rounds. Experimental results show that the proposed technique accurately detects always-on-Trojans and HT nets from Trust-Hub, DeTrust, and DeTest benchmarks.

Thin shell model of a coated conductor with a ferromagnetic support, efficient spectral method for numerical simulations

The work is related to the privacy of smart meter and the benifits of using URLLC in smart grid network.

The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, and artificial intelligence is the next change in the making popularly known as Industry 4.0. Indeed, experts predict that artificial intelligence will be the main force behind the following significant virtual shift in the way we stay, converse, and study, live, communicate, and conduct business (AI). All facets of our social connection are being transformed by this growing technology. One of the newest areas of educational technology is Artificial Intelligence in the field of Education (AIED). This study emphasis the different applications of Artificial Intelligence in education from both an industrial and academic standpoint. It highlights the most recent applications of AIED, with some of its main areas being the reduction of instructors’ burden and students’ contextualized learning novel transformative evaluations, and advancements in sophisticated tutoring systems. It analyses the AIED’s ethical component and the influence of this transition on people, particularly students and instructors as well. Finally, the article touches on AIEd’s potential future research and practices. The goal of this study is to introduce the present day applications of AIEd-based applications to its intended audience.

Automated Machine Learning (AutoML) is a recent technology that provides speed to machine learning iterations and allows individuals with less experience to take advantage of existing tools. Due to several frameworks with different features, deciding the best option to solve each classification problem becomes difficult. It is necessary to consider aspects such as performance metrics and time when choosing the algorithm to reduce the demand for highly technical, specific knowledge in the subject. There are some comparisons of AutoML tools and approaches that perform tests in the area of data preprocessing, model selection, and hyperparameter optimization. However, most of these studies focus on binary and multiclass classification, not covering multilabel classifications and consequently not exploiting the full potential of the tools. In this paper, a comparative study between multiple AutoML tools is performed related to the features, architecture, capabilities, and results achieved on binary, multiclass, and multilabel classification problems from experimentation on various data sets.

In this study, the performance of underwater optical wireless communication links is investigated by taking into account turbulence, absorption and scattering effects. Weak turbulent channel is modeled using log-normal distribution while moderate and strong turbulence channels are modeled using gamma-gamma distribution. Rytov variance of Gaussian beam is derived analytically for oceanic turbulence optical power spectrum. Subsequently, scintillation index is calculated using the computed Rytov variance. Moreover, the closed-form expression of bit-error-rate (BER) for underwater wireless optical communication (UWOC) systems using intensity-modulated/direct detection (IM/DD) implementation and on-off-keying (OOK) modulation scheme is obtained. Results show that the performance of wireless optical communication link between two platforms in underwater medium is degraded significantly due to turbulence, absorption and scattering. In fact, as the turbulence level increases, its effect becomes quantitatively comparable to those of absorption and scattering effects. The variation of both scintillation index and BER performance are presented for various underwater medium and communication system parameters, such as chlorophyll concentration, average temperature, average salinity concentration, temperature and dissipation rates, wavelength, link length and receiver aperture size. Optical network and internet of underwater things (IoUT) applications, which are growing day by day and requiring high data rates, will benefit from the results of this study.