We report a 92 channel RF channelizer based on a 48.9 GHz integrated micro-comb that operates via soliton crystals, together with a passive high-Q ring resonator that acts as a periodic filter with an optical 3dB bandwidth of 121.4 MHz. We obtain an instant RF bandwidth of 8.08 GHz and 17.55 GHz achieved through temperature tuning. These results represent a major advance to achieving fully integrated photonic RF spectrum channelizers with reduced low complexity, size, and high performance for digital-compatible signal detection and broadband analog signal processing.

We propose a CNN based visual servoing scheme for precise positioning of an eye-to-hand manipulator in which the control input of a robot is calculated directly from images by a neural network. In this paper, we propose Difference of Encoded Features driven Interaction matrix Network (DEFINet), a new convolutional neural network (CNN), for eye-to-hand visual servoing. DEFINet estimates a relative pose between desired and current end-effector from desired and current images captured by an eye-to-hand camera. DEFINet includes two branches of the same CNN that share weights and encode target and current images, which is inspired by the architecture of Siamese network. Regression of the relative pose from the difference of the encoded target and current image features leads to a high positioning accuracy of visual servoing using DEFINet. The training dataset is generated from sample data collected by operating a manipulator randomly in task space. The performance of the proposed visual servoing is evaluated through numerical simulation and experiments using a six-DOF industrial manipulator in a real environment. Both simulation and experimental results show the effectiveness of the proposed method. *The complete version of this preprint paper is published in IEEE Access. Please refer to “https://ieeexplore.ieee.org/abstract/document/9464907”.

Most of the driving maneuver classification methods follow supervised learning techniques and utilize ground truth in order to train classifiers. However, collecting ground truth is the most troublesome, expensive, and significant task of classification and effects a classifier’s performance. The work proposes an empirical framework for automatic labeling of timeseries data that can be further used in training phrases during semi-supervised learning. The proposed algorithm generates class labels and find that generated label of 4895 data matched with 11077 manual labeled data. The work analyzes the challenges involved in the driving time series data labeling. So, reasons behind mismatch of data label can also be explained.

The stability of grid-connected voltage source converters (VSCs) is commonly studied by the small-signal model in the synchronous reference (dq) frame. With phase-locked loop dynamic from VSCs, it is inevitable to convert the small-signal model to a common dq frame. When linearizing the frame transformation, the trigonometric functions of the angle are approximated by first-order terms. In this letter, the limitation of such linearization on the dq-transformation is discussed. The accuracy of the model, in presence of phase jumps and frequency variations, are examined. It is found that even under small phase jumps, mismatches are found between the small-signal model (SSM) and a nonlinear model (NM). It is further revealed that during the dynamics of frequency variation, significant mismatches can occur between the SSM and the NM.

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.

A lot of hard work and years of research are still needed for developing successful Blockchain (BC) applications. Although it is not yet standardized, BC technology was proven as to be an enhancement factor for security, decentralization, and reliability, leading to be successfully implemented in cryptocurrency industries. Fog computing (FC) is one of the recently emerged paradigms that needs to be improved to serve Internet of Things (IoT) environments of the future. As hundreds of projects, ideas, and systems were proposed, one can find a great R\&D potential for integrating BC and FC technologies. Examples of organizations contributing to the R\&D of these two technologies, and their integration, include Linux, IBM, Google, Microsoft, and others. To validate an integrated Fog-Blockchain protocol or method implementation, before the deployment phase, a suitable and accurate simulation environment is needed. Such validation should save a great deal of costs and efforts on researchers and companies adopting this integration. Current available simulation environments facilitate Fog simulation, or BC simulation, but not both. In this paper, we introduce a Fog-Blockchain simulator, namely FoBSim, with the main goal is to ease the experimentation and validation of integrated Fog-Blockchain approaches. According to our proposed workflow of simulation, we implement different Consensus Algorithms (CA), different deployment options of the BC in the FC architecture, and different functionalities of the BC in the simulation. Furthermore, technical details and algorithms on the simulated integration are provided. We validate FoBSim by describing the technologies used within FoBSim, highlighting FoBSim novelty compared to the state-of-the-art, discussing the event validity in FoBSim, and providing a clear walk-through validation. Finally, we simulate two case studies, then present and analyze the obtained results.

This paper presents a new L-shape reflector to enhance the cross-polarization radar cross section (RCS). Cross-polarization component can increase the jamming effect against the polarization radars. Such L-shape reflectors can also be used as radar tags in cars, books and so on for radar target detection. By bending the structure of traditional dipole reflector to L shape, the direction of surface current is changed, which produces the cross-polarization component.

The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently, unsupervised optical flow estimation has become a research hotspot. However, unsupervised approaches are often easy to be unreliable on partially occluded or texture-less regions. To deal with this problem, we propose CoT-AMFlow in this paper, an unsupervised optical flow estimation approach. In terms of the network architecture, we develop an adaptive modulation network that employs two novel module types, flow modulation modules (FMMs) and cost volume modulation modules (CMMs), to remove outliers in challenging regions. As for the training paradigm, we adopt a co-teaching strategy, where two networks simultaneously teach each other about challenging regions to further improve accuracy. Experimental results on the MPI Sintel, KITTI Flow and Middlebury Flow benchmarks demonstrate that our CoT-AMFlow outperforms all other state-of-the-art unsupervised approaches, while still running in real time. Our project page is available at https://sites.google.com/view/cot-amflow.

We propose novel multi-model fusion-based step detection and step length estimation approaches that use the Kalman filter. The proposed step detection approach combines results from three conventional step detection algorithms, namely, findpeaks, localmax, and advanced zero crossing to obtain a single and more accurate step count estimate. The proposed step length estimation approach combines results from two popular step length estimation algorithms namely Weinberg’s and Kim’s methods. In our experiment, we consider five different smartphone placements, that is, when the smartphone is handheld, handheld with an arm swing, placed in the backpack, placed in a trousers’ back pocket and placed in a handbag. The system relies on inertia measurement unit sensors embedded in smartphones to generate accelerometer, gyroscope and magnetometer values from the human subject’s motion. Results from our experiments show that our proposed fusion based step detection and step length estimation approaches outperform the convectional step detection and step length estimation algorithms, respectively. Our Kalman fusion approach achieves a better step detection, step length estimation for all the five smart phone placements hence providing a better positioning accuracy. The performance of the proposed multi-model fusion-based positioning system was measured using the root mean square error (RMSE) of the displacement errors and step count errors exhibited by all the the step length and step count algorithms. The results show that the proposed Kalman fusion approach for step count estimation and step length estimation provides the least RMSE for all the smartphone placements. The proposed approach provides an average RMSE of 0.26 m in terms of the final position estimate for all the smartphone placements.

We propose a new general framework attempting a unification of circuit and quantum theories. The theory is based on our discovery of a common Lie-group-theoretic structure shared by continuous circuits and quantum systems. Applications are proposed by applying topology to selected problems in microwave network theory.

In this work, the Piecewise Variable-strain (PVS) approach is applied to the case of Concentric Tube Robots (CTRs) and extended to include the tubes’ sliding motion. In particular, the currently accepted continuous Cosserat rod model is discretized onto a finite set of strain basis functions. At the same time, the insertion and rotation motions of the tubes are included as generalized coordinates instead of boundary kinematic conditions. Doing so, we obtain a minimum set of closed-form algebraic equations that can be solved not only for the shape variables but also for the actuation forces and torques for the first time. This new approach opens the way to torque-controlled CTRs, which is poised to enhance elastic stability and improve interaction forces’ control at the end-effector.

We use built-in Tensorflow and Pytorch dataset for our experiments.

The smiles stimuli were collected from the UvA-NEMO dataset. A total of 60 videos were randomly selected for this experiment. We created four categories out of these 60 videos, namely PV (paired videos), PI (paired images), SV (single video), and SI (single image). When the same person was viewed by observers in both real and posed smile videos, we report this as “paired videos”, otherwise we use the term “single video”.

Effective and Secured Supplier Collaboration is always a critical element for any organizations revenue and its value. In these changing times, businesses are operating remotely, it is essential to lookout the effective collaborative solution for sharing and securing the data with suppliers. In this article, I am elaborating few business solution principles for effective supplier collaborations in the context of manufacturing sector. However, these principles can be applicable common across any industry with minor changes.

Convolutional networks are at the center of best in class computer vision applications for a wide assortment of undertakings. Since 2014, profound amount of work began to make better convolutional architectures, yielding generous additions in different benchmarks. Albeit expanded model size and computational cost will, in general, mean prompt quality increases for most undertakings but, the architectures now need to have some additional information to increase the performance. We show empirical evidence that with the amalgamation of content-based image similarity and deep learning models, we can provide the flow of information which can be used in making clustered learning possible. We show how parallel training of sub-dataset clusters not only reduces the cost of computation but also increases the benchmark accuracies by 5-11 percent.

The 3rd Generation Partnership Project (3GPP) standard for 5G telecommunications specifies privacy protection schemes to cryptographically encrypt and conceal permanent identifiers of subscribers to prevent them from being exposed and tracked by over-the-air eavesdroppers. However, conventional privacy-preserving protocols and architectures alone are insufficient to protect subscriber privacy as they are vulnerable to new types of attacks due to the utilization of the emerging technologies such artificial intelligence (AI). A conventional brute force attack to unmask concealed 5G identity using a CPU would require ~877 million years. This paper presents an apparatus using machine learning (ML) and a graphics processing unit (GPU) that is able to unmask a concealed 5G identity in ~12 minutes with an untrained neural-network. The 5G concealed identities in the training are effectively identified without requiring decryption, hence severely diminishing the level of privacy-preservation. Finally, several ML defence countermeasures are proposed to re-establish privacy protection in 5G identity.

Concurrent respiration monitoring of multiple subjects remains a challenge in microwave Doppler radar-based non-contact physiological sensing technology. Prior research using Independent component analysis with the JADE algorithm has been limited to the separation of respiratory signatures for normal breathing patterns. This paper investigates the feasibility of using the ICA-JADE algorithm with a 24-GHz phase comparison monopulse radar transceiver for separating respiratory signatures from combined mixtures of varied breathing patterns. Normal, fast, and slow breathing pattern variations likely to occur due to physiological activity, and emotional stress were used as a basis for assessing separation robustness. Experimental results showed efficacy for recognition of three different breathing patterns, and isolation of respiratory signatures with an accuracy of 100% for normal breathing, 92% for slow breathing, and 83.78% for fast breathing using ICA-JADE. Breathing pattern variations were observed to affect the signal-to-noise ratio through multiple mechanisms, decreasing with an increase in the number of breathing cycles and associated motion artifacts. Additionally, for removing motion artifacts of fast breathing pattern empirical mode decomposition (EMD) is employed, and for slow breathing pattern, increasing the breathing cycles helps to achieve an accuracy of 89.2% and 94.5% respectively.

This letter studies the performance of a single gateway LoRa system in the presence of different interference considering the imperfect orthogonality effect. It utilizes concepts of stochastic geometry to present a low-complexity approximate closed-form model for computing the success and coverage probabilities under these challenging conditions. Monte Carlo simulation results have shown that LoRa is not as theoretically described as a technology that can cover few to ten kilometers. It was found that in the presence of the combination of signal-to-noise ratio (SNR) and imperfect orthogonality between spreading factors (SF), the performance degrades dramatically beyond a couple of kilometers. However, better performance is observed when perfect orthogonality is considered and SNR is not included. Furthermore, the performance is annulus dependent and slightly improves at the border of the deployment cell annuli. Finally, the coverage probability declines exponentially as the average number of end devices grows.