The short-term sensitivity of a several-kilometers long fiber-optic gyroscope is limited mainly by thermal phase noise and relative intensity noise. Increasing the phase modulation frequency decreases the thermal phase noise but not the relative intensity noise since it behaves as white noise. Here, we propose and experimentally demonstrate that the angular random walk can be effectively decreased by suppressing relative intensity noise at the modulation frequency and its third-order harmonic using direct feedback to the drive current of a superluminescent diode. Our simultaneous suppression of thermal phase noise and relative intensity noise yields an angular random walk of 15 μdeg/√h and a bias instability of 33 μdeg/h using a fiber coil with a length of 5km and an effective area of 280m2 for a measurement time of 40 hours.

Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to mitigate fading and improve spectral efficiency. In a cooperative scenario, the performance of the system is improved by selecting the best relay for data transmission among a group of available relays. However, due to the mobility of WVNs, the best relay is often selected in practice based on outdated channel state information (CSI), which in turn affects the overall system performance. Therefore, there is a need for a robust relay selection scheme (RSS) that improves the overall achievable performance of an outdated CSI. Motivated by this and considering the advantageous features of autoregressive moving average (ARMA), in the present work we model a cooperative vehicular communication scenario with relay selection as a Markov decision process (MDP) and propose two deep Q-networks (DQNs), namely DQN-RSS and DQN-RSS-ARMA. In the proposed framework, two deep reinforcement learning (RL)-based RSS are trained based on the intercept probability, aiming to select the optimal vehicular relay from a set of multiple relays. We then compare the proposed RSS with the conventional methods and evaluate the performance of the network from the security point of view. Simulation results show that DQN-RSS and DQN-RSS-ARMA perform better than conventional approaches, and they reduce intercept probability by approximately 15\% and 30\%, respectively, compared to the ARMA approach.

Macroscopic superconductivity models have improved significantly over the last decades. Formulations and methods have been developed to faster solve problems involving high-temperature superconductors (HTS). Despite these developments, finite element analysis (FEA) of AC superconducting machines (SCMs) with HTS coils still remains time-consuming. The combined burden from the HTS models and the moving mesh makes the models complex and slow. To deal with this challenge, a new framework for FEA of AC superconducting SCMs with surface-mounted permanent magnet (PM) rotors and HTS armature windings is proposed in this paper. In this approach, the rotating rotor geometry is emulated with a stationary array of small PM segments excited with time-varying boundary sources. The major benefit of this approach is that the models can be realized without moving meshes, which increases the computation speed by more than one order of magnitude. In our dedicated case study of a complete SCM design, the speedup factors are 17.4x and 38.5x for the H-A and T-A formulations, respectively. Over a large parametric space of 18 design permutations, the highest relative error in calculated HTS loss was 4.12 percent. As a result, this work enables the designer to perform much more comprehensive performance studies of SCMs.

In this article, we analyzed the performance of several classes of quantum circuits under various errors. We executed NISQ circuits on both noisy simulator and real hardware backend publicly available from IBM Quantum. On simulation, we created noise models with both individual errors and combination of multiple errors to quantify how each type of error impact performance of quantum circuits. We utilized statistical distances such as total variation distance (TVD), Jensen-Shannon divergence (JSD), and Hellinger distance, to measure the deviation of noisy output from ideal output. Finally, we compare the noisy outputs from the simulator and outputs from the real hardware to showcase the gap between simulation and real device experiment. This gap points to the limitation of present noise models failure to exhibit the behavior of real hardware.

The platform carrying capacity of urban rail transit stations is limited and overcrowding of the platform will lead to serious safety risks for passengers and trains. It is significant to collaborate on the optimization of passenger flow strategy and skip-stopping scheme to alleviate traffic pressure and ensure platform safety. This study proposes and solves the joint optimization problem of coordinated flow control and skip-stopping scheme considering platform carrying capacity. Firstly, platform demand constraints and platform stranded constraints are designed according to the maximum carrying capacity of the platform to control the number of allowable arrivals ensuring platform safety. Secondly, train arrival variable and train stop variable are introduced to generate train skip-stopping index. Finally, considering the characteristics of passengers' continuous arrival and platform carrying capacity, a mixed integer programming model is established to minimize the number of passengers outside the station and the number of passengers stranded on the platform. Based on empirical data, this study takes Beijing Batong line as a case study and uses the established model to generate flow control strategy and skip-stopping schemes for each station during morning rush hours. Experimental results show that compared to the baseline without implementing the two proposed strategies, the proposed collaborative optimization method can effectively reduce the demand for staying at the platform and increase the number of boarding passengers at downstream stations. Thus, the balance between train capacity and passenger flow demand is maintained while ensuring platform safety. Moreover, the proposed method can also avoid overcrowding at downstream stations.

The emerging cybersecurity industry in Canada is not seen as anywhere near the scale of its American cyber-industrial complex counterpart, with limited-scale projects occurring to improve security sensor technology for institutions. However, due to international trade increasingly occurring through cyberspace, it is expected that the cyber industry will grow in Canada through both international and domestic partnerships, with Canadian critical infrastructure partners requiring greater security controls in order to ensure the confidentiality, integrity and availability of their data and operations.

Abstract— This work-in-progress presents a compact toolkit for reproducing the training of T5-based multiinstruments and multi-track transcription models on multiple MIR tasks. We plan to release our code in early 2023 at https://github.com/mimbres/YourMT3.

In this paper, a computationally efficient chance-constrained rollover-free motion planning method is presented. Specifically, the method is developed to plan motions for reconfigurable vehicles with the knowledge of a 3-D terrain model that has limited accuracy. The overall motion planning problem is formulated as a nonlinear optimal control problem (NOCP) that employs a constraint in the form of a bound on the probability of rollover under terrain-induced vehicle orientation uncertainty. To increase the computational efficiency of the NOCP, a geometric interpretation of the chance constraint is derived based on the characteristics of SO(3), the 3-D rotation group. Monte Carlo simulations are provided to demonstrate the usefulness of the geometric interpretation through comparisons with other methods. Experimental data gathered from driving a mobile robot through real forests are also used to validate the proposed model. Finally, path and trajectory generation results obtained with the proposed planning method for a feller-buncher machine traversing through uncertain 3-D terrain are presented to showcase the method’s overall performance and efficiency.

This research paper explores the concept of edge computing and the advancements in this technology. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the devices that generate and collect data in order to reduce latency and improve performance. It has been identified as a powerful tool for providing low-latency access to data and applications and is becoming increasingly popular in the IoT and 5G era. This paper focuses on the various advancements in edge computing technology, such as the emergence of edge cloud platforms and the development of edge analytics and machine learning solutions. It also discusses various applications of edge com- puting, such as autonomous driving, augmented/virtual reality, and the Internet of Things, and how they are being used to improve efficiency and reduce latency. Finally, the paper analyses the various challenges and opportunities associated with edge computing, including security, scalability and privacy concerns.

A document by Siyue Li. Click on the document to view its contents.

This paper aims to suggest method for sustaining vacuum in particle accelerator while in an variety of circumstances.

The standard interpretation of Young’s double slit experiment is that the light behaves as waves before and after passing through the diaphragm of the double slit. Recently, the convex lens has been utilized to study the behavior of photons near the diaphragm. The experiments show that, within certain macroscopic distance, the pattern is the particle pattern, i.e., the non-interference pattern, and that the non-interference pattern gradually evolve to the interference pattern. blob:https://www.techrxiv.org/35fd3146-204c-4a6b-9148-f0f77f9af13d blob:https://www.techrxiv.org/44bec0b0-0f2c-46b6-addb-07e5678b17cd blob:https://www.techrxiv.org/c362971d-4441-4f4c-b275-8ef1202257fc In this article, we show that, in the double slit experiments, the particle pattern (non-interference pattern) and interference pattern emerge on the screen simultaneously. It is the challenge to interpret this phenomenon.

360° images and videos have become an economic and popular way to provide VR experiences using real-world content. However, the manipulation of the stereo panoramic content remains less explored. In this paper, we focus on the 360° image composition problem, and develop a solution that can take an object from a stereo image pair and insert it at a given 3D position in a target stereo panorama, with well-preserved geometry information. Our method uses recovered 3D point clouds to guide the composited image generation. More specifically, we observe that using only a one-off operation to insert objects into equirectangular images will never produce satisfactory depth perception and generate ghost artifacts when users are watching the result from different view directions. Therefore, we propose a novel view-dependent projection method that segments the object in 3D spherical space with the stereo camera pair facing in that direction. A deep depth densification network is proposed to generate depth guidance for the stereo image generation of each view segment according to the desired position and pose of the inserted object. We finally merge the synthesized view segments and blend the objects into the target stereo 360° scene. A user study demonstrates that our method can provide good depth perception and removes ghost artifacts. The view-dependent solution is a potential paradigm for other content manipulation methods for 360° images and videos.

Data transfer infrastructures composed of Data Transfer Nodes (DTN) are critical to supporting distributed computing and storage capabilities for clouds, data repositories, and complexes of supercomputers and instruments. The infrastructure’s throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. We show that the inherent statistical variations and precision of the throughput profiles estimated by ML methods can be exploited for unauthorized use of DTNs’ computing and network capacity. We present a game theoretic formulation that captures the cost-benefit trade-offs between an attacker that attempts to hide under the profile’s statistical variations and a provider that counters by using additional measurements at an added cost. The Nash equilibrium conditions of this game provide qualitative insights and bounds for the success probabilities of the attacker and provider, based on the generalization equation of the ML-estimate. We present experimental results that illustrate scenarios wherein a significant portion of DTN computing capacity is compromised without being detected by an attacker that exploits the ML estimate properties.

This study shows the application of AI image generators with fine-tuning to the Kansei engineering design process.  Kansei engineering is a method for surveying and analyzing users’ latent feelings and preferences to product and service development (Nagamachi, 1991, 2011).. There are several common difficulties in applying Kansei engineering, including a lack of variety in evaluation samples, the need for non-designers to participate in the design process, and the fact that designers are often too busy to fully engage in the design process. In order to address these challenges, we have applied an AI image generator, StableDiffusion  (Rombach et al. 2021),to the Kansei engineering design process. The “Hypernetworks” framework to fine-tune StableDiffusion in order to make better representation of the shape and appearance of milk cartons. Based on previous Kansei engineering study on milk cartons (Ishihara et al., 1996) , several attempts are shown to use AI-generated designs to create innovative and visually appealing milk carton designs, including designs incorporating red and blue colors and abstract shapes, and a design inspired by the abstract paintings of Jackson Pollock. The authors conclude that the use of AI technology can be a valuable tool in boosting innovative design through Kansei engineering, and that further research is needed to explore the potential for interaction between humans and AI in the design process.

This is the final part of the two- part series paper, where the simulation results namely leg’s trajectory, path tracing, obsacles detected, path constructed, movements of three motors and test like two-element chain three- element chain, are presented. I have left r&d, so will not correct update or complete papers. The earlier version of two parts, which was able to get saved from computer crash is uploaded.

Motivated by the large number of wearables offering geolocation, human mobility mining has emerged as an novel research field  within AI. The study of mobility creates increasingly predictable models in which it is easy to find patterns of behaviour. However, this data is not publicly available and access to it is restricted to large telecommunications operators. In this context, this paper aims to solve one of the main problems of human mobility databases, i.e. the scarcity of data for the generation of human mobility models. For this purpose, Generative adversarial network (GANs) have been proposed to generate synthetic time-series mobility data. Moreover, several neural network models are proposed to assess the impact of synthetic data generation on the prediction of human mobility. Our results show that the use of synthetic data improves predictions of human mobility compared to models based on available measured data.

This is a preprint only and has not been peer-reviewed or published anywhere so far. Abstract : Blockchain technology (BCT) is a game changer for many industries due to its distinct advantages like conducting peer-to-peer transactions in trustless environment, enhanced data security, greater transparency, and the inclusion of smart contracts. Its application in the energy industry in Blockchain Enabled Interconnected Smart Microgrids (BSMGs) is on the rise as it can automate local energy markets; execute energy trading; and implement market operations and management. However, they are limited by their scalability and low transactions rate. Also, with the increase in adoption of BSMGs, different types of BCT platforms will emerge, creating heterogeneity in the system. This shortcoming may lead to a monopoly of certain platforms over the rest. These drawbacks can be overcome by establishing interoperability between heterogeneous blockchains. Interoperability can also enable inter-microgrid transactions which will be hindered if there is no inter-chain communication and transaction. Cosmos is a network of blockchains which allows blockchains, applications and services to be interconnected through Inter Blockchain Communication (IBC) protocol. Ignite CLI is an open-source, command line interface which easily creates modular and customizable blockchains which are inherently connected to Cosmos through IBC. In this paper, Ignite CLI is used to create blockchains for BSMGs which exchange data and calls for inter-microgrid transactions via IBC. The procedure to establish inter-chain communication is defined. Interoperability between BSMGs is explored and demonstrated through different examples, for the first time in the energy domain.