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Integrated Access and Backhaul in Cell-free Massive MIMO Systems
Get PDFOne of the major challenges with cell-free (CF) massive multiple-input multiple-output (MIMO) networks is providing backhaul links for a large number of distributed access points (APs). In general, providing fiber optics backhaul for these APs is not cost-effective and also reduces network scalability. Wireless backhauling is a promising solution that can be integrated with wireless access links to increase spectrum efficiency. In this paper, the application of integrated access and backhaul (IAB) technique in millimeter-wave (mmWave) CF massive MIMO systems is investigated. The access and backhaul links share a frequency spectrum in the mmWave bands, and in both, hybrid beamforming techniques are adopted for signal transmission. The bandwidth allocation (division) parameter between the two link types as well as the beamforming matrices are optimized to maximize the end-to-end data rate. This leads to a non-convex optimization problem for which an efficient solution method is proposed. The simulation results show the effectiveness of the IAB technique and our proposed scheme in CF massive MIMO systems. These simulations also compare the proposed hybrid beamforming method with a fully digital solution in terms of the number of radio frequency (RF) chains and the volume of backhaul traffic. Finally, the effect of increasing the number of APs on the users’ data rates in terms of wireless access and backhaul links constraints is also examined
Multivariate Sequence Clustering for Driving Preference Classification Based on Wide-Range Trajectory Data
No full textAccurate driving preferences classification is a crucial component for autonomous connected vehicles in making more safety and more efficient driving decisions. Most existing studies identify drivers’ driving preferences based on the historical data of the individual vehicle, and the selected variables are limited to the mechanical motion of the vehicle, which seldomly takes the influence of road traffic conditions and surrounding vehicles into account. This study proposes a driving preferences classification method by multivariate sequence clustering algorithm based on wide-range trajectory data. Based on the specific range of road sections, the selected variables for each trajectory are converted from the time domain to the space domain separately, to capture the dynamic changes of the features along the road area. Multivariate time series clustering combining a weighted Dynamic Time Warping (WDTW) and the k-medoids algorithm is used to classify driving preferences into different levels, and a popular internal evaluation metric is employed to determine the optimal cluster result. This study also investigates the heterogeneity of driving behaviors at different driving preference levels. The results show that the proposed method could better recognize drivers’ internal driving preferences
Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs?
Get PDFAntimicrobial resistance (AMR) is a global threat to human and animal health and well-being. To understand AMR dynamics, it is important to monitor resistant bacteria and resistance genes in all relevant settings. However, while monitoring of AMR has been implemented in clinical and veterinary settings, comprehensive monitoring of AMR in the environment is almost completely lacking. Yet, the environmental dimension of AMR is critical for understanding the dissemination routes and selection of resistant microorganisms, as well as the human health risks related to environmental AMR. Here, we outline important knowledge gaps that impede implementation of environmental AMR monitoring. These include lack of knowledge of the ‘normal’ background levels of environmental AMR, definition of high-risk environments for transmission, and a poor understanding of the concentrations of antibiotics and other chemical agents that promote resistance selection. Furthermore, there is a lack of methods to detect resistance genes that are not already circulating among pathogens. We conclude that these knowledge gaps need to be addressed before routine monitoring for AMR in the environment can be implemented on a large scale. Yet, AMR monitoring data bridging different sectors is needed in order to fill these knowledge gaps, which means that some level of national, regional and global AMR surveillance in the environment must happen even without all scientific questions answered. With the possibilities opened up by rapidly advancing technologies, it is time to fill these knowledge gaps. Doing so will allow for specific actions against environmental AMR development and spread to pathogens and thereby safeguard the health and wellbeing of humans and animals
Development and Validation of a Wearable Device to Provide Rich Somatosensory Stimulation for Rehabilitation After Sensorimotor Impairment
Get PDFTraining sensory discrimination of the skin has the potential to reduce chronic pain due to sensorimotor impairments and increase sensorimotor function. Currently, there is no such device that can systematically provide rich skin stimulation suitable for a training protocol for individuals with amputation or major sensory impairment. This study describes the development and validation of a non-invasive wearable device meant to repeatedly and safely deliver somatosensory stimulations. The development was guided by a structured design control process to ensure the verifiability and validity of the design outcomes. Two sub-systems were designed: 1) a tactile display for touch and vibration sensations, and 2) a set of bands for sliding, pressure, and strain sensations. The device was designed with a versatile structure that allows for its application on different body parts. We designed a device-paired interactive computer program to enable structured sensory training sessions. Validation was performed with 11 individuals with intact limbs whose upper arm tactile sensitivity was measured over 5 training sessions. Tactile discrimination and perception threshold were measured using the standard 2-point discrimination and Semmes-Weinstein monofilament tests, respectively. The results of the monofilament test showed a significant improvement (p = 0.011), but the improvement was not significant for the 2-point discrimination test(p = 0.141). These promising results confirm the potential of the proposed training to increase the sensory acuity in the upper arms of individuals with intact limbs. Further studies will be conducted to determine how to transfer the findings of this work to improve the pain and/or functional rehabilitation in individuals with sensorimotor impairments
Work-learn balance – a new concept that could help bridge the divide between education and working life?
Get PDFThere is a deep divide between the worlds of education and working life. Differences comprise culture, values, processes, tools and more. This divide hinders many people from reaching their fullest potential, instead often demotivating them. Collaboration across the divide can be facilitated by semantic concepts that both sides can relate to. This article attempts to propose a new unifying concept – “work-learn balance” – that the two worlds can appreciate and use when working together. It is defined as when people on a weekly basis combine new value creation for others (“work”) and own personal development (“learn”). It was inductively articulated through action research. Extant research has shown that many people with a good work-learn balance get more motivated and feel a deeper sense of meaning in life, making them work harder and achieve more. Examples are provided from entrepreneurship, innovation, vocational education and entrepreneurial education. Work-learn balance could be used as a visionary organising principle informing leadership strategy. This could facilitate collaboration and unleash the human potential of more students and employees. Work-learn balance as a concept is novel and unexplored, but not previously unheard of. This could be the first attempt to define, describe, substantiate and sense-make it
Prescient Collision-Free Navigation of Mobile Robots with Iterative Multimodal Motion Prediction of Dynamic Obstacles
No full textTo explore safe interactions between a mobile robot and dynamic obstacles, this paper presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates multimodal motion predictions of dynamic obstacles with predictive control for obstacle avoidance. Multimodal Motion Prediction (MMP) is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles
Triplet States of Cyanostar and Its Anion Complexes
No full textThe design of advanced optical materials based on triplet states requires knowledge of the triplet energies of the molecular building blocks. To this end, we report the triplet energy of cyanostar (CS) macrocycles, which are the key structure-directing units of small-molecule ionic isolation lattices (SMILES) that have emerged as programmable optical materials. Cyanostar is a cyclic pentamer of covalently linked cyanostilbene units that form π-stacked dimers when binding anions as 2:1 complexes. The triplet energies, ET, of the parent cyanostar and its 2:1 complex around PF6- are measured to be 1.96 and 2.02 eV, respectively, using phosphorescence quenching studies at room temperature. The similarity of these triplet energies suggests that anion complexation leaves the triplet energy relatively unchanged. Similar energies (2.0 and 1.98 eV, respectively) were also obtained from phosphorescence spectra of the iodinated form, I-CS, and of complexes formed with PF6- and IO4- recorded at 85 K in an organic glass. Thus, measures of the triplet energies likely reflect geometries close to those of the ground state either directly by triplet energy transfer to the ground state or indirectly by using frozen media to inhibit relaxation. Density functional theory (DFT) and time-dependent DFT were undertaken on a cyanostar analogue, CSH, to examine the triplet state. The triplet excitation localizes on a single olefin whether in the single cyanostar or its π-stacked dimer. Restriction of the geometrical changes by forming either a dimer of macrocycles, (CSH)2, or a complex, (CSH)2\ub7PF6-, reduces the relaxation resulting in an adiabatic energy of the triplet state of 2.0 eV. This structural constraint is also expected for solid-state SMILES materials. The obtained T1 energy of 2.0 eV is a key guide line for the design of SMILES materials for the manipulation of triplet excitons by triplet state engineering in the future
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Get PDFIn this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it as \textit{Model Transfer Reinforcement Learning (MTRL)} problem. First, we formulate MTRL for discrete MDPs and Linear Quadratic Regulators (LQRs) with continuous state actions. Then, we propose a generic two-stage algorithm, MLEMTRL, to address the MTRL problem in discrete and continuous settings. In the first stage, MLEMTRL uses a \textit{constrained Maximum Likelihood Estimation (MLE)}-based approach to estimate the target MDP model using a set of known MDP models. In the second stage, using the estimated target MDP model, MLEMTRL deploys a model-based planning algorithm appropriate for the MDP class. Theoretically, we prove worst-case regret bounds for MLEMTRL both in realisable and non-realisable settings. We empirically demonstrate that MLEMTRL allows faster learning in new MDPs than learning from scratch and achieves near-optimal performance depending on the similarity of the available MDPs and the target MDP
Hydride-Enhanced Plating and Stripping of Aluminum from Triflate-based Organic Electrolytes**
No full textIn the quest for developing rechargeable aluminum (Al) batteries, reversible Al plating in the absence of active-halide components is considered an immense challenge. For this reason, the choice of electrolyte has been primarily limited to the highly corrosive chloroaluminate systems based on aluminum trichloride (AlCl3). In this work, we demonstrate reversible room-temperature Al plating from an active-halide-free (AHF) organic electrolyte based on aluminum trifluoromethanesulfonate (Al(OTF)3) and lithium aluminum hydride (LiAlH4) in tetrahydrofuran (THF), as well as its AlCl3 counterpart. From insights obtained by Density Functional Theory (DFT) and Fourier-Transform Infrared (FTIR) spectroscopy, ionic speciation in the electrolyte is explored, and mechanisms for the underlying electrochemical processes in the trifluoromethanesulfonate (OTF−)-based electrolytes are proposed. Al plating and stripping were confirmed by optical microscopy, scanning electron microscopy (SEM) and X-ray diffraction (XRD). Characterizing the Al deposits from either the OTF−- or the chloride (Cl−)-based electrolytes via depth-profile X-ray Photoelectron Spectroscopy (XPS) analyses, we find that these deposits consist of metallic Al, aluminum oxide (Al2O3), and either aluminum trifluoride (AlF3) or aluminum chloride (AlxCly) contaminants arising from a reaction with the electrolyte components which occurs during the plating process