Set Of Slots Research Articles

Strategic allocation of landmarks to reduce uncertainty in indoor navigation

Indoor navigation systems often rely on verbal, turn-based route instructions. These can, at times, be ambiguous at complex decision points with multiple paths intersecting under angles that are not well distinguished by the turn grammar used. Landmarks can be included into turn instructions to reduce this ambiguity. Here, we propose an approach to optimize landmark allocation to improve the clarity of route instructions. This study assumes that landmark locations are constrained to a pre-determined set of slots. We select a minimum-size subset of the set of all slots and allocate it with landmarks, such that the navigation ambiguity is resolved. Our methodology leverages computational geometric analysis, graph algorithms, and optimization formulations to strategically incorporate landmarks into indoor route instructions. We propose a method to optimize landmark allocation in indoor navigation guidance systems, improving the clarity of route instructions at complex decision points that are inadequately served by turn-based instructions alone.

The study of cascading effect in the integration of intake with gas turbine engine bay in subsonic cruise vehicle

Abstract Submerged air intake is often acquainted with the merit of reducing the drag and fitting well within the frame of weight constraints imposed on subsonic cruise vehicle. A novel method for designing an Intake was developed by GTRE, which has been successful with the validation against wind tunnel testing. The designed intake has been integrated with engine bay area of subsonic cruise vehicle. In this study, a provision of bleed from the ramp surface of the intake into the bay through splitter is used for cooling the engine frame, mechanical fixtures, oil tank and other electronic instruments mounted around the engine frame. Different sets of cowl ventilation slots provided on the bay wall will effuse this by-pass air from the intake into the atmosphere there by influencing the heat distribution within the bay area. The cascading effect of the intake with the bay area is extensively studied in this paper.

Toward Improving the Generation Quality of Autoregressive Slot VAEs.

Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations ("slots") from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.

Space–time recurrent memory network

Transformers have recently been popular for learning and inference in the spatial–temporal domain. However, their performance relies on storing and applying attention to the feature tensor of each frame in video. Hence, their space and time complexity increase linearly as the length of video grows, which could be very costly for long videos. We propose a novel visual memory network architecture for the learning and inference problem in the spatial–temporal domain. We maintain a fixed set of memory slots in our memory network and propose an algorithm based on Gumbel-Softmax to learn an adaptive strategy to update this memory. Finally, this architecture is benchmarked on the video object segmentation (VOS) and video prediction problems. We demonstrate that our memory architecture achieves state-of-the-art results, outperforming transformer-based methods on VOS and other recent methods on video prediction while maintaining constant memory capacity independent of the sequence length.

Exact and heuristic solution approaches for energy-efficient identical parallel machine scheduling with time-of-use costs

Nowadays, energy-efficient scheduling has assumed a key role in ensuring the sustainability of manufacturing processes. In this context, we focus on the bi-objective problem of scheduling a set of jobs on identical parallel machines to simultaneously minimize the maximum completion time and the total energy consumption over a time horizon partitioned into a set of discrete slots. The energy costs are determined by a time-of-use pricing scheme, which plays a crucial role in regulating energy demand and flattening its peaks. First, we uncover a symmetry-breaking property that characterizes the structure of the solution space of the problem. As a consequence, we provide a novel, compact mixed-integer linear programming formulation at the core of an efficient exact solution algorithm. A thorough experimental campaign shows that the use of the novel mathematical programming formulation enables the solution of larger-scale instances and entails a reduction in the computational times as compared to the formulation already available in the literature. Furthermore, we propose a new heuristic that improves the state-of-the-art in terms of required computational effort and quality of solutions. Such a heuristic outperforms the existing heuristics for the problem and is also capable of speeding up the exact solution algorithm when used for its initialization. Finally, we introduce a novel dynamic programming algorithm that is able to compute the optimal timing of the jobs scheduled on each machine to further improve the performance of the new heuristic.

Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey

The combined increase of energy demand and environmental pollution on a global scale is forcing a rethinking, in sustainable terms, of energy supply policies and production models. To flatten demand peaks in power plants, energy suppliers adopted pricing policies that stimulate a change in the consumption practices of customers. One example of such policies is the Time-of-Use (TOU)-based tariffs, which encourage electricity usage at off-peak hours using low prices while penalizing peak hours with higher prices. To avoid a sharp rise in energy supply costs, the manufacturing industry must carefully reschedule the production processes, by shifting them toward less expensive periods. TOU-based tariffs impose specific constraints on the completion of the jobs involved in the production processes as well as a partitioning of the time horizon of the production into a set of time slots, whose associated cost becomes part of the optimization objective. In this article, we review the flourishing literature on job scheduling under TOU-based energy tariffs. Our purpose is to provide researchers and practitioners with a framework that may guide them toward the most important theoretical results on the topic as well as the most prominent practical applications in sustainable manufacturing.

ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning.

Learning predictive models for unlabeled spatiotemporal data is challenging in part because visual dynamics can be highly entangled, especially in real scenes. In this paper, we refer to the multi-modal output distribution of predictive learning as spatiotemporal modes. We find an experimental phenomenon named spatiotemporal mode collapse (STMC) on most existing video prediction models, that is, features collapse into invalid representation subspaces due to the ambiguous understanding of mixed physical processes. We propose to quantify STMC and explore its solution for the first time in the context of unsupervised predictive learning. To this end, we present ModeRNN, a decoupling-aggregation framework that has a strong inductive bias of discovering the compositional structures of spatiotemporal modes between recurrent states. We first leverage a set of dynamic slots with independent parameters to extract individual building components of spatiotemporal modes. We then perform a weighted fusion of slot features to adaptively aggregate them into a unified hidden representation for recurrent updates. Through a series of experiments, we show high correlation between STMC and the fuzzy prediction results of future video frames. Besides, ModeRNN is shown to better mitigate STMC and achieve the state of the art on five video prediction datasets.

A low-thrust finite state machine based controller for N-satellites formations in distributed synthetic aperture radar applications

Spacecraft formation flying has attracted notable interest in recent years, thanks to the properties of flexibility to different mission needs and enhanced robustness and capabilities compared to monolithic space systems. A key element for the full exploitation of this technology consists in the automation of formation management and control, allowing higher independence from ground stations as well as prompter reactions and higher autonomy in task-assignment and decision making. In this context, this work proposes a distributed architecture to autonomously coordinate and control formations containing any number of members applying low-thrust propulsion and employing finite state machines to make decisions and assign tasks to the elements of the formation based on their current state. The nominal relative trajectories (called slots) are represented within the architecture through a set of geometric parameters characterizing their stability, size, and shape. The architecture can autonomously maintain the spacecraft on a set of nominal slots, as well as reconfigure the formation to a newly assigned geometry and deal with contingency situations, performing collision avoidance manoeuvres and keeping track of the slots of the formation that remain empty to autonomously define a course of action allowing the evading spacecraft to re-join the formation safely. At the same time, a strategy for the identification of the geometrical conditions to be violated to efficiently perform formation maintenance is proposed. The architecture is tested by means of numerical simulations within a GMAT-MATLAB integrated environment considering a quasi-linear safe ellipse formation aimed at distributed synthetic aperture radar applications as reference test case. Simulation results demonstrate that the proposed architecture allows effective autonomous coordination within tight formations, with cm-level ΔV expenses, while being fully scalable with the number of satellites.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

Compact Wide-Beam Circularly Polarized End-Fire Antenna Combining Ground Radiation Mode and Vertically Polarized Mode

A compact wide-beam endfire antenna with ground radiation mode and vertically polarized mode for circular polarization (CP) is presented in this article. The antenna is constructed based on a configuration of coupled-mode patch antenna (CMPA), which consists of two coupling cavities with a shorting wall, and can generate vertically polarized modes. The CP is realized through a pair of orthogonally polarized modes of horizontal and vertical polarizations. Meanwhile, rendering them acquires an equal amplitude and a 90° phase difference. The horizontally polarized radiation mode is realized through inserting a crossed ground slot. Offset probe feed is used to excite all the radiation modes and introduce an additional phase shift for the CP. A set of offset ground slots (OGSs) are then applied to perturb the electromagnetic coupling of the modes and adjust the phase and magnitude of the radiating electric fields (E-fields). Several effects are synthesized, which results in a wide-beam circularly polarized (CP) endfire radiation. Aiming to improve the radiation performances, a cavity-backed planar reflector was applied in the experiment. Simulated and measured results are detailed and analyzed, which demonstrate that the measured 3 dB axial ratio (AR < 3 dB) bandwidth (ARBW) ranges from 3.385 to 3.428 GHz (1.2%), and the peak gain can be up to 3.4 dBi if the planar reflector is added. The measured half-power beamwidth (HPBW) and 3 dB ARBW can be up to 88°/114° and 53°/112° at 3.4 GHz, respectively. The antenna size (excluding the reflector) is maintained at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.49\lambda _{0}\times 0.25\lambda _{0}\times 0.017\lambda _{0}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength in free space, performing a compact configuration.

Multi-Agent Reinforcement Learning for a Random Access Game

This work investigates a random access (RA) game for a time-slotted RA system, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> players choose a set of slots of a frame and each frame consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> multiple time slots. We obtain the pure strategy Nash equilibria (PNEs) of this RA game, where slots are fully utilized as in the centralized scheduling. As an algorithm to realize a PNE (Pure strategy Nash Equilibrium), we propose an Exponential-weight algorithm for Exploration and Exploitation (EXP3)-based multi-agent (MA) learning algorithm, which has the computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N N_{\max }^{2} T)$</tex-math></inline-formula> . EXP3 is a bandit algorithm designed to find an optimal strategy in a multi-armed bandit (MAB) problem that users do not know the expected payoff of each strategy. Our simulation results show that the proposed algorithm can achieve PNEs. Moreover, it can adapt to time-varying environments, where the number of players varies over time.

ASMAC: An Adaptive Slot Access MAC Protocol in Distributed VANET

The media access control (MAC) protocol is responsible for channel resource sharing and conflict management in the communication process in VANET, which is of great significance for ensuring efficient and reliable communication. The existing MAC protocol with fixed time slot allocation cannot solve the problem of time slot resource exhaustion or waste when the traffic density is unbalanced. In this paper, we propose an adaptive time slot access MAC protocol in distributed VANET (ASMAC). ASMAC adapts the access time slot according to the driving direction of the vehicle and the traffic density ratio to improve the time slot access efficiency. In the ASMAC protocol, the frame is divided into several time slots based on TDMA, and the time slots in a frame are divided into two time slot sets according to the driving direction. The vehicle selects a time slot access corresponding to the time slot set. When the vehicle finds that there is no accessible time slot in the time slot set, it accesses the available time slot in the reverse time slot set to improve the efficiency of time slot access. In this paper, the analysis and simulation results of highway and four-way intersection scenarios are given and the validity of the ASMAC protocol is verified by comparing with the VeMAC and SAMD protocols.

A substrate integrated waveguide based dual‐band leaky‐wave antenna fed by TE 10 and TE 20 modes

A dual-band leaky wave antenna (LWA) based on substrate integrated waveguide (SIW) is presented in this paper. The radiation elements are composed of a set of periodical transverse slots and a long longitudinal slot that can radiate scanning beams at two different bands under TE10-mode and TE20-mode excitation, respectively. The scanning beams in the two bands both cover the forward region, and polarizations are perpendicular. Under TE10 excitation, the proposed antenna radiates scanning beams in the low band (8.2 GHz ~ 9.1 GHz) with gain varying from 10.6 to 15.9 dBi. Under TE20 excitation, the beams of the proposed antenna scan in the high band of 18.8 GHz ~ 22 GHz with gain varying in 11.7 dBi ~ 16 dBi. The measured results of the fabricated prototype confirm the design theory and simulated results.

Design of a Novel Split-Bowtie Slotted Multi-Resonant Antenna

This article demonstrates a novel design of a split-bowtie slotted multi-resonant antenna. The design process was inspired from previous research of integrating a slot of a similar profile to the outer boundary of a rectangular patch antenna. The followed approach presents steps of proposed antenna evolution, which starts with a bowtie-shape slotted antenna and a coaxial feed to a final design with a split-bowtie-shaped slot, and a double transmission line feed. Polar dimensions of the outer boundary (radii and angle) and slot profiles together with the dimensions of the feed, whether coaxial or transmission line, were considered as parameters for optimization. Four antennas were accordingly studied, which led to the final split-bowtie-shaped antenna IV. These designs were performed utilizing a special simulation package called "CST Microwave Suite" that included the optimizer "Genetic Algorithm". The final design offers 4 simultaneous bands from a single split-bowtie-shaped slot; a big advantage of unnecessary integration of a set of complicated slots on front and ground planes. Further, the antenna finds applications in mobile GSM and LTE bands 900/1800 MHz and 2.1 GHz, together with the satellite communications band 1.25 GHz. It is therefore simple, non-reconfigurable, easy to fabricate, designed on a single copper layer, with a low cost FR4 substrate and a plane copper ground.

КОГНИТИВНЫЕ МЕХАНИЗМЫ ЭВФЕМИЗАЦИИ МАНИПУЛЯТИВНОГО ХАРАКТЕРА

The article discusses the cognitive mechanisms underlying the creation of manipulative euphemisms; it presents the essence of those cognitive tools that are used by the media to manipulate the recipients, consuming their content. The research tasks are: identification of the parameters enhancing the cognitive mechanism of reframing in relation to euphemisms; determining what part of the original meaning structure is inherited in a new creative meaning of the euphemism formation. The methods used are: conceptual analysis, frame analysis, analysis of socio-cultural interpretant, principle of dynamic conceptual semantics. As a result of the study, we obtained the data on how the process of reframing is carried out with euphemisms used for manipulation purposes. For its implementation, the cognitive mechanisms of analogy, conceptual deviation and conceptual blending are used, backstage cognition is activated. During reframing, the slots of the source frame are mapped onto the slots of the construed frame. Word or phrase representing the source frame profiles the conceptualization and triggers the set of target frame slots. In the case of using a euphemism for reframing, a contextually justified, but more neutral expression that does not carry negative conceptual attributes is chosen. The results can be used in cognitive linguistics and journalism studies.

Temporal assortment of cooperators in the spatial prisoner\u2019s dilemma

We study a spatial, one-shot prisoner’s dilemma (PD) model in which selection operates on both an organism’s behavioral strategy (cooperate or defect) and its decision of when to implement that strategy, which we depict as an organism’s choice of one point in time, out of a set of discrete time slots, at which to carry out its PD strategy. Results indicate selection for cooperators across various time slots and parameter settings, including parameter settings in which cooperation would not evolve in an exclusively spatial model—as in work investigating exogenously imposed temporal networks. Moreover, in the presence of time slots, cooperators’ portion of the population grows even under different combinations of spatial structure, transition rules, and update dynamics, though rates of cooperator fixation decline under pairwise comparison and synchronous updating. These findings indicate that, under certain evolutionary processes, merely existing in time and space promotes the evolution of cooperation.

LBFA: A Load-Balanced and Fragmentation-Aware Resource Allocation Algorithm in Space-Division Multiplexing Elastic Optical Networks

We consider a space-division multiplexing elastic optical network (SDM-EON) that supports super-channels (SChs). A Sch comprises a set of contiguous frequency slots on multiple cores in a multi-core fiber. The problem of finding a lightpath using SChs involves routing, modulation, spectrum and core assignment (RMSCA). To minimize the request blocking probability (RBP), two critical issues must be addressed. First, routing and modulation assignment (RMA) should not cause hotspots, or overutilized links. Second, spectrum and core assignment (SCA) should aim at minimizing fragmentation, or small frequency slot blocks that can hardly be utilized by future requests. In this paper, a pre-computation method is first proposed for better load balancing in RMA. Then an efficient fragmentation-aware SCA is proposed based on a new fragmentation metric that measures both the spectral and spatial fragmentation. With the enhanced RMA and SCA, a joint load-balanced and fragmentation-aware algorithm called LBFA is designed to solve the RMSCA problem. As compared with the existing algorithms, simulation results show that our LBFA provides significant reduction in RBP.

Hybrid strategy for routing, modulation and spectrum assignment in elastic optical networks

In this paper, we propose a novel hybrid strategy that combines mixed integer linear programming (MILP) formulation with different alternative routing approaches that is capable of solving, simultaneously, the routing, modulation format and spectrum assignment problem in the design of elastic optical networks. We extend our proposal to a path-link formulation that, although it is not guaranteed to find the optimal solution, it has shown reasonable solutions within inferior simulation time, allowing it to be used in not very short networks. We also compare our proposal with another MILP formulation and three adapted heuristics, all available in the literature. The results show the benefits of our proposal considering diverse simulation scenarios and different number of modulation formats over 19 realistic networks in terms of simulation run time and maximum number of used set of slots.

Large area microwave plasma CVD of diamond using composite right/left-handed materials

Diamond growth at low temperatures (≤400 °C) and over large areas is attractive for materials, which are sensitive to high temperatures and require good electronic, chemical or surface tribological properties. Resonant-cavity microwave plasma enhanced (MWPE) chemical vapor deposition (CVD) is a standard method for growing diamonds, however, with limited deposition area. An alternative method for CVD of diamond over large area and at low temperature is to use a surface wave plasma (SWP). In this work we introduce a novel method to excite SWP using composite right/left-handed (CRLH) materials and demonstrate growth of nanocrystalline diamond (NCD) on 4-inch Si wafers. The method uses a set of slotted CRLH waveguides coupled to a resonant launcher, which is connected to a deposition chamber. Each CRLH waveguide supports infinite wavelength propagation and consists of a chain of periodically cascaded unit cells. The SWP is excited by a set of slots placed to interrupt large area surface current on the resonant launcher. This configuration yields a uniform gas discharge distribution. We achieve 80 nm/h growth rate for NCD films with a low surface roughness (5–10 nm) at 395 °C and 0.5 mbar pressure using a H2/CH4/CO2 gas mixture.

Optimal algorithms for scheduling under time-of-use tariffs

We consider a natural generalization of classical scheduling problems to a setting in which using a time unit for processing a job causes some time-dependent cost, the time-of-use tariff, which must be paid in addition to the standard scheduling cost. We focus on preemptive single-machine scheduling and two classical scheduling cost functions, the sum of (weighted) completion times and the maximum completion time, that is, the makespan. While these problems are easy to solve in the classical scheduling setting, they are considerably more complex when time-of-use tariffs must be considered. We contribute optimal polynomial-time algorithms and best possible approximation algorithms. For the problem of minimizing the total (weighted) completion time on a single machine, we present a polynomial-time algorithm that computes for any given sequence of jobs an optimal schedule, i.e., the optimal set of time slots to be used for preemptively scheduling jobs according to the given sequence. This result is based on dynamic programming using a subtle analysis of the structure of optimal solutions and a potential function argument. With this algorithm, we solve the unweighted problem optimally in polynomial time. For the more general problem, in which jobs may have individual weights, we develop a polynomial-time approximation scheme (PTAS) based on a dual scheduling approach introduced for scheduling on a machine of varying speed. As the weighted problem is strongly NP-hard, our PTAS is the best possible approximation we can hope for. For preemptive scheduling to minimize the makespan, we show that there is a comparably simple optimal algorithm with polynomial running time. This is true even in a certain generalized model with unrelated machines.