Recent studies have explored various object detection methods that use channel state information (CSI) in wireless local area networks (WLANs) such as IEEE 802.11ac. However, evaluations used with WLAN-based approaches by experimentation mainly target indoor object detection scenarios. Therefore, the effects of antenna placement of access points (AP) and stations (STA) on detection performance in outdoor environments have not been clarified. As described herein, by experimentation, we evaluate the performance of our developed WLAN-based object detection system with distributed antennas in outdoor and large-scale indoor scenarios. Then we clarify the effects of AP and STA placements on the achieved detection performance, where an off-the-shelf WLAN device is used to capture feedback CSI, including beamforming weight information. We leverage these data as feature information for machine learning (ML)-based object detection. Findings indicate that the object detection method can increase the detection probability effectively in specific locations by appropriate location of AP and STA, while reducing the necessary complexity for ML model training and detecting the target in outdoor and large-scale indoor scenarios.

Wireless sensing is a promising technology for future wireless communication networks to realize various application services. Wireless local area network (WLAN)-based localization approaches using channel state information (CSI) have been investigated intensively. Further improvements of detection performance will depend on selecting appropriate feature information and determining the placements of distributed antenna elements. This paper presents a proposal of an enhanced device-free WLAN-based localization scheme with beam-tracing-based antenna placement optimization and spatially localized regression, where beam-forming weights (BFWs) are used as feature information for training machine-learning (ML)-based models localized to partitioned areas. By this scheme, the antenna placement at the access point (AP) is determined by solving a combinational optimization problem with beam-tracing between AP and station (STA) without knowledge of the CSI. Additionally, we propose the use of localized regression to improve localization accuracy with low complexity, where classification and regression-based ML models are used for coarse and precise estimations of the target position. We evaluate the proposed scheme effects on localization performance in an indoor environment. Experiment results demonstrate that the proposed antenna placement and localized regression scheme improve the localization accuracy while reducing the necessary complexity for both off-line training and on-line localization relative to other reference schemes.

Various object detection schemes using channel state information (CSI) in wireless local area networks (WLANs) such as IEEE802.11ac have been investigated recently. For further detection performance improvement, adopting a proper feature selection technique and using an appropriate antenna placement to obtain more effective CSI is important As proposed herein, a device-free WLAN-based object detection scheme with reflectors is developed, where the feedback CSI in WLANs (i.e., beamforming weight information) is captured at an off-the-shelf WLAN device and is used as feature information for machine learning (ML)-based object detection. In this scheme, reflectors are placed to create a multipath rich condition in an observation area. By experimentation, we demonstrate that reflectors can improve object detection performance. Additionally, we clarify that proper antenna placement effectively improves the detection accuracy, even when using a small number of antenna elements in an indoor environment. Experiment results demonstrate that the object detection performance of WLAN systems in an indoor environment can be improved by selecting antenna placement appropriately and by using measured concatenated CSI as effective feature information.

Wireless local area network (WLAN) based object detection techniques have attracted much attention. However, the detection performance of WLAN-based object detection is highly dependent on the antenna topology used. In this paper, we experimentally evaluate performance of antenna topology on the achieved object detection performance using channel state information (CSI) in WLAN systems, where spatially concatenated CSI is utilized as effective feature information for machine learning (ML)-based object detection. Based on the experiment results, we discuss how much object detection performance improvement is expected when the proper antenna topology is used at the access point side. We also experimentally clarify that the detection accuracy is improved when concatenated CSI is used as feature information for ML-based object detection for various antenna placement scenarios.

We initiate a study of automata-based model checking for previously proposed quantitative linear time logics interpreted over coalgebras. Our results include: (i) an automata-theoretic characterisation of the semantics of these logics, based on a notion of extent of a quantitative parity automaton, (ii) a study of the expressive power of Buchi variants of such automata, with implications on the expressiveness of fragments of the logics considered, and (iii) a naive algorithm for computing extents, under additional assumptions on the domain of truth values.

Despite its success in producing numerous general results on state-based dynamics, the theory of coalgebra has struggled to accommodate the Buechi acceptance condition---a basic notion in the theory of automata for infinite words or trees. In this paper we present a clean answer to the question that builds on the "maximality" characterization of infinite traces (by Jacobs and Cirstea): the accepted language of a Buechi automaton is characterized by two commuting diagrams, one for a least homomorphism and the other for a greatest, much like in a system of (least and greatest) fixed-point equations. This characterization works uniformly for the nondeterministic branching and the probabilistic one; and for words and trees alike. We present our results in terms of the parity acceptance condition that generalizes Buechi's.

In the context of formal verification in general and model checking in particular, parity games serve as a mighty vehicle: many problems are encoded as parity games, which are then solved by the seminal algorithm by Jurdzinski. In this paper we identify the essence of this workflow to be the notion of progress measure, and formalize it in general, possibly infinitary, lattice-theoretic terms. Our view on progress measures is that they are to nested/alternating fixed points what invariants are to safety/greatest fixed points, and what ranking functions are to liveness/least fixed points. That is, progress measures are combination of the latter two notions (invariant and ranking function) that have been extensively studied in the context of (program) verification. We then apply our theory of progress measures to a general model-checking framework, where systems are categorically presented as coalgebras. The framework's theoretical robustness is witnessed by a smooth transfer from the branching-time setting to the linear-time one. Although the framework can be used to derive some decision procedures for finite settings, we also expect the proposed framework to form a basis for sound proof methods for some undecidable/infinitary problems.