Series Preface * Preface to the Third Edition * 1 Linear Systems * 2 Nonlinear Systems: Local Theory * 3 Nonlinear Systems: Global Theory * 4 Nonlinear Systems: Bifurcation Theory * References * Index

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Differential Equations and Dynamical Systems

Due to their light weight, low power, and practically unlimited identification capacity, radio frequency identification (RFID) tags and associated devices offer distinctive advantages and are widely recognized for their promising potential in context-aware computing; by tagging objects with RFID tags, the environment can be sensed in a cost- and energy-efficient means. However, a prerequisite to fully realizing the potential is accurate localization of RFID tags, which will enable and enhance a wide range of applications. In this paper we show how to exploit the phase difference between two or more receiving antennas to compute accurate localization. Phase difference based localization has better accuracy, robustness and sensitivity when integrated with other measurements compared to the currently popular technique of localization using received signal strength. Using a software-defined radio setup, we show experimental results that support accurate localization of RFID tags and activity recognition based on phase difference.

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Accurate localization of RFID tags using phase difference

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the object's orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches).This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compass's key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robot's consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.

http://people.csail.mit.edu/fadel/papers/rfcompass-paper.pdf

RF-compass: robot object manipulation using RFIDs

Categorization with basic color terms is an intuitive and universal aspect of color perception. Yet research on visual working memory capacity has largely assumed that only continuous estimates within color space are relevant to memory. As a result, the influence of color categories on working memory remains unknown. We propose a dual content model of color representation in which color matches to objects that are either present (perception) or absent (memory) integrate category representations along with estimates of specific values on a continuous scale (“particulars”). We develop and test the model through 4 experiments. In a first experiment pair, participants reproduce a color target, both with and without a delay, using a recently influential estimation paradigm. In a second experiment pair, we use standard methods in color perception to identify boundary and focal colors in the stimulus set. The main results are that responses drawn from working memory are significantly biased away from category boundaries and toward category centers. Importantly, the same pattern of results is present without a memory delay. The proposed dual content model parsimoniously explains these results, and it should replace prevailing single content models in studies of visual working memory. More broadly, the model and the results demonstrate how the main consequence of visual working memory maintenance is the amplification of category related biases and stimulus-specific variability that originate in perception.

Why some colors appear more memorable than others: A model combining categories and particulars in color working memory.

In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques.

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Modeling RFID signal strength and tag detection for localization and mapping

Binding refers to the operation that groups different features together into objects. We propose a neural architecture for feature binding in visual working memory that employs populations of neurons with conjunction responses. We tested this model using cued recall tasks, in which subjects had to memorize object arrays composed of simple visual features (color, orientation, and location). After a brief delay, one feature of one item was given as a cue, and the observer had to report, on a continuous scale, one or two other features of the cued item. Binding failure in this task is associated with swap errors, in which observers report an item other than the one indicated by the cue. We observed that the probability of swapping two items strongly correlated with the items' similarity in the cue feature dimension, and found a strong correlation between swap errors occurring in spatial and nonspatial report. The neural model explains both swap errors and response variability as results of decoding noisy neural activity, and can account for the behavioral results in quantitative detail. We then used the model to compare alternative mechanisms for binding nonspatial features. We found the behavioral results fully consistent with a model in which nonspatial features are bound exclusively via their shared location, with no indication of direct binding between color and orientation. These results provide evidence for a special role of location in feature binding, and the model explains how this special role could be realized in the neural system.SIGNIFICANCE STATEMENT The problem of feature binding is of central importance in understanding the mechanisms of working memory. How do we remember not only that we saw a red and a round object, but that these features belong together to a single object rather than to different objects in our environment? Here we present evidence for a neural mechanism for feature binding in working memory, based on encoding of visual information by neurons that respond to the conjunction of features. We find clear evidence that nonspatial features are bound via space: we memorize directly where a color or an orientation appeared, but we memorize which color belonged with which orientation only indirectly by virtue of their shared location.

https://www.jneurosci.org/content/jneuro/37/14/3913.full.pdf

Neural Architecture for Feature Binding in Visual Working Memory

In recent years, radio frequency identification (RFID) has found its way into the field of mobile robot navigation. On the one hand, the technology promises to contribute solutions to common problems in self-localization and mapping such as the data association problem. On the other hand, questions like how to cope with poor or even missing range and bearing information remain open. In this paper, we present a novel method which tackles these challenges: Inspired by vision-based self-localization approaches, it utilizes RFID snapshots for the estimation of the robot pose. Our experiments show that the new technique enables a robot to successfully localize itself in an indoor environment. The accuracy is comparable to the one of a previous approach using an explicit model of detection probabilities. Our method, however, requires fewer iterations of the underlying particle filter in order to converge to the approximate robot pose.

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Using RFID Snapshots for Mobile Robot Self-Localization.

No fixed item limit in visuospatial working memory.

http://old.nbu.bg/cogs/events/5_S_Sandamirksaya_EtAl_2013.pdf

Using Dynamic Field Theory to extend the embodiment stance toward higher cognition

In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products.

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Sebastian Schneegans

Papers

Neural Architecture for Feature Binding in Visual Working Memory

Using RFID Snapshots for Mobile Robot Self-Localization.

No fixed item limit in visuospatial working memory.

Using Dynamic Field Theory to extend the embodiment stance toward higher cognition

Self-Localization with RFID snapshots in densely tagged environments