3-Dimensional Embodied Reference Understanding (3D-ERU) combines a language description and an accompanying pointing gesture to identify the most relevant target object in a 3D scene. Although prior work has explored pure language-based 3D grounding, there has been limited exploration of 3D-ERU, which also incorporates human pointing gestures. To address this gap, we introduce a data augmentation framework– Imputer, and use it to curate a new benchmark dataset– ImputeRefer for 3D-ERU, by incorporating human pointing gestures into existing 3D scene datasets that only contain language instructions. We also propose Ges3ViG, a novel model for 3D-ERU that achieves ~30% improvement in accuracy as compared to other 3D-ERU models and ~9% compared to other purely language-based 3D grounding models. Our code and dataset are available at https://github.com/AtharvMane/Ges3ViG.

Autonomous Vehicles (AVs) rely on sensor-based perception systems and high-definition maps for navigation. How-ever, their performance may degrade in challenging conditions such as poor visibility, unpredictable traffic conditions, or GNSS-denied environments like urban canyons or temporary construction zones. To address these limitations, we introduce Time-Logic-Map (TLM), a spatial messaging language that enables the road infrastructure to broadcast structured, machine-readable messages to supplement AV perception and support decision-making. This approach reduces the dependence on onboard sensors and describes road logic through machine-oriented language. TLM organizes road information into three layers: map, defining road geometry and a local 3D Cartesian coordinate system; logic, encoding the structural layout of roads and the precedence rules governing vehicle movements; and time, broadcasting real-time information like traffic signal phases. We describe modular design using multiple practical examples in standard and complex intersections, as well as in road construction zones.

Collisions caused by simultaneous tag responses are a fundamental challenge in RFID systems, limiting their throughput and scalability. Existing solutions often rely on complex signal processing or hardware modifications, reducing practicality. This paper presents a novel collision resolution algorithm, fully compatible with the EPC Gen2 standard, that decodes individual tag responses by analyzing I/Q constellation patterns formed during collisions. The method introduces a new constellation cluster labeling strategy inspired by geometric alignment from computer vision, which uses analytical geometry and pattern matching techniques to efficiently resolve tag states without requiring retransmissions, channel estimation nor hardware changes. The algorithm reliably resolves up to four colliding tags and achieves up to a 33% relative gain in time-normalized throughput over Framed Slotted ALOHA (FSA). To support next-generation protocol enhancements, we also propose a multi-tag acknowledgment configuration, where the algorithm achieves up to a 121% relative gain, with peak performance at a slot-to-tag ratio of 0.5. Moreover, the algorithm achieves a 25× speedup in decoding time compared to the latest state-of-the-art method, significantly enhancing its practicality for real-time deployment. These results demonstrate the method’s effectiveness across both current and next-generation RFID systems.

Nowadays, Radio Frequency IDentification (RFID) systems are extensively used in many applications, such as warehouse management, inventory tracking, sensing or localization. The usage of passive RFID tags allows for low maintenance, thus reducing complexity and cost. However, in scenarios with a large population of tags, the process of discovering tags is time-consuming and may exceed the time requirements of certain applications. Rapidly anticipating the number of tags in advance, namely RFID counting, would help at optimizing the system efficiency. This work proposes an alternative conceptualization of the counting process, embedded within a novel framework that redefines collisions as a usable resource, preserving backward compatibility and contributing to lower the system latency up to 97% with a reliability of minimum 95%, depending on the scenario. Extensive system-level simulations and experimental results demonstrate that our proposed solution successfully detects an entire population of tags within a single round, offering significant advantages for various applications, including a subsequent identification phase.

This work-in-progress innovative practice paper presents a novel approach to 1) extract tacit knowledge from expert trainers while they perform a task demo, 2) decrease the learner's cognitive load via the use of instructional videos portraying the variables at play during a task demonstration, and 3) define quantifiable metrics of expertise by extracting features that differentiate experts from novice practitioners. Implicit or tacit knowledge is know-how that experts develop with experience and is difficult to verbalize, formalize or explicitly transfer to others. For this reason, knowledge transfer from expert to apprentice is usually slow and inefficient. Our approach seeks to support knowledge transfer using technology-enhanced approaches. Here, we focus on extracting and describing exper-tise. We do so by instrumenting experts, trainees and their tools with sensors that can help structure and formalize knowledge. Our first application of this framework is on the knowledge transfer between an expert and novice glassblower. Glassblowing is well known for its crucial expert/apprentice relation and its slow learning rate due in part to the difficulties in verbal transfer of skills. Our framework seeks to capture relevant data while an expert glassblower demonstrates basic actions in a beginners glass blowing course. Our sensors collect eye-tracking activity, verbal demo instructions, pipe accelerometry, air infusion, scene video and muscle activity (EMG), which continuously monitor the expert, their explanations, the tools, and the glass piece. We bring together all the sensed data into instructional videos to be used by novice learners as supportive training material. We present preliminary results related to metrics of expertise and future steps towards gathering similar data from novices. This will help develop AI-based models to extract data-driven differences between experts and apprentices, which can be used as further instructional material. We will also present plans to test the instructional effectiveness of the developed videos and how our approach can be used in other training settings involving tacit knowledge transfer.

Previous work on misbehavior detection and trust management for vehicle-to-everything (V2X) communication security is effective in identifying falsified and malicious V2X data. Each vehicle in a given region can be a witness to report on the misbehavior of other nearby vehicles, which will then be added to a “blacklist.” However, there may not exist enough witness vehicles that are willing to opt-in in the early stage of connected-vehicle deployment. In this article, we propose a “whitelisting” approach to V2X security, titled proof-of-travel (POT), which leverages the support of roadside infrastructure. Our goal is to transform the power of cryptography techniques embedded within vehicle-to-infrastructure (V2I) protocols into game-theoretic mechanisms to incentivize connected-vehicle data sharing and validate data trustworthiness simultaneously. The key idea is to determine the reputation and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testified by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra costs for normal vehicles, which naturally want to move from the origin to the destination. The POT protocol is used to enhance the security of previous voting-based data validation algorithms for V2I crowdsensing applications. For the POT-enhanced voting, we prove that all vehicles choosing to cheat are not a pure Nash equilibrium using game-theoretic analysis. Simulation results suggest that the POT-enhanced voting is more robust to malicious data.

In this work, we leverage neural mechanisms of visual attention to improve the accuracy of a commercial eye tracker through the analysis of electroencephalography (EEG) waves. Gaze targets were rendered in a computer screen with imperceptible flickering stimuli (≥ 40Hz) that elicited attention-modulated steady-state visual evoked potentials (SSVEPs). Our hybrid system combines EEG and eye-tracking modalities to overcome accuracy limitations of the gaze-tracker alone. We integrate EEG and gaze data to efficiently exploit their complementary strengths driving a Bayesian probabilistic decoder that estimates the target gazed by the user. Our system’s performance was analyzed across the screen with varying target sizes, spacings and dataset epoch lengths, using data from 10 subjects. Overall, our hybrid approach improves the classification accuracy of the eye tracker alone for all target parameters and dataset epoch lengths in 11 units on average. The system shows a larger impact at peripheral screen regions where performance enhancement is maximal, reaching improvements of over 45 units. The findings of this work demonstrate that the intrinsic accuracy limitations of camera-based eye-trackers can be corrected with the integration of EEG data, and opens opportunities for gaze tracking applications with higher target granularity.

A roadside adversary who holds compromised vehicle-to-everything (V2X) credentials can easily spoof vehicle identities and broadcast fabricated messages that jeopardize the maneuvers of surrounding vehicles. Previous work on the security of ad hoc networks suggests the use of a side channel for two parties to exchange digital certificates to prevent impersonation and man-in-the-middle attacks on the main wireless channel. This paper presents a two-factor authentication scheme by leveraging line-of-sight (LOS) communication as the side channel to impede roadside adversaries who try to impersonate legitimate moving vehicles in the non-line-of-sight (NLOS) channel. To gain the trust of other traffic participants, a vehicle that has received a challenge message broadcast by infrastructure through the main (NLOS) wireless channel must send back its response through the LOS channel to demonstrate it is indeed a vehicle in traffic. The directional property and visual confirmation of the LOS channel and the fact that vehicle movement is ascertained based on physics make it extremely difficult for the roadside adversary to finish the response-challenge process without being detected. Experimental results demonstrate the feasibility of using the proposed scheme for authenticating low-speed vehicles. However, for authenticating vehicles traveling at high speed, transmitting the response message containing certificates through the LOS channel can create a communication bottleneck for the authentication process, although implicit certificates can be adopted to reduce the total authentication time. Future work will explore the alternative format of the challenge-response protocol and the potential technologies for realizing LOS communication to reduce the communication bottleneck.

This article presents the concept of a chipless radio-frequency identification-based pervasive crack sensing scheme for structural health monitoring (SHM). This scheme includes the design of a novel “smart skin” sensor that can provide contiguous or nondiscretized detection of a structural deformation at any point on its surface. The proposed sensor can identify the growth and propagation of cracks in an area of a building structure. Smart skin sensor has a sensitive microwave structure made of cascaded novel split box resonators coupled to a coplanar waveguide (CPW)-based transmission line. This enables it to offer an uninterrupted crack detection along with the ability to detect multiple structural perturbations simultaneously. The proposed sensor can detect a crack width of as little as 0.5 mm having any orientation; namely, vertical, horizontal, and diagonal cracks. In addition to crack detection, the sensing tag can also provide a distinctive response for moisture ingress into the structure. The article illustrates the theory behind choosing the split box resonator in this sensing scheme followed by the sensor design. It also provides a thorough analysis of the sensor, based on the simulated and experimentally obtained results. Both of these results conform to each other very well, which lays the foundation to use machine learning as future work, in detecting random structural cracks. Such results incorporate many distinguishing features that enable an estimation of crack location and orientations using visual and machine-based classification approaches. The repeatability of the obtained results is also established through the experimental analysis.

We propose an integrated approach to active exploration by exploiting the Cartographer method as the base SLAM module for submap creation and performing efficient frontier detection in the geometrically co-aligned submaps induced by graph optimization. We also carry out analysis on the reachability of frontiers and their clusters to ensure that the detected frontier can be reached by robot. Our method is tested on a mobile robot in real indoor scene to demonstrate the effectiveness and efficiency of our approach.

In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x,y,yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection.

Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points, allowing inter-point correlations to be well-exploited and 3D shape generative processes to be better interpreted. Since point cloud object shapes are typically encoded by long-range dependencies, we augment our model with dedicated self-attention modules to capture such relations. Extensive evaluations show that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to realism and diversity. Several important applications, such as unsupervised feature learning and shape arithmetic operations, are also demonstrated.

Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target's pose parameters. Quantitative and qualitative experiments prove this system's efficacy and show potential for fusing these emerging technologies in real-world applications.

The testing of cyber-physical systems such as automated vehicles (AV) is difficult as engineers face challenges from both cybersecurity and safety domains that start to converge. For cybersecurity, conducting vulnerability testing even before mitigation designs are fixed requires the predication and modeling of adversaries' malicious behaviors. For safety, complete testing at system-level is time-consuming and also infeasible due to the large combination of operational domains. To help engineers design cost-effective mitigation solutions, this paper presents a framework for constructing testing scenarios driven by cyber threats that can be evaluated early in the design process. The testing results can inform the design of mitigation strategies and help engineers in constructing security requirements such that the large solution space will converge more quickly on effective designs. We also illustrate how to build visualization tools to support this process.

We present an Augmented Reality (AR) visualization and interaction tool for users to control Internet of Things (IoT) devices with hand gestures. Today, smart IoT devices are becoming increasingly ubiquitous with diverse forms and functions, yet most user controls over them are still limited to mobile devices and web interfaces. Recently, AR has been developed rapidly, and provided immersive solutions to enhance user experience of applications in many fields. Its capability to create immersive interactions allows AR to improve the way smart devices are controlled via more direct visual feedback. In this paper, we create a functional prototype of one such system, enabling seamless interactions with sound and lighting systems through the use of augmented hand-controlled interaction panels. To interpret users' intentions, we implement a standard 2D convolution neural network (CNN) for real-time hand gesture recognition and deploy it within our system. Our prototype is also equipped with a simple but effective object detector which can identify target devices within a proper range by analyzing geometric features. We evaluate the performance of our system qualitatively and quantitatively and demonstrate it on two smart devices.

Communication range in passive radio-frequency identification (RFID) front-end devices is a critical barrier in the real-world implementation of this low-cost technology. Purely passive RFID tags power up by harvesting the limited RF energy transmitted by the interrogator, and communicate by backscattering the incident signal. This mode of communication keeps manufacturing costs below a few cents per tag, but the limited power available at the tag undermines long-range deployment. In this paper, we present an approach to use photovoltaics (PV) to augment the available energy at the tag to improve read range and sensing capabilities. We provide this extra-energy to the RFID integrated circuit (IC) using minimum additional electronics yet enabling persistent sensor-data acquisition. Current and emerging thin-film PV technologies have significant potential for being very low-cost, hence eliminating the barrier for implementation and making of PV-RFID wireless sensors. We reduce the long-range PV-RFID idea to practice by creating functional prototypes of: 1) a wireless building environment sensor to monitor temperature and 2) an embedded tracker to find lost golf balls. The read range of PV-RFID is enhanced eight times compared to conventional passive devices. In addition, the PV-RFID tags persistently transmit large volumes of sensor data (>0.14 million measurements per day) without using batteries. For communication range and energy persistence, we observe good agreement between calculated estimates and experimental results. We have also identified avenues for future research to develop low-cost PV-RFID devices for wireless sensing in the midst of the other competitive wireless technologies, such as Bluetooth, ZigBee, long range (LoRa) backscatter etc.

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.

In this work we present Sublime, a new concept of Steady-State Visually Evoked Potential (SSVEP) based Brain-Computer Interface (BCI) where brain-computer communication occurs by capturing imperceptible visual stimuli integrated in the virtual scene and effortlessly conveying subliminal information to a computer. The technology was tested in a Virtual Reality (VR) environment, where the subject could navigate between the different menus by just gazing at them. The ratio between the stimuli frequencies and the refresh rate of the VR display creates an undesired perception of beats for which different solutions are proposed. To inform the user of target activation, real-time feedback in the form of loading bars is incorporated under each selectable object. We conducted experiments with several subjects and though the system is slower than a conventional joystick, users reported a satisfactory overall experience, in part due to the unexpected responsiveness of the system, as well as due to the fact that virtual objects flickered at a rate that did not cause annoyance. Since the imperceptible visual stimuli can be integrated unobtrusively to any element of the virtual world, we conclude that the potential applications of Sublime are extensive, especially in situations where knowing user's visual focus can be relevant.

The Internet of Things' (IoT's) rapid growth is constrained by resource use and fears about privacy and security. A solution jointly addressing security, efficiency, privacy, and scalability is needed to support continued expansion. We propose a solution modeled on human use of context and cognition, leveraging cloud resources to facilitate IoT on constrained devices. We present an architecture applying process knowledge to provide security through abstraction and privacy through remote data fusion. We outline five architectural elements and consider the key concepts of the “data proxy” and the “cognitive layer.” The data proxy uses system models to digitally mirror objects with minimal input data, while the cognitive layer applies these models to monitor the system's evolution and to simulate the impact of commands prior to execution. The data proxy allows a system's sensors to be sampled to meet a specified quality of data target with minimal resource use. The efficiency improvement of this architecture is shown with an example vehicle tracking application. Finally, we consider future opportunities for this architecture to reduce technical, economic, and sentiment barriers to the adoption of the IoT.

This paper summarizes the state of the art in connected vehicles-from the need for vehicle data and applications thereof, to enabling technologies, challenges, and identified opportunities. Connectivity is increasing around the world and its expansion to vehicles is no exception. With improvements in connectivity, sensing, and computation, the future will see vehicles used as development platforms capable of generating rich data, acting based on inference, and effecting great change in transportation, the human-vehicle dynamic, the environment, and the economy. Connected vehicle technologies have already been used to improve fleet safety and efficiency, with emerging technologies additionally allowing data to be used to inform aspects of vehicle design, ownership, and use. While the demand for connected vehicles and its enabling technology has progressed significantly in recent years, there remain challenges to connected and collaborative vehicle application deployment before the full potential of connected cars may be realized. From extensibility and scalability to privacy and security, this paper informs the reader about key enabling technologies, opportunities, and challenges in the connected vehicle landscape.

Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow . Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful over- and under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners.

The articles in this special section focus on the use of cloud computing in the robotics industry. The Internet and the availability of vast computational resources, ever-growing data and storage capacity have the potential to define a new paradigm for robotics and automation. An intelligent system connected to the Internet can expand its onboard local data, computation and sensors with huge data repositories from similar and very different domains, massive parallel computation from server farms and sensor/actuator streams from other robots and automata. It is the potential and also the research challenges of the field that become the focus on this special section. The goal is to group together and to show the state-of-the-art of this newly emerged field, identify the relevant advances and topics, point out the current lines of research and potential applications, and discuss the main research challenges and future work directions.

This paper presents a novel communication method for semi-passive RFID based sensors. The new method uses a digitally reconfigurable UHF RFID tag antenna to modulate sensed information at an RFID tag on to the received signal strength indicator (RSSI) response perceived at an RFID reader. This technique is completely compatible with the existing class 1 generation 2 UHF air interface protocol thereby enabling the use of existing RFID reader infrastructure to decode the additional sensed information. The effect of read distance, environment and bit duration on the performance of the communication method is examined through measurements obtained from a prototype. Through experimental verification, it is demonstrated that error free transmission of sensor information can be achieved up to 3.5 meters in different environments with a bit duration of 500 ms. Prospective future research directions are also discussed.

In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG scale-space blob detector to a 5-D gLoG scale-space one, where the five parameters are image-domain coordinates (x, y), scales (σ(x), σ(y)), and orientation (θ), respectively. Instead of searching the local extrema of the image's 5-D gLoG scale space for locating blobs, a more feasible solution is given by locating the local maxima of an intermediate map, which is obtained by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. The proposed gLoG-based blob detector is applied to both biomedical images and natural ones such as general road-scene images. For the biomedical applications on pathological and fluorescent microscopic images, the gLoG blob detector can accurately detect the centers and estimate the sizes and orientations of cell nuclei. These centers are utilized as markers for a watershed-based touching-cell splitting method to split touching nuclei and counting cells in segmentation-free images. For the application on road images, the proposed detector can produce promising estimation of texture orientations, achieving an accurate texture-based road vanishing point detection method. The implementation of our method is quite straightforward due to a very small number of tunable parameters.

We propose a framework for road-vanishing-point detection based on a new generalized Laplacian of Gaussian (gLoG) filter. In the first part, the gLoG filter can be applied to estimate the texture orientation at each pixel of an image, and the road vanishing point can be detected based on the estimated texture orientations. However, such a texture-based road-vanishing-point detection scheme suffers from high computational complexity. In the second part, an efficient gLoG-based road-vanishing-point detection method is proposed by only using the dominant texture orientations estimated at a sparse set of salient microblob road regions, where the gLoG filter is used to detect these salient microblob areas and simultaneously estimate their dominant texture orientations. Experimental results on 1003 general road images show that the efficient gLoG-based method is significantly faster than a Gabor-filter-based method, whereas the detection accuracy is comparable. The nonefficient gLoG-based method is more accurate in detecting the vanishing point than the Gabor-based approach.

Radio-frequency identification (RFID) has been well established as an effective technology for track and trace applications. In this paper, we go beyond the ID in RFID, and discuss the potential for RFID tags to be used as low-cost sensors by mapping a change in some physical parameter of interest to a controlled change in RFID tag antenna electrical properties. We will also show that it is possible to design the tag antenna to suffer a permanent change in case of violation of a critical threshold in the parameter of interest thereby creating a low-cost threshold sensing mechanism. This can be achieved by inducing controlled changes to the tag antenna geometry parameters or to the antenna boundary conditions, in effect creating a nonelectric memory to monitor state. After identifying the application space for which this class of sensing is well suited, we present details into the design and testing of three different kinds of sensors based on this sensing paradigm. We demonstrate how we use this concept to sense displacements, temperature thresholds, and fluid levels. We will show that RFID-tag-antenna-based sensing has the potential to revolutionize application domains in which there is a need for low-cost, long-lasting, ubiquitous sensors.

We address theoptimal sensor arrangement problem, which is the determination of a geometric configuration of sensors such that the mean-squared error (MSE) in the estimation of an unknown trigonometric polynomial is minimum. Unsurprisingly, an arrangement in which sensors are spaced uniformly in each dimension is optimal. However, for multidimensional problems the minimum MSE is achieved with a much larger class of configurations that we callgeneralized regular arrangements. These arrangements are not necessarily generated by lattices and may exhibit great nonuniformity locally.

Radio-frequency identification (RFID) poses a number of research challenges, such as interference mitigation, throughput optimization and security over the RF channel. A number of new approaches to address these issues have been proposed recently, but due to the highly integrated nature of passive RFID tags, it is difficult to evaluate them in real-world scenarios. In this paper, we present an RFID simulation engine, RFIDSim, which implements the ISO 18000-6C communication protocol and supports pathloss, fading, backscatter, capture, and tag mobility models. This paper also shows that our implementation of RFIDSim that relies on a discrete event simulator can be used to simulate large populations featuring thousands of RFID tags. RFIDSim also simulates the deep fades that lead to frequent power losses of the battery-less RFID tags by modeling the multipath effects statistically.

The eight articles in this special section describe state-of-the-art technologies and tools and one application of RFID.

We present the Colorwave algorithm, a simple, distributed, on-line algorithm for the reader collision problem in radio frequency identification (RFID) systems. RFID systems are increasingly being used in applications, such as those experienced in supply chain management, which require RFID readers to operate in close proximity to one another. Readers physically located near one another may interfere with one another's operation. Such reader collisions must be minimized to ensure the correct operation of the RFID system. The Colorwave algorithm yields on-line solutions that are near the optimal static solutions. The dynamic nature of the algorithm enables the RFID system to automatically adapt to changes in the system and in the operating environment of the system.

We present Colorwave, a medium access control (MAC) protocol designed for wireless sensor networks such as radio frequency identification (RFID) reader networks. A network of readers will collaborate for a common application such as item-level monitoring in supply chain management. Readers may be deployed in an ad hoc manner, and readers must not interfere with one another's reader-to-tag communication. Colorwave capitalized on the localized nature of reader-to-tag communications to provide an on-line, distributed, and localized MAC protocol that minimized reader-to-reader interference.

We introduce a class of path optimization problems, which we call "sweeping path problems," found in a wide range of engineering applications. The question is how to find a family of curve segments on a free-form surface that optimizes a certain objective or a cost while respecting specified constraints. For example, when machining a free-form surface, we must ensure that the surface can be machined or swept as quickly as possible while respecting a given geometric tolerance, and while satisfying the speed and the acceleration limits of the motors. The basic requirement of engineering tasks of this type is to "visit" or "cover" an entire area, whereas conventional optimal control theory is largely about point-to-point control. Standard ordinary differential equation-based Lagrangian description formulations are not suitable for expressing or managing optimization problems of this type. We introduce a framework using an Eulerian description method, which leads to partial differential equations. We show that the basic requirement is expressed naturally in this formulation. After defining the problem, we show the connection between the two perspectives. Using this reasoning, we develop the necessary conditions for the optimality of the problem. Finally, we discuss computational approaches for solving the problem.