Haptic augmented reality (AR) enables the user to feel a real environment augmented with synthetic haptic stimuli. This article addresses two important topics in haptic AR. First, a new taxonomy for haptic AR is established based on a composite visuo-haptic reality-virtuality continuum extended from the conventional continuum for visual AR. Previous studies related to haptic AR are reviewed and classified using the composite continuum, and associated research issues are discussed. Second, the feasibility of haptically modulating the feel of a real object with the aid of virtual force feedback is investigated, with the stiffness as a goal haptic property. All required algorithms for contact detection, stiffness modulation, and force control are developed, and their individual performances are thoroughly evaluated. The resulting haptic AR system is also assessed in a psychophysical experiment, demonstrating its competent perceptual performance for stiffness modulation. To our knowledge, this work is among the first efforts in haptic AR for systematic augmentation of real object attributes with virtual forces, and it serves as an initial building block toward a general haptic AR system. Finally, several research issues identified during the feasibility study are introduced, with the aim of eliciting more research interest in this exciting yet unexplored area.

Haptic augmented reality (AR) mixes a real environment with computer-generated virtual haptic stimuli, enabling the system to modulate the haptic attributes of a real object to desired values. This paper reports our second study on this functionality, with stiffness as a goal modulation property. Our first study explored the potential of haptic AR by presenting an effective stiffness modulation system for simple 1D interaction. This paper extends the system so that a user can interact with a real object in any 3D exploratory pattern while perceiving its augmented stiffness. We develop a complete set of algorithms for contact detection, deformation estimation, force rendering, and force control. The core part is the deformation estimation where the magnitude and direction of real object deformation are estimated using a contact dynamics model identified in a preprocessing step. All algorithms are designed in a way that maximizes the efficiency and usability of the system while maintaining convincing perceptual quality. In particular, the need for a large amount of preprocessing such as geometry modeling is avoided to improve the usability. The physical performance of each algorithm is thoroughly evaluated with real samples. Each algorithm is experimentally verified to satisfy the physical performance requirements that need to be satisfied to achieve convincing rendering quality. The final perceptual quality of stiffness rendering is assessed in a psychophysical experiment where the difference in the perceived stiffness between augmented and virtual objects is measured. The error is less than the human discriminability of stiffness, demonstrating that our system can provide accurate stiffness modulation with perceptually insignificant errors. The limitations of our AR system are also discussed along with a plan for future work.

Haptic augmented reality (AR) is an emerging research area, which targets the modulation of haptic properties of real objects by means of virtual feedback. In our research, we explore the feasibility of using this technology for medical training systems. As a possible demonstration example, we currently examine the use of augmentation in the context of breast tumor palpation. The key idea in our prototype system is to augment the real feedback of a silicone breast mock-up with simulated forces stemming from virtual tumors. In this paper, we introduce and evaluate the underlying algorithm to provide these force augmentations. This includes a method for the identification of the contact dynamics model via measurements on real sample objects. The performance of our augmentation is examined quantitatively as well as in a user study. Initial results show that the haptic feedback of indenting a real silicone tumor with a rod can be approximated reasonably well with our algorithm. The advantage of such an augmentation approach over physical training models is the ability to create a nearly infinite variety of palpable findings.

The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

We propose a new 2-step phase-shifting digital holographic optical encryption technique and analyze tolerance error for this cipher system. 2-step phase-shifting digital holograms are acquired by moving the PZT mirror with phase step of 0 or π/2 in the reference beam path of the Mach-Zehnder type interferometer. Digital hologram with the encrypted information is Fourier transform hologram and is recorded on CCD camera with 256 gray-level quantized intensities. The decryption performance of binary bit data and image data is analyzed by considering error factors. One of the most important errors is quantization error in detecting the digital hologram intensity on CCD. The more the number of quantization error pixels and the variation of gray-level increase, the more the number of error bits increases for decryption. Computer experiments show the results to be carried out encryption and decryption with the proposed method and the graph to analyze the tolerance of the quantization error in the system.