Augmented reality annotations to assist persons with Alzheimers and their caregivers

Persons with Alzheimer’s disease (AD) and their caregivers implement diverse strategies to cope with memory loss. A common strategy involves placing tags on drawers or removing cabinet doors to make their contents visible. This study describes the Ambient aNnotation System (ANS), aimed at assisting people suffering from AD and their caregivers with this task. The system has two main modules: The tagging subsystem allows caregivers to create and manage ambient annotations in order to assist people with memory problems. The second subsystem allows people with AD to use a mobile phone to recognize tags in the environment and to receive relevant information in the form of audio, text, or images. The identification of these tags is performed in real time by uploading images from the mobile phone to a server, which uses the SURF algorithm for object recognition. We describe the design and implementation of the system as well as results of the evaluation of its performance and efficiency. ANS can process query images approximately every 2 s and is able to locate users in their homes with a precision of 0.93. A usability study conducted with six subjects determined that audio notifications are more effective than vibrating notifications to alert the user about tags in the environment.     

Growth factor delivery-based tissue engineering: general approaches and a review of recent developments

The identification and production of recombinant morphogens and growth factors that play key roles in tissue regeneration have generated much enthusiasm and numerous clinical trials, but the results of many of these trials have been largely disappointing. Interestingly, the trials that have shown benefit all contain a common denominator, the presence of a material carrier, suggesting strongly that spatio-temporal control over the location and bioactivity of factors after introduction into the body is crucial to achieve tangible therapeutic effect. Sophisticated materials systems that regulate the biological presentation of growth factors represent an attractive new generation of therapeutic agents for the treatment of a wide variety of diseases. This review provides an overview of growth factor delivery in tissue engineering. Certain fundamental issues and design strategies relevant to the material carriers that are being actively pursued to address specific technical objectives are discussed. Recent progress highlights the importance of materials science and engineering in growth factor delivery approaches to regenerative medicine.     

Light and Strong SiC Networks

The directional freezing of microfiber suspensions is used to assemble highly porous (porosities ranging between 92% and 98%) SiC networks. These networks exhibit a unique hierarchical architecture in which thin layers with honeycomb‐like structure and internal strut length in the order of 1–10 μm in size are aligned with an interlayer spacing ranging between 15 and 50 μm. The resulting structures exhibit strengths (up to 3 MPa) and stiffness (up to 0.3 GPa) that are higher than aerogels of similar density and comparable to other ceramic microlattices fabricated by vapor deposition. Furthermore, this wet processing technique allows the fabrication of large‐size samples that are stable at high temperature, with acoustic impedance that can be manipulated over one order of magnitude (0.03–0.3 MRayl), electrically conductive and with very low thermal conductivity. The approach can be extended to other ceramic materials and opens new opportunities for the fabrication of ultralight structures with unique mechanical and functional properties in practical dimensions.     

Recurrent pattern matching based stereo image coding using linear predictors

The Multidimensional Multiscale Parser (MMP) is a pattern-matching-based generic image encoding solution which has been investigated earlier for the compression of stereo images with successful results. While first MMP-based proposals for stereo image coding employed dictionary-based techniques for disparity compensation, posterior developments have demonstrated the advantage of using predictive methods. In this paper, we focus on recent investigations on the use of predictive methods in the MMP algorithm and propose a new prediction framework for efficient stereo image coding. This framework comprises an advanced intra directional prediction model and a new linear predictive scheme for efficient disparity compensation. The linear prediction model is the main novelty of this work, combining adaptive linear models estimated by least-squares algorithm with fixed linear models provided by the block-matching algorithm. The performance of the proposed intra prediction and disparity compensation methods when applied in an MMP encoder has been evaluated experimentally. Comparisons with the current stereo image coding standards showed that the proposed MMP algorithm significantly outperforms the Stereo High Profile of H.264/AVC standard. In addition, it presents a competitive performance relative to the MV-HEVC standard. These results also suggest that current stereo image coding standards may benefit from the proposed linear prediction scheme for disparity compensation, as an extension to the omnipresent block-matching solution.     

Amplification of Cerenkov Luminescence Using Semiconducting Polymers for Cancer Theranostics

The therapeutic efficacy of photodynamic therapy is limited by the ability of light to penetrate tissues. Due to this limitation, Cerenkov luminescence (CL) from radionuclides has recently been proposed as an alternative light source in a strategy referred to as Cerenkov radiation-induced therapy (CRIT). Semiconducting polymer nanoparticles (SPNs) have ideal optical properties, such as large absorption cross-sections and broad absorbance, which can be utilized to harness the relatively weak CL produced by radionuclides. SPNs can be doped with photosensitizers and have ≈100% energy transfer efficiency by multiple energy transfer mechanisms. Herein, an optimized photosensitizer-doped SPN is investigated as a nanosystem to harness and amplify CL for cancer theranostics. It is found that semiconducting polymers significantly amplify CL energy transfer efficiency. Bimodal positron emission tomography (PET) and optical imaging studies show high tumor uptake and retention of the optimized SPNs when administered intravenously or intratumorally. Lastly, it is found that photosensitizer-doped SPNs have excellent potential as a cancer theranostics nanosystem in an in vivo tumor therapy study. This study shows that SPNs are ideally suited to harness and amplify CL for cancer theranostics, which may provide a significant advancement for CRIT that are unabated by tissue penetration limits.     

CAC-OLSR: Extending OLSR to Provide Admission Control in Wireless Mesh Networks

This work presents an admission control mechanism for multi-hop wireless mesh networks based on the IEEE 802.11 standard and the OLSR routing protocol. This mechanism, called CAC-OLSR, aims at ensuring that traffic flows with quality of service (QoS) requirements, especially voice and video, are only admitted in the mesh network if it has available resources in order to provide flow requirements. In addition, QoS requirements of previously admitted traffic flows cannot be violated. The proposal was evaluated with NS-2 and Evalvid simulations.     

RF Power Amplifier Behavioral Modeling Based on Takenaka–Malmquist–Volterra Series

In this paper, a Takenaka–Malmquist–Volterra (TMV) model structure is employed to improve the approximations in the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs). The Takenaka–Malmquist basis generalizes the orthonormal basis functions previously used in this context. In addition, it allows each nonlinearity order in the expanded Volterra model to be parameterized by multiple complex poles (dynamics). The state-space realizations for the TMV models are introduced. The pole sets for the TMV model and also for the previous Laguerre–Volterra (LV) and Kautz–Volterra (KV) models are obtained using a constrained nonlinear optimization approach. Based on experimental data measured on a GaN HEMT class AB RF PA excited by a WCDMA signal, it is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity.     

A time Petri net-based method for embedded hard real-time software synthesis

Over the last few years, embedded software synthesis has drawn much attention. However, few works deal with software synthesis for hard real-time systems considering arbitrary inter-tasks precedence and exclusion relations. Code generation for meeting all timing and resource constraints is not a trivial task. Thus, this research area has several open issues, mainly related to generation of predictable-guaranteed scheduled code. The method proposed in this paper starts from a high-level specification, and automatically translates such specification into a time Petri net model; this model is adopted for finding a feasible static schedule meeting all constraints. If found, the approach generates a scheduled code, based on the found feasible schedule. Therefore, the user just enter the specification and receives, as result, the scheduled code. Thus, all intermediary phases are hidden from the users.