Wake-up receivers for wireless sensor networks: benefits and challenges - [Accepted from open call]

For successful data delivery, the destination nodes should be listening to the medium to receive data when the sender node starts data communication. To achieve this synchronization, there are different rendezvous schemes, among which the most energy-efficient is utilizing wakeup receivers. Current hardware technologies of wake-up receivers enable us to evaluate them as a promising solution for wireless sensor networks. In this article the benefits achieved with wake-up receivers are investigated along with the challenges observed. In addition, an overview of state-of-the-art hardware and networking protocol proposals is presented. As wake-up receivers offer new opportunities, new potential application areas are also presented and discussed.     

Equivalent circuit for capacitive micromachined ultrasonic transducers to predict anti-resonances

Equivalent circuit models have been long used to evaluate the dynamics of the capacitive micromachined ultrasonic transducer (CMUT). An important parameter in the characterization of a CMUT is the anti-resonance frequency, which limits the immersion bandwidth. However, there is no equivalent circuit model that can accurately determine the anti-resonance frequency of a membrane. In this work, we present an improved lumped element parametric model for immersed CMUT. We demonstrate that the proposed equivalent circuit model accurately predicts anti-resonance and higher order mode frequencies, in addition to that of the fundamental mode. The proposed circuit model is in good agreement with device characteristics calculated using the finite element method and experimentally measured data.

     

Model-based ideal testing of hardware description language (HDL) programs

Software and Systems Modeling - An ideal test is supposed to show not only the presence of bugs but also their absence. Based on the Fundamental Test Theory of Goodenough and Gerhart (IEEE Trans...     

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.

     

Interplay of Particle Shape and Surface Roughness to Reach Maximum Flotation Efficiencies Depending on Collector Concentration

Particle–particle and bubble–particle-interactions in flotation systems are governed by physico-chemical and hydrodynamic conditions of pulp. Shape factor and roughness of particles significantly affect these interactions, and hence both grade and recovery in flotation. Although many studies have been conducted to understand morphological features of particles, the underlying mechanism of their effect on flotation recovery have not been clearly shown. Towards this aim, acombination of grinding and abrasion processes was applied to mimic grinding in terms of shape and roughness in order to get their corresponding flotation recoveries at different collector levels. For this purpose, glass beads representing smooth spherical particles of –150+106 µm in size along with ground and abraded glass particles of different shapes and roughness were used to evaluate the flotation efficiency of these particles in the absence and presence of amine collector. The dependence of the shape and roughness on the flotation recoveries at different hydrophobicities as monitored by different amine collector concentrations is demonstrated. Finally, the results are discussed to see if morphology ofparticles can be tuned through grinding to achieve maximum flotation efficiencies.     

Mechanical properties of bovine hydroxyapatite (BHA) composites doped with SiO<Subscript>2</Subscript>, MgO,Al<Subscript>2</Subscript>O<Subscript>3</Subscript>, and ZrO<Subscript>2</Subscript>

Biologically derived hydroxyapatite from calcinated (at 850 °C) bovine bones (BHA) was doped with 5 wt% and 10 wt% of SiO₂, MgO, Al₂O₃ and ZrO₂ (stabilized with 8% Y₂O₃). The aim was to improve the sintering ability and the mechanical properties (compression strength and hardness) of the resultant BHA-composites. Cylindrical samples were sintered at several temperatures between 1,000 and 1,300 °C for 4 h in air. The experimental results showed that sintering generally occurs at 1,200 °C. The BHA–MgO composites showed the best sintering performance. In the BHA–SiO₂ composites, extended formation of glassy phase occurred at 1,300 °C, resulting in structural degradation of the resultant samples. No sound reinforcement was achieved in the case of doping with Al₂O₃ and zirconia probably due to the big gap between the optimum sintering temperatures of BHA and these two oxides.     

Porous medium based burner for efficient and clean combustion of ammonia–hydrogen–air systems

Due to its high hydrogen density and extensive experience base, ammonia (NH₃) has been gaining special attention as a potential green energy carrier. This study focuses on premixed ammonia–hydrogen–air flames under standard temperature and pressure conditions using an inert silicon-carbide (SiC) porous block as a practical and effective medium for flame stabilization. Combustion experiments conducted using a lab scale burner resulted in stable combustion and high combustion efficiencies at very high ammonia concentration levels over a wide range of equivalence ratios. Noticeable power output densities have also been achieved. Preliminary results of NO_x emission measurements indicate NO_x concentrations as low as 35 ppm under rich conditions. The remarkable capability of this specific burner to operate efficiently and cleanly at high ammonia concentration levels, which can easily be achieved by partial cracking of NH₃, is believed to be a key accomplishment in the development of ammonia fired power generation systems.