Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

CuPc based organic--inorganic hetero-junction with Au electrodes

In present work, a hetero-junction of n-silicon (n-Si) with copper phthalocyanine (CuPc) has been fabricated. The current-voltage characteristics were investigated to explain the rectification and conduction mechanism. The effect of temperature and humidity on electrical properties of n-Si/CuPc hetero-junction has also been investigated. The characteristics of the junction have been observed to be temperature and humidity dependent so it is suggested that this junction can be used as temperature and humidity sensor.     

Zr‐Doped Indium Oxide (IZRO) Transparent Electrodes for Perovskite‐Based Tandem Solar Cells

Parasitic absorption in transparent electrodes is one of the main roadblocks to enabling power conversion efficiencies (PCEs) for perovskite‐based tandem solar cells beyond 30%. To reduce such losses and maximize light coupling, the broadband transparency of such electrodes should be improved, especially at the front of the device. Here, the excellent properties of Zr‐doped indium oxide (IZRO) transparent electrodes for such applications, with improved near‐infrared (NIR) response, compared to conventional tin‐doped indium oxide (ITO) electrodes, are shown. Optimized IZRO films feature a very high electron mobility (up to ≈77 cm² V^?1 s^?1), enabling highly infrared transparent films with a very low sheet resistance (≈18 Ω □^?1 for annealed 100 nm films). For devices, this translates in a parasitic absorption of only ≈5% for IZRO within the solar spectrum (250–2500 nm range), to be compared with ≈10% for commercial ITO. Fundamentally, it is found that the high conductivity of annealed IZRO films is directly linked to promoted crystallinity of the indium oxide (In₂O₃) films due to Zr‐doping. Overall, on a four‐terminal perovskite/silicon tandem device level, an absolute 3.5 mA cm^?2 short‐circuit current improvement in silicon bottom cells is obtained by replacing commercial ITO electrodes with IZRO, resulting in improving the PCE from 23.3% to 26.2%.     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio

Aggregates are the biggest contributor to concrete volume and are a crucial parameter in dictating its mechanical properties. As such, a detailed experimental investigation was carried out to evaluate the effect of sand-to-aggregate volume ratio (s/a) on the mechanical properties of concrete utilizing both destructive and non-destructive testing (employing UPV (ultrasonic pulse velocity) measurements). For investigation, standard cylindrical concrete samples were made with different s/a (0.36, 0.40, 0.44, 0.48, 0.52, and 0.56), cement content (340 and 450 kg/m³), water-to-cement ratio (0.45 and 0.50), and maximum aggregate size (12 and 19 mm). The effect of these design parameters on the 7, 14, and 28 d compressive strength, tensile strength, elastic modulus, and UPV of concrete were assessed. The careful analysis demonstrates that aggregate proportions and size need to be optimized for formulating mix designs; optimum ratios of s/a were found to be 0.40 and 0.44 for the maximum aggregate size of 12 and 19 mm, respectively, irrespective of the W/C (water-to-cement) and cement content.     

Gas Sensing of NiO‐SCCNT Core–Shell Heterostructures: Optimization by Radial Modulation of the Hole‐Accumulation Layer

Hierarchical core–shell (C–S) heterostructures composed of a NiO shell deposited onto stacked‐cup carbon nanotubes (SCCNTs) are synthesized by atomic layer deposition (ALD). A film of NiO particles (0.80–21.8 nm in thickness) is uniformly deposited onto the inner and outer walls of the SCCNTs. The electrical resistance of the samples is found to increase of many orders of magnitude with the increasing of the NiO thickness. The response of NiO–SCCNT sensors toward low concentrations of acetone and ethanol at 200 °C is studied. The sensing mechanism is based on the modulation of the hole‐accumulation region in the NiO shell layer upon chemisorption of the reducing gas molecules. The electrical conduction mechanism is further studied by the incorporation of an Al₂O₃ dielectric layer at NiO and SCCNT interfaces. The investigations on NiO–Al₂O₃–SCCNT, Al₂O₃–SCCNT, and NiO–SCCNT coaxial heterostructures reveal that the sensing mechanism is strictly related to the NiO shell layer. The remarkable performance of the NiO–SCCNT sensors toward acetone and ethanol benefits from the conformal coating by ALD, large surface area of the SCCNTs, and the optimized p‐NiO shell layer thickness followed by the radial modulation of the space‐charge region.     

Bio-cellular processes modeling on silicon substrate: receptor–ligand binding and Michaelis Menten reaction

Reducing transmit power is the most straightforward way towards more energy-efficient communications, but it results in lower SNRs at the receiver which can add a performance and/or complexity cost. At low SNRs, synchronization and channel estimation errors erode much of the gains achieved through powerful turbo and LDPC codes. Further expanding the turbo concept through an iterative receiver—which brings synchronization and equalization modules inside the loop—can help, but this solution is prohibitively complex and it is not clear what can and what cannot be a part of the iterative structure. This paper fills two important gaps in this field: (1) as compared to previous research which either focuses on a subset of the problem assuming perfect remaining parameters or is computationally too complex, we propose a proper partitioning of algorithm blocks in the iterative receiver for manageable delay and complexity, and (2) to the best of our knowledge, this is the first physical demonstration of an iterative receiver based on experimental radio hardware. We have found that for such a receiver to work, (1) iterative timing synchronization is impractical, iterative carrier synchronization can be avoided by using our proposed approach, while iterative channel estimation is essential, and (2) the SNR gains claimed in previous publications are validated in indoor channels. Finally, we propose a heuristic algorithm for simplifying the carrier phase synchronization in an iterative receiver such that computations of the log likelihood ratios of the parity bits can be avoided to strike a tradeoff between complexity and performance.     

Discovery of Five New Ethylene-Forming Enzymes for Clean Production of Ethylene in E. coli

Ethylene is an essential platform chemical with a conjugated double bond, which can produce many secondary chemical products through copolymerisation. At present, ethylene production is mainly from petroleum fractionation and cracking, which are unsustainable in the long term, and harmful to our environment. Therefore, a hot research field is seeking a cleaner method for ethylene production. Based on the model ethylene-forming enzyme (Efe) {"type":"entrez-protein","attrs":{"text":"AAD16440.1","term_id":"4323597","term_text":"AAD16440.1"}}AAD16440.1 (6vp4.1.A) from Pseudomonas syringae pv. phaseolicol, we evaluated five putative Efe protein sequences using the data derived from phylogenetic analyses and the conservation of their catalytic structures. Then, pBAD expression frameworks were constructed, and relevant enzymes were expressed in E. coli BL21. Finally, enzymatic activity in vitro and in vivo was detected to demonstrate their catalytic activity. Our results show that the activity in vitro measured by the conversion of α-ketoglutarate was from 0.21–0.72 μmol ethylene/mg/min, which varied across the temperatures. In cells, the activity of the new Efes was 12.28–147.43 μmol/gDCW/h (DCW, dry cellular weight). Both results prove that all the five putative Efes could produce ethylene.