Robust weighted graph transformation matching for rigid and nonrigid image registration

This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. The algorithm starts with a set of matches (including outliers) between the two images. A set of nondirectional graphs is then generated for each feature and its K nearest matches (chosen from the initial set). Using the angular distances between edges that connect a feature point to its K nearest neighbors in the graph, the algorithm finds a graph in the second image that is similar to the first graph. In the case of a graph including outliers, the algorithm removes such outliers (one by one, according to their strength) from the graph and re-evaluates the angles until the two graphs are matched or discarded. This is a simple intuitive and robust algorithm that is inspired by a previous work. Experimental results demonstrate the superior performance of this algorithm under various conditions, such as rigid and nonrigid transformations, ambiguity due to partial occlusions or match correspondence multiplicity, scale, and larger view variation.     

Characteristics of p-Ge/N-GaAs heterojunctions grown by molecular beam epitaxy

Reports the nearly ideal electrical characteristics of p-Ge/N-GaAs heterojunction diodes grown by molecular beam epitaxy. The temperature and device size dependencies of the current-voltage characteristics have been investigated. At room temperature, unity ideality factor has been demonstrated over almost six decades of current.<>     

Channel estimation based on learning automata for OFDM systems

The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.     

Advent of 2D Rhenium Disulfide (ReS2): Fundamentals to Applications

Rhenium disulfide (ReS₂) is a two‐dimensional (2D) group VII transition metal dichalcogenide (TMD). It is attributed with structural and vibrational anisotropy, layer‐independent electrical and optical properties, and metal‐free magnetism properties. These properties are unusual compared with more widely used group VI‐TMDs, e.g., MoS₂, MoSe₂, WS₂ and WSe₂. Consequently, it has attracted significant interest in recent years and is now being used for a variety of applications including solid state electronics, catalysis, and, energy harvesting and energy storage. It is anticipated that ReS₂ has the potential to be equally used in parallel with isotropic TMDs from group VI for all known applications and beyond. Therefore, a review on ReS₂ is very timely. In this first review on ReS₂, we critically analyze the available synthesis procedures and their pros/cons, atomic structure and lattice symmetry, crystal structure, and growth mechanisms with an insight into the orientation and architecture of domain and grain boundaries, decoupling of structural and vibrational properties, anisotropic electrical, optical, and magnetic properties impacted by crystal imperfections, doping and adatoms adsorptions, and contemporary applications in different areas.     

Encapsulation of Nanoparticles Using Nitrilotriacetic Acid End‐Functionalized Polystyrenes and Their Application for the Separation of Proteins

The use of nitrilotriacetic acid end‐functionalized polystyrenes (NTA‐PS) as a multifunctional nanocarrier for the aqueous dispersion of CdSe, γ‐Fe₂O₃ and gold nanoparticles (NPs) is described. When the amphiphilic end‐ functionalized polystyrenes and NPs are dissolved together in tetrahydrofuran, the addition of water causes the spontaneous formation of micellar aggregates, resulting in the successful encapsulation and aqueous dispersion of NPs. Transmission electron microscopy (TEM), scanning electron microscopy (SEM), photoluminescence (PL) spectroscopy, and vibrating sample magnetometer (VSM) are used to characterize the structure and properties of the NPs‐containing micellar aggregates (nanocarrier). After complexation of Ni²⁺ with NTA on the surface of the nanocarrier containing γ‐Fe₂O₃, specific binding between Ni‐NTA complex and histidine‐tagged (His‐tagged) proteins enables selective separation of His‐tagged proteins using a magnet.     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Constraining Si Particles within Graphene Foam Monolith: Interfacial Modification for High‐Performance Li+ Storage and Flexible Integrated Configuration

Pulverization of electrode materials and loss of electrical contact have been identified as the major causes for the performance deterioration of alloy anodes in Li‐ion batteries. This study presents the hierarchical arrangement of spatially confining silicon nanoparticles (Si NPs) within graphene foam (GF) for alleviating these issues. Through a freeze‐drying method, the highly oriented GF monolith is engineered to fully encapsulate the Si NPs, serving not only as a robust framework with the well‐accessible thoroughfares for electrolyte percolation but also a physical blocking layer to restrain Si from direct exposure to the electrolyte. In return, the pillar effect of Si NPs prevents the graphene sheets from restacking while preserving the highly efficient electron/Li⁺ transport channels. When evaluated as a binder‐free anode, impressive cycle performance is realized in both half‐cell and full‐cell configurations. Operando X‐ray diffraction and in‐house X‐ray photoelectron spectroscopy confirm the pivotal protection of GF to sheathe the most volume‐expanded lithiated phase (Li₁₅Si₄) at room temperature. Furthermore, a free‐standing composite film is developed through readjusting the pore size in GF/Si monolith and directly integrated with nanocellulose membrane (NCM) separator. Because of the good electrical conductivity and structural integrity of the GF monolith as well as the flexibility of the NCM separator, the as‐developed GF/Si‐NCM electrode showcases the potential use in the flexible electronic devices.     

Anti‐Oxygen Leaking LiCoO2

LiCoO₂ is a prime example of widely used cathodes that suffer from the structural/thermal instability issues that lead to the release of their lattice oxygen under nonequilibrium conditions and safety concerns in Li‐ion batteries. Here, it is shown that an atomically thin layer of reduced graphene oxide can suppress oxygen release from Li_xCoO₂ particles and improve their structural stability. Electrochemical cycling, differential electrochemical mass spectroscopy, differential scanning calorimetry, and in situ heating transmission electron microscopy are performed to characterize the effectiveness of the graphene‐coating on the abusive tolerance of Li_xCoO₂. Electrochemical cycling mass spectroscopy results suggest that oxygen release is hindered at high cutoff voltage cycling when the cathode is coated with reduced graphene oxide. Thermal analysis, in situ heating transmission electron microscopy, and electron energy loss spectroscopy results show that the reduction of Co species from the graphene‐coated samples is delayed when compared with bare cathodes. Finally, density functional theory and ab initio molecular dynamics calculations show that the rGO layers could suppress O₂ formation more effectively due to the strong C? O_cathode bond formation at the interface of rGO/LCO where low coordination oxygens exist. This investigation uncovers a reliable approach for hindering the oxygen release reaction and improving the thermal stability of battery cathodes.     

Unraveling the Reasons for Efficiency Loss in Perovskite Solar Cells

The effect of the presence of unreacted PbI₂ on the perovskite solar cells efficiency is reported. N,N‐Dimethylformamide vapor treatment is introduced to study the influence of complete conversion to a power conversion efficiency of the device. It is discovered that the optimized morphology of the PbI₂ under layer is essential to form a dense perovskite layer preventing recombination by direct contact between TiO₂ and a hole transporting layer, and to increase the charge collection efficiency. The present findings provide an insight into the morphology and growth mechanism of perovskite layer, the correlation between the device performance, and the film deposition process.     

Development,In-Vitro Characterization and Preclinical Evaluation of Esomeprazole-Encapsulated Proniosomal Formulation for the Enhancement of Anti-Ulcer Activity

Objective: The present study aimed to develop and optimize esomeprazole loaded proniosomes (EZL-PNs) to improve bioavailability and therapeutic efficacy. Method: EZL-PNs formulation was developed by slurry method and optimized by 33 box-Bhekhen statistical design software. Span 60 (surfactant), cholesterol, EZL concentration were taken as independent variables and their effects were evaluated on vesicle size (nm), entrapment efficiency (%, EE) and drug release (%, DR). Furthermore, optimized EZL-PNs (EZL-PNs-opt) formulation was evaluated for ex vivo permeation, pharmacokinetic and ulcer protection activity. Result: The EZL-PNs-opt formulation showed 616 ± 13.21 nm of vesicle size, and 81.21 ± 2.35% of EE. EZL-PNs-opt exhibited negative zeta potential and spherical confirmed scanning electron microscopy. EZL-PNs-opt showed sustained release of EZL (95.07 ± 2.10% in 12 h) than pure EZL dispersion. The ex-vivo gut permeation result exhibited a significantly (p < 0.05) enhanced flux than pure EZL. The in vivo results revealed 4.02-fold enhancement in bioavailability and 61.65% protection in ulcer than pure EZL dispersion (43.82%). Conclusion: Our findings revealed that EZL-PNs formulation could be an alternative delivery system of EZL to enhance oral bioavailability and antiulcer activity.