A high-performance and scalable multi-core aware software solution for network monitoring

In recent years, the need for high-performance network monitoring tools, which can cope with rapidly increasing network bandwidth, has become vital. A possible solution is to utilize the processing power of multi-core processors that nowadays are available as commercial-off-the-shelf (COTS) hardware. In this paper, we introduce a software solution for wire-speed packet capturing and transmission for TCP/IP networks under Linux operating system, called DashCap. The results of our experimental evaluations show that the proposed solution causes more than two times performance boost for packet capturing in comparison to the existing software solutions under Linux. We have proposed a scalable software architecture for network monitoring tools called DashNMon, which is based on DashCap. Multi-core awareness is a distinguished property of this architecture. Comparing to the existing cluster-based solutions, DashNMon can be used with COTS multi-core processors. In order to evaluate the proposed solutions, we have developed several prototype tools. The results of the experiments carried out using these tools show the scalability and high performance of the network monitoring tools that are based on the proposed architecture. Using the proposed architecture, it is possible to design and implement high-performance multi-threaded network intrusion detection systems (NIDSs) or application-layer firewalls, completely in the user space and with better utilization of the computational resources of multi-processor/multi-core systems.     

Selection of the optimum decomposition level using the discrete wavelet transform for automobile suspension system

Journal of Mechanical Science and Technology - This paper discusses the determination of the optimum decomposition level by using the discrete wavelet transform (DWT) method for an automobile...     

Energy efficiency optimization of one-way and two-way DF relaying considering circuit power

In this paper, the energy efficiency (EE) of a decode and forward (DF) relay system is studied, where two sources communicate through a half-duplex relay node in one-way and two-way relaying strategies. Both the circuitry power and the transmission power of all nodes are taken into consideration. In addition, three different coding schemes for two-way DF relaying strategy with two phases and two-way DF relaying with three phases are considered. The aim is to maximize the EE of the system for a constant spectral efficiency (SE). For this purpose, the transmission time and the transmission power of each node are optimized. Simulations are used to compare the EE–SE curve of different DF strategies with one-way and two-way amplify and forward (AF) strategies and direct transmission (DT), to find the best energy efficient strategy in different SE conditions. Analytical and simulation results demonstrate that in low SE conditions, DF relaying strategies are more energy efficient compared to that of AF strategies and DT. However, in high SE conditions, the EE of two-way AF relaying and DT strategy outperform some of the DF relaying strategies. In simulations, the impact of different circuitry power and different channel conditions on the EE–SE curves are also investigated.     

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.<>     

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.     

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.     

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.