A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.     

A novel rectifying circuit for microwave power harvesting system

Many researchers continue to look for novel means to harvest energy and generate power using existing sources thus eradicating the need of batteries and power supplies which only add up to the cost of the system. A rectifier is the main component in energy harvesting circuits as it converts RF energy from available sources to DC power. This article aimed at developing a novel rectifying circuit to be used in harvesting applications at a frequency of 900 MHz. Besides, it is also aimed to enhance the output voltage of Cockcroft Walton rectifier circuit and Dickson rectifier. The design is built by integrating the Cockcroft Walton rectifier, arranged in series, with the Dickson rectifier, arranged in parallel. A Schottky diode HSMS 285C is selected for designing the rectifiers circuit. A simulation of the circuit was optimized utilizing ADS 2009. The rectifiers had been fabricated on an RT/Duroid 5880 (RO5880) printed circuit board (PCB) substrate with a dielectric constant and loss tangent of 2.2 and 0.0009, respectively. Simulation and experimental results show great output thresholds for the three models. The novel rectifier had the highest efficiency and output voltage.     

Growth and characterization of pure and cadmium dichloride doped aminoethanoic acid (AEA) single crystals

Organic and semiorganic centrosymmetry single crystals of pure and cadmium dichloride doped aminoethanoic acid was successfully grown by slow evaporation solution growth technique. The solubility of the grown crystals was studied for different temperatures upto 50 °C. The single crystal X-ray diffraction analysis shows that the grown crystal belongs to monoclinic system and their unit cell parameter values were determined. The presence of various functional groups was identified by Fourier transform infrared spectroscopy. UV–Vis spectrum shows a wide optical transmittance in the visible region and the obtained lower cut off wavelength is around 252 nm. The presence of 0.31 mg of cadmium element per litre of water was measured by AAS analysis which indicates the effect of dopant in the material. TG/DTA study reveals that the organic complex has slightly more stable than the semiorganic complex and it has high thermal stability of about 193 °C. Vickers microhardness tester confirms the reverse indentation size effect of the crystals and found that the crystals belongs to soft material category by Meyer law. Dielectric study was carried out on the grown crystals which represent the lower value of dielectric constant while increase the frequency.     

Progressive failure monitoring and analysis in aluminium by in situ nondestructive evaluation

Damage initiation and progression in precipitate hardened alloys are typically linked to the failure of second phase particles that result from the precipitation process. These particles have been shown to be stress concentrators and crack starters as a result of both particle debonding and fracture. In this investigation, a precipitate hardened aluminium alloy (Al 2024‐T3) is loaded monotonically to investigate the role the particles have in the progressive failure process. The damage process was monitored continuously by combining the acoustic emission method either with in situ scanning electron microscopy or X‐ray microcomputed tomography to obtain both surface and volume microstructural information. Particles were observed to fracture only in the elastic regime of the material response, while void growth at locations predominantly near particles were found to be associated with progressive failure in the plastic region of the macroscopic response. Experimental findings were validated by fracture simulations at the scale of particle‐matrix interface.     

A novel wet-spinning method of manufacturing continuous bio-inspired composites based on graphene oxide and sodium alginate

Nacre is a lightweight, strong, stiff, and tough material, which makes it a mimicking object for material design. Many attempts to mimic nacre by various methods resulted in the synthesis of artificial nacre with excellent properties. However, the fabrication procedure was very laborious and time-consuming due to the sequential steps, and only limited-sized materials could be obtained. Hence, a novel design enabling scalable production of high-performance artificial nacre with uniform layered structures is urgently needed. We developed a novel wet-spinning assembly technique to rapidly manufacture continuous nacremimic graphene oxide (GO, brick)-sodium alginate (SA, mortar) films and fibers with excellent mechanical properties. At high concentrations, the GO-SA mixtures spontaneously produced liquid crystals (LCs) due to the template effect of GO, and continuous, 6 m long nacre-like GO-SA films were wet-spun from the obtained GO-SA liquid crystalline (LC) dope with a speed of up to 1.5 m/min. The assembled macroscopic GO-SA composites inherited the alignment of the GO sheets from the LC phase, and their mechanical properties were investigated by a joint experimental-computational study. The tensile tests revealed that the maximum strength (σ) and Young’s modulus (E) of the obtained films reached 239.6 MPa and 22.4 GPa, while the maximum values of σ and E for the fibers were 784.9 MPa and 58 GPa, respectively. The described wet-spinning assembly method is applicable for a large-scale and fast production of high-performance continuous artificial nacre.

Open image in new window

     

A Comprehensive Review on Automotive Antennas for Short Range Radar Communications

Wireless Personal Communications - Epilepsy, a globally growing chronic nervous disorder, affects the lives of millions of patients annually through the abrupt occurrence of recurrent seizures. It...     

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.     

Performance analysis of IEEE 802.11ah wireless local area network under the restricted access window‐based mechanism

IEEE 802.11ah is an approved amendment to IEEE 802.11 wireless local area network (WLAN) standard to support growing demand for machine‐to‐machine (M2M) applications. To enable an efficient scheme for accessing the channel by a large number of stations (STAs) within the coverage of an access point, 802.11ah has developed a novel mechanism known as Restrictred Access Window (RAW). Here, a group of STAs attempts channel access during their designated RAW slot by following the carrier sense multiple access‐collission avoidance (CSMA‐CA)‐based enhanced distributed channel access (EDCA) mechanism. In this paper, we develop an analytical model for finding the nonsaturation/saturation throughput of 802.11ah WLAN under the RAW‐based channel access mechanism. We describe an analytical model for finding the average frame delay as well. The impact of the number of STAs and number of groups on network throughput and average delay are presented. We establish that the RAW mechanism can improve the throughput while the average frame delay gets reduced. The analytical results are validated by extensive simulation studies.     

Digital document analytics using logistic regressive and deep transition-based dependency parsing

The selection of text features is a fundamental task and plays an important role in digital document analysis. Conventional methods in text feature extraction necessitate indigenous features. Obtaining an efficient feature is an extensive process, but a new and real-time representation of features in text data is a challenging task. Deep learning is making inroads in digital document mining. A significant distinction between deep learning and traditional methods is that deep learning learns features in a digital document in an automatic manner. In this paper, logistic regression and deep dependency parsing (LR-DDP) methods are proposed. The logistic regression token generation model generates robust tokens by means of Napierian grammar. With the robust generated tokens, a deep transition-based dependency parsing using duplex long short-term memory is designed. Experimental results demonstrate that our dependency parser achieves comparable performance in terms of digital document parsing accuracy, parsing time and overhead when compared to existing methods. Hence, these methods are found to be computationally efficient and accurate.

     

Automating factory performance diagnostics using overall throughput effectiveness (OTE) metric

The overall equipment effectiveness (OEE) metric is a powerful tool that can be used to measure performance and also perform diagnostics at the equipment level. Although important, gains made in OEE are insufficient; because the ultimate objective is a highly efficient integrated system, not brilliant individual equipment. Factory level performance monitoring and diagnostics can be facilitated by classifying the entire manufacturing system layout into unique subsystems including “series,” “parallel,” “assembly,” and “expansion.” The overall throughput effectiveness (OTE) metric is then derived for each of these subsystems. Factory level metric can be computed by synthesizing the subsystem level metrics, capturing their interconnectivity information. The objective of this paper is to present the algorithms developed to automate the factory-level performance monitoring and diagnostics process using OTE. The algorithms are implemented into a software tool called SIMPRO. SIMPRO allows factory professionals to efficiently measure performance and conduct factory level diagnostics (i.e., identify bottlenecks/hidden capacity). A glass manufacturing case study is used to illustrate automated performance diagnostics and the benefits obtained by using the approach.