Thermally Resistive Electrospun Composite Membranes for Low‐Grade Thermal Energy Harvesting

In this work, thermally insulating composite mats of poly(vinylidene fluoride) (PVDF) and polyacrylonitrile (PAN) blends are used as the separator membranes. The membranes improve the thermal‐to‐electrical energy conversion efficiency of a thermally driven electrochemical cell (i.e., thermocell) up to 95%. The justification of the improved performance is an intricate relationship between the porosity, electrolyte uptake, electrolyte uptake rate of the electrospun fibrous mat, and the actual temperature gradient at the electrode surface. When the porosity is too high (87%) in PAN membranes, the electrolyte uptake and electrolyte uptake rate are significantly high as 950% and 0.53 µL s^?1, respectively. In such a case, the convective heat flow within the cell is high and the power density is limited to 32.7 mW m^?2. When the porosity is lesser (up to 81%) in PVDF membranes, the electrolyte uptake and uptake rate are relatively low as 434% and 0.13 µL s^?1, respectively. In this case, the convective flow shall be low, however, the maximum power density of 63.5 mW m^?2 is obtained with PVDF/PAN composites as the aforementioned parameters are optimized. Furthermore, multilayered membrane structures are also investigated for which a bilayered architecture produces highest power density of 102.7 mW m^?2.     

KAFD Arabic font database

Font recognition is useful for improving optical text recognition systems’ accuracy and time, and to restore the documents’ original formats. This paper addresses a need for Arabic font recognition research by introducing an Arabic font recognition database consisting of 40 fonts, 10 sizes (ranging from 8 to 24 points) and 4 styles (viz. normal, bold, italic, and bold–italic). The database is split into three sets (viz. training, validation, and testing). The database is freely available to researchers.¹ Moreover, we introduce a baseline font recognition system for benchmarking purposes, and report identification rates on our KAFD database and the Arabic Printed Text Image (APTI) database with 20 and 10 fonts, respectively. The best recognition rates are achieved using log-Gabor filters.     

A statistical tolerance analysis approach for over-constrained mechanism based on optimization and Monte Carlo simulation

Tolerancing decisions can profoundly impact the quality and cost of the mechanism. To evaluate the impact of tolerance on mechanism quality, designers need to simulate the influences of tolerances with respect to the functional requirements. This paper proposes a mathematical formulation of tolerance analysis which integrates the notion of quantifier: “For allacceptable deviations (deviations which are inside tolerances),there existsa gap configurationsuch asthe assembly requirements and the behavior constraints are verified” & “For allacceptable deviations (deviations which are inside tolerances), andfor alladmissible gap configurations, the assembly and functional requirements and the behavior constraints are verified”. The quantifiers provide a univocal expression of the condition corresponding to a geometrical product requirement. This opens a wide area for research in tolerance analysis. To solve the mechanical problem, an approach based on optimization is proposed. Monte Carlo simulation is implemented for the statistical analysis. The proposed approach is tested on an over-constrained mechanism.     

FA–QABC–MRTA: a solution for solving the multi-robot task allocation problem

Intelligent Service Robotics - The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the...     

Optimize adaptive media playout using dynamic fuzzy logic control for video streaming

Multimedia Tools and Applications - Adaptive Media Playout (AMP) controls adapt playout rate to prevent buffer outage and to reduce delay in playout. Most AMP techniques use buffer fullness or its...     

KHATT: An open Arabic offline handwritten text database

A comprehensive Arabic handwritten text database is an essential resource for Arabic handwritten text recognition research. This is especially true due to the lack of such database for Arabic handwritten text. In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) consisting of 1000 handwritten forms written by 1000 distinct writers from different countries. The forms were scanned at 200, 300, and 600 dpi resolutions. The database contains 2000 randomly selected paragraphs from 46 sources, 2000 minimal text paragraph covering all the shapes of Arabic characters, and optionally written paragraphs on open subjects. The 2000 random text paragraphs consist of 9327 lines. The database forms were randomly divided into 70%, 15%, and 15% sets for training, testing, and verification, respectively. This enables researchers to use the database and compare their results. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. The verified ground truth database contains meta-data describing the written text at the page, paragraph, and line levels in text and XML formats. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. In addition we are presenting our experimental results on the database using two classifiers, viz. Hidden Markov Models (HMM) and our novel syntactic classifier.