Experimental Investigation and Thermodynamic Modeling of Glycerin/Methanol/Organic Solvent Systems

Glycerin is an important by‐product in biodiesel production. To increase its quality to be suitable for use it in other operations, e.g., the pharmaceutical industry, it needs to be purified. Therefore, the purification of glycerin by liquid‐liquid extraction of methanol using different solvents was investigated. It was shown that, in terms of separation, petroleum ether was more effective than toluene and toluene was more effective than n‐butanol. In addition to the experimental investigations, the NRTL and UNIQUAC thermodynamic models were used to predict the compositions of ternary mixtures of glycerin + methanol + organic solvent in glycerin‐rich and organic solvent‐rich phases. The results showed the high accuracy of the presented models and their consistency with the measured data.     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.     

Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains

Data envelopment analysis (DEA) is a method for measuring performance of decision making units (DMUs). Conventional DEA models view DMUs as black boxes. Network DEA (NDEA) models have been developed to overcome this shortfall. This paper develops a new NDEA model based on modified enhanced Russell measure model. This paper measures performance of humanitarian supply chains (HSCs) by an NDEA model. Capabilities of the proposed model are addressed by theorems. However, in the real world, there might be stochastic data. This paper presents a stochastic version of the proposed NDEA model to measure the performance of HSCs. We analyse main properties of our model. We present a case study to demonstrate the applicability of the proposed model.     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

Exercise classification and event segmentation in Hammersmith Infant Neurological Examination videos

Image and video processing techniques are being frequently used in medical science applications. Computer vision-based systems have successfully replaced various manual medical processes such as analyzing physical and biomechanical parameters, physical examination of patients. These systems are gaining popularity because of their robustness and the objectivity they bring to various medical procedures. Hammersmith Infant Neurological Examinations (HINE) is a set of physical tests that are carried out on infants in the age group of 3–24 months with neurological disorders. However, these tests are graded through visual observations, which can be highly subjective. Therefore, computer vision-aided approach can be used to assist the experts in the grading process. In this paper, we present a method of automatic exercise classification through visual analysis of the HINE videos recorded at hospitals. We have used scale-invariant-feature-transform features to generate a bag-of-words from the image frames of the video sequences. Frequency of these visual words is then used to classify the video sequences using HMM. We also present a method of event segmentation in long videos containing more than two exercises. Event segmentation coupled with a classifier can help in automatic indexing of long and continuous video sequences of the HINE set. Our proposed framework is a step forward in the process of automation of HINE tests through computer vision-based methods. We conducted tests on a dataset comprising of 70 HINE video sequences. It has been found that the proposed method can successfully classify exercises with accuracy as high as 84%. The proposed work has direct applications in automatic or semiautomatic analysis of “vertical suspension” and “ventral suspension” tests of HINE. Though some of the critical tests such as “pulled-to-sit,” “lateral tilting,” or “adductor’s angle measurement” have already been addressed using image- and video-guided techniques, scopes are there for further improvement.     

PSW statistical LSB image steganalysis

Multimedia Tools and Applications - Steganography is the art and science of producing covert communications by concealing secret messages in apparently innocent media, while steganalysis is the art...     

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...     

An efficient numerical algorithm for multi-dimensional time dependent partial differential equations

An efficient and robust numerical scheme based on Haar wavelets and finite differences is suggested for the solution of two-dimensional time dependent linear and nonlinear partial differential equations (PDEs). Excellent feature of the scheme is the conversion of linear and non-linear PDEs to algebraic equations which are comparatively easy to handle. Convergence of the scheme, which guarantees small error norm as the resolution level increases, is also an important part of this work. Different error norms are computed to check efficiency of the technique. Computations verify accuracy, flexibility and low computational cost of the method.     

BUILDING USER TASK SPACE BY MINING TEMPORALLY PROXIMATE DESKTOP ACTIONS

Awareness of the user's current context, that is, the task the user resides in, can allow for various ways of supporting their work. In this article we present a novel approach for mining user's regular tasks on the basis of temporal proximity of users’ desktop actions without user intervention in a completely automatic, unobtrusive, and unsupervised manner. The proposed method is based on time-based clustering of a user's desktop actions and, in contrast to previous approaches, it does not enforce fixed time constraints on the mined actions, and typical transition times, when they exist, emerge from the logged activity data for that specific user. The performance of our technique was evaluated on a large data set of 724 days of desktop work by five knowledge workers. The results showed that our approach performed very well and was able to cluster those temporally proximate user actions that commonly used fix time window techniques fail to deal with. Notably, the main improvement is in the recall of the tasks, where our approach recalled 10.18% more actions than the predefined time window technique.