Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

The need for suitable and cost-effective technologies rise with the growth of the internet of things (IoT) applications. These aim at handling voluminous data transmission in addition to minimum energy and latency cost constraints. LoRa networks are recommended for applications in confined spaces, long ranges, and less battery consumption requirements. However, the end devices in these networks communicate to all gateways in their ranges, thereby expediting energy unproductively in redundant transmissions. In our article, we explore the possibilities of whether LoRa networks could employ the advantages of clustering and propose two algorithms, path-based and data-centric, for such networks. We suggest that LoRaWAN technology with clustering can be apt for long-range, low power consumption IoT applications in the future. We study the impact of network density, node range, and cluster range on the energy consumption in data transmissions. The algorithms are compared with the inherent star-based communication of LoRa networks based on energy consumed, and our results show that, for dense deployments, clustering becomes advantageous.

     

Dynamic estimation of the modeling error statistics in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is a non-invasive imaging technique that suffers from a typical large-scale and ill-posed inverse problem with low spatial resolution. In DOT, the inverse problem is computationally intensive and decreasing the computation complexity and making it well-posed is the one of the most challenging research areas. More precisely, one of the well-known complexity reduction techniques is defined as applying modelling error originated from discretization of forward problem. Applying the discretization error in Bayesian inference has already been discussed; the method in which the likelihood is modified by an off-line prior density estimation. This paper implements a new method to enhance the modelling error approach using an iterative scheme to update statistical parameters of modelling discrepancy in DOT. The algorithm is very similar to Ensemble Kalman Filter. Moreover, the reconstruction process in the applied method is conducted by a small sample size rather than off-line method. Hence, the computation complexity is decreased and the algorithm converges in few iterations. The efficiency of the proposed method is illustrated by simulations.     

Preemptive cloud resource allocation modeling of processing jobs

Cloud computing allows execution and deployment of different types of applications such as interactive databases or web-based services which require distinctive types of resources. These applications lease cloud resources for a considerably long period and usually occupy various resources to maintain a high quality of service (QoS) factor. On the other hand, general big data batch processing workloads are less QoS-sensitive and require massively parallel cloud resources for short period. Despite the elasticity feature of cloud computing, fine-scale characteristics of cloud-based applications may cause temporal low resource utilization in the cloud computing systems, while process-intensive highly utilized workload suffers from performance issues. Therefore, ability of utilization efficient scheduling of heterogeneous workload is one challenging issue for cloud owners. In this paper, addressing the heterogeneity issue impact on low utilization of cloud computing system, conjunct resource allocation scheme of cloud applications and processing jobs is presented to enhance the cloud utilization. The main idea behind this paper is to apply processing jobs and cloud applications jointly in a preemptive way. However, utilization efficient resource allocation requires exact modeling of workloads. So, first, a novel methodology to model the processing jobs and other cloud applications is proposed. Such jobs are modeled as a collection of parallel and sequential tasks in a Markovian process. This enables us to analyze and calculate the efficient resources required to serve the tasks. The next step makes use of the proposed model to develop a preemptive scheduling algorithm for the processing jobs in order to improve resource utilization and its associated costs in the cloud computing system. Accordingly, a preemption-based resource allocation architecture is proposed to effectively and efficiently utilize the idle reserved resources for the processing jobs in the cloud paradigms. Then, performance metrics such as service time for the processing jobs are investigated. The accuracy of the proposed analytical model and scheduling analysis is verified through simulations and experimental results. The simulation and experimental results also shed light on the achievable QoS level for the preemptively allocated processing jobs.     

Probabilistic design of ground improvement by vertical drains for soil of spatially variable coefficient of consolidation

The design of soil consolidation via prefabricated vertical drains (PVDs) has been traditionally carried out deterministically and thus can be misleading due to the ignorance of the uncertainty associated with the inherent variability of soil properties. To treat such uncertainty in the course of design of soil improvement by PVDs, more rational probabilistic methods are necessary. In this paper, a simplified probabilistic method is proposed in which the inherent variability of the coefficient of consolidation, which is the most significant uncertain soil parameter that affects the consolidation process, is considered. An easy-to-use design procedure and charts are provided for routine use by practitioners.     

Morphology and crystallization behavior of nylon 6‐clay/neat nylon 6 bicomponent nanocomposite fibers

Nylon 6‐clay hybrid/neat nylon 6, sheath/core bicomponent nanocomposite fibers containing 4 wt % of clay in sheath section, were melt spun at different take‐up speeds. Their molecular orientation and crystalline structure were compared to those of neat nylon 6 fibers. Moreover, the morphology of the bicomponent fibers and dispersion of clay within the fibers were analyzed using scanning electron microscopy and transmission electron microscopy (TEM), respectively. Birefringence measurements showed that the orientation development in sheath part was reasonably high while core part showed negligibly low birefringence. Results of differential scanning calorimetry showed that crystallinity of bicomponent fibers was lower than that of neat nylon 6 fibers. The peaks of γ‐crystalline form were observed in the wide‐angle X‐ray diffraction of bicomponent and neat nylon 6 fibers in the whole take‐up speed, while α‐crystalline form started to appear at high speeds in bicomponent fibers. TEM micrographs revealed that the clay platelets were individually and evenly dispersed in the nylon 6 matrix. The neat nylon 6 fibers had a smooth surface while striped pattern was observed on the surface of bicomponent fibers containing clay. This was speculated to be due to thermal shrinkage of the core part after solidification of the sheath part in the spin‐line. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2014 , 131, 39996.     

Supplemental Conjugated Linoleic Acid Consumption Does Not Influence Milk Macronutrient Contents in all Healthy Lactating Women

The term “conjugated linoleic acid” (CLA) refers to a group of positional and geometric isomers that are derived from linoleic acid and are found primarily in meat and milk products from ruminant animals. Due to the array of putative benefits associated with various forms of CLA, there has been recent interest in supplementing human diets with these fatty acids especially when weight loss is desired. However, in many animal models, CLA has been shown to decrease milk fat production. There is some concern, therefore, that maternal CLA supplementation during lactation might inadvertently decrease nutrient supply to the nursing infant. However, there is only limited research on the effect of CLA consumption on milk fat content in women. Based on previously published work from our laboratory, we hypothesized that CLA supplementation would reduce the milk fat percentage in lactating women in a dose-dependent manner. Breastfeeding women (n = 12) were assigned randomly to treatments of 4 g/day safflower oil (SFO), 2 g/day CLA plus 2 g/day SFO, or 4 g/day CLA in a double blind, 3 × 3 Latin square design. Conjugated linoleic acid supplements contained approximately equal amounts of cis9,trans11–18:2 and trans10,cis12–18:2; the two most common isoforms of CLA. Milk was collected by complete breast expression on the last day (day 5) of each intervention period and analyzed for macronutrient and fatty acid composition. On day 4 of each intervention period, infant milk consumption was estimated by 24 h weighing of the infant. Washout periods were 9 days in length. We observed a dose-dependent increase in the concentrations of cis9,trans11–18:2 and trans10,cis12–18:2 in the milk fat. However, we detected neither a change in overall macronutrient composition nor infant milk consumption. These data do not support those obtained from animal models or our previous human work suggesting that consumption of CLA mixtures necessarily reduces milk fat. It is possible that either (1) the interpretation of our previously published data should be reevaluated, and/or (2) there are important intra- and inter-species differences in this regard.     

Consumption of c9,t11-18:2 or t10,c12-18:2 enriched dietary supplements does not influence milk macronutrients in healthy, lactating women

Substantial research suggests that the t10,c12–18:2, but not the c9,t11–18:2, isomer of conjugated linoleic acid (CLA) reduces milk fat synthesis in lactating bovine and rodent species. Because fat is the major energy-yielding component in human milk, we were interested in whether this is true for women as well. Thus, the effects of c9,t11–18:2 and t10,c12–18:2 on milk fat were examined in breast-feeding women (n = 12) in a double-blind, placebo-controlled, crossover study with latin-square design. The study was divided into six periods: baseline (3 days), three intervention periods (5 days each), and two washout periods (9 days each). During each intervention period, women consumed 750 mg/day of a supplement containing predominantly c9,t11–18:2, t10,c12–18:2, or 18:1 (olive oil placebo). Milk was collected by complete breast expression on the final day of each period. Infant milk consumption was estimated by 24 h weighing on the penultimate day of each intervention and washout period, and maternal adiposity (% body fat) was determined at baseline using dual energy X-ray absorptiometry. Milk c9,t11–18:2 and t10,c12–18:2 concentrations were greater (P < 0.05) during the corresponding CLA treatment periods as compared to the placebo period, providing strong evidence of subject compliance. Both CLA isomers were transferred into milk fat at relatively high efficiency; average transfer efficiency was estimated to be 23.3%. Compared to the placebo treatment, milk fat content was not reduced during either CLA treatment. Data indicate that body fatness did not modify any putative effect of isomeric CLA consumption on milk fat concentration. The evidence from this study suggests that the sensitivity of lactating women’s mammary tissue to an anti-lipogenic effect of the t10,c12–18:2 isoform of CLA may be less than previously hypothesized.     

Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels

Sprout damage (pre-harvest germination) in wheat results in highly deleterious effects on end-product quality. Alpha-amylase, the pre-dominant enzyme in the early stage of sprouting has the most damaging effect. This paper introduces a new method using a SWIR hyperspectral imaging system (1000–2500 nm) to predict the α-amylase activity of individual wheat kernels. Two classes of Canadian wheat, Canada Western Red Spring (CWRS) and Canada Western Amber Durum (CWAD), with samples of differing degrees of sprout damage were investigated. Individual kernels were first imaged with the hyperspectral imaging system and then the α-amylase activity of each kernel was determined analytically. Individual kernel α-amylase activity prediction was significant (R ² 0.54 and 0.73) for CWAD and CWRS, respectively using Partial Least Square regression on the hyperspectral data. A classification method is proposed to separate CWRS kernels with high α-amylase activity level from those with low α-amylase activity giving an accuracy of above 80%. This work shows that hyper/multi-spectral imaging techniques can be used for rapidly predicting the α-amylase activity of individual kernels, detecting sprouting at early stage.     

Energy inputs and crop yield relationship in potato production in Hamadan province of Iran

The aim of this study was to determine energy consumption and the relationship between energy input and yield for potato production in Kaboud Rahang region of Hamadan state. The data used in this study are collected by questionnaire. The results revealed that nitrogen fertilizer (39%), diesel (21%), seed (14.9%), water (7.5%) and manure (6.4%) consumed the bulk of energy. In the surveyed farms, average yield and energy consumption were calculated as around 28613.7 kg/ha, 92296.3 MJ/ha, respectively. The results also showed that energy ratio, specific energy and energy productivity were 1.1, 3.2 MJ/kg and 0.3 kg/MJ, respectively. An econometric model was developed to estimate the impact of energy inputs on yield by using parametric methods. For this purpose, potato yield, an endogenous variable was assumed to be a function of energy inputs: fertilizer manure, chemical, machinery, human, water for irrigation, diesel and seed. The empirical results indicated that variables: fertilizer, chemical, seed and human were found statistically significant and contributed to yield. Among statistically significant exogenous variables, seed, water for irrigation, chemical, human and fertilizer were ranked in terms of elasticities.