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Pretreatment with N-methylmorpholine-N-oxide (NMMO), phosphoric acid, and sodium hydroxide was evaluated for improvement of dilute-acid hydrolysis of cotton fiber, the most difficult to break down cellulose. The pretreatments improved the yield of glucose formation by acid hydrolysis. Compared to the other methods, phosphoric acid pretreatment resulted in higher glucose yields and minimal byproduct formations by hydrolysis under milder conditions. Furthermore, the solid residue of the hydrolysis was subjected to enzymatic hydrolysis in order to convert the remaining cellulose to glucose. Different combinations of parameters in dilute-acid and enzymatic hydrolysis were considered for obtaining a high glucose yield with minimal enzyme loading. A process involving phosphoric acid pretreatment, dilute-acid hydrolysis, and enzymatic hydrolysis using only 5 FPU/g cellulase and 10 IU/g β-glucosidase resulted in total glucose yield of 95.4%, and fermentation of the hydrolysates resulted in a yield of 458 g of eth...

Network-on-Chips are now the popular communication medium to support inter-IP communications in complex on-chip systems with tens to hundreds IP cores. Higher scalability (compared to the traditional shared bus and point-to-point interconnects), throughput, and reliability are among the most important advantages of NoCs. Moreover, NoCs can well match current CAD methodologies mainly relying on modular and reusable structures with regularity of structural pattern. However, since NoCs are resource-limited, determining how to distribute application load over limited on-chip resources (e.g. switches, buffers, virtual channels, and wires) in order to improve the metrics of interest and satisfy the application requirements becomes a challenging research issue known as topological mapping problem. This paper introduces a topological mapping strategy for direct networks. The Multi-Objective Genetic Algorithm (MOGA) is used to obtain optimal Pareto-front of topological mapping solutions for an arbitrary network topology using a deadlock-free routing algorithm. Considered cost functions are the network latency and power consumption which are accurately estimated through two accurate analytical models. Before using the proposed analytical models in our MOGA method, we validate them through extensive simulation experiments, and compare their accuracy to some known models already proposed in the literature. We then quantitatively and qualitatively compare our analytical model based mapping method to two other methods: a genetic-based and a heuristic. Experimental evaluations using real workloads confirm that the proposed method is cost-efficient and can be used as a powerful tool for NoC design space exploration. Compared to the traditional mapping strategies, our mapping mechanism has the following advantages: (1) it greatly shortens the design period by using analytical models for fast and accurate predictions; (2) it can give a set of solutions, using MOGA, in terms of Pareto-front including, at least one performance-optimal and one power-optimal, and some intermediate solutions; and (3) its runtime is reduced by determining the best generation size based on the used benchmark.

Beamforming is a signal processing technique. It has been studied in many areas such as radar, sonar, seismology and wireless communications, to name but a few. It can be used for a myriad of purposes, such as detecting the presence of a signal, estimating the direction of arrival, and enhancing a desired signal from its measurements corrupted by noise, competing sources and reverberation. Actually, Beamforming has been adopted by the audio research society, mostly to separate or extract speech for noisy environment. Beamforming techniques basically approach the problem from a spatial point of view. A microphone array is used to form a spatial filter which can extract a signal from a specific direction and reduce the contamination of signals from other directions. In this paper we survey some Beamforming techniques used for multichannel audio signal separation., 9 pages, 7 Figures

An efficient and above all cheap solutions, biometrics provide extensive information in access control applications. 3D mode provides great new opportunities in this sector in recent years. Firstly the paper discusses the formal work done in this domain discussing approach based on curvature calculation, alignment of surfaces, feature selection and facial curve including different techniques for 3D facial recognition data. The second part is about the steps required for the preprocessing phase of the 3D face data. Various experiments conducted and results obtained.

Hydraulic parameters play an important role in mass transfer phenomena in photocatalytic reactors. These fundamental hydraulic parameters are flow rate, relative roughness, and Reynolds number. This research experimentally evaluates the effects of flow rate and artificial relative roughness in order to determine the factors influencing the removal efficiency and reaction rate constant of Reactive Yellow 81 (RY81). For this purpose, a cascade photocatalytic reactor is constructed which consists of similar polymethylmethacrylate plates coated by ZnO nanoparticles and with various types of roughness. Dye removal efficiency is calculated for different flow rates with and without considering relative roughness. Furthermore, this study also reveals main effective parameters increasing the mass transfer rate, and uses a decision tree model called M5-Pruned (M5P) for predicting dye removal efficiency. Therefore, simple relations are provided for calculation of dye removal efficiency that consider importan...

Background: Pain management after abdominal surgery is a critical issue in cancer patients undergoing laparotomy. Opioid analgesics commonly used postoperatively have significant side effects and can postpone restoring normal life. Administration of analgesics before the surgery by inhibiting pain cascades may be an effective method for more efficient pain control. Objectives: This study aimed to investigate the effect of the preemptive use of oral pregabalin-acetaminophen-naproxen on pain control and morphine consumptions in cancer patients undergoing laparotomy. Patients and Methods: A total of 40 cancer patients scheduled for open abdominal surgery were randomized into the two groups. one group received combination of pregabalin 150 mg, acetaminophen 1 g and naproxen 250 mg (the PAN group) an hour before laparotomy. Following the surgery, morphine was administered on a protocolized schedule based on patients’ demand for pain control. Postoperative pain level was assessed using universal pain assessment tool (UPAT) at 0, 2, 4, 6, 12, 24 and 48 hours after the operation. The postoperative morphine dose and complications were noted. Data were analyzed using SPSS version 16. Results: Patients in the PAN group had significantly lower UPAT scores at 0, 2, 4, 6, 12, 24 and 48 hours after the surgery than those in the control group (P = 0.008, 0.021, 0.008, 0.047, 0.004, 0.001, and 0.001). The mean dose of postoperative morphine consumption in the PAN group was 37% less than the control group (P = 0.001). The complications were not significantly different between the two groups. Conclusions: Preemptive use of pregabalin-acetaminophen-naproxen decreases intensity of pain and morphine consumption in the cancer patients after laparotomy without significant complications.

The pattern recognition domain, particularly the online Arabic character recognition is a rich area of research. This paper puts forward a new approach for character recognition using domain ontology. Our main idea is based on the modeling of Arabic character by construction of ontology created by a domain expert. This ontology consists of a set of concepts and spatial relations between them. The concepts represent the strokes extracted by segmenting the character introduced. The aim of this work is to study the role of domain ontologies and their contributions in the recognition phase.

Recognition of Arabic Character field has been gaining more interest for many years, and a large number of research papers and reports have already been published in this area. There are several major issues with Arabic character recognition: Arabic characters are spelled differently (depending on whether they are isolated, at the beginning, in the middle or at the end of the word), multiple characters can have the same body but a number and/or position of various diacritics. The size of the Arabic characters may vary from one writer to another and even within the writing of a single writer, etc. This paper presents a new approach for feature extraction step of online handwritten Arabic character using global and local features. The system was tested with 2000 Characters written by different writers and the best rate of recognition obtained was 92.43%.

As the environment identification is a vital necessity for blind people, a visual substitution system based on videoanalysis is the solution to their problem. This paper focuses onthe assessment and integration of the local dissimilarity map inthe video processing. A Real Value local Dissimilarity Map isbuilt for grayscale images in order to get an excellent detectionof similar frames. The elimination of similar frames processingallows reducing the redundancy of objects re-identification.

With the massive changes in input acquisition systems such as smart phones and tablets, the field of handwriting recognition has more attention accorded by a several researchers. This article addresses the problem of online Arabic character segmentation. Our approach is based on top-down segmentation-free of Arabic character by detecting the candidate points in the general chain code of the character to extraction strokes. The main goal of our work to ameliorate the recognition rate.

In this paper, we propose a technique of features selection based on modified particle swarm optimization (MPSO) for face recognition system. PSO is a new class of algorithm for feature selection based on the idea of collaborative behavior of bird flocking. The feature selected by the proposed MPSO algorithm plays a vital role to search the solution space for an optimum solution where features are carefully selected according to a well defined discrimination criterion. Several novelties are introduced to make the recognition robust to varying illumination, facial expressions and poses at certain angles is challenging. Image of the face is divided first into sub-regions. Afterwards, the MPSO algorithm is applied to coefficients extracted by Discrete Wavelet Transform (DWT). We illustrate the experimental results of our new algorithm with the minimal set of selected features using different experimental protocols on several databases, including Yale Face, FEI and ORL.

Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work, we proposed an application to detect Alzheimer's diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Hydatidosis, is an important worldwide zoonotic disease caused by larval stages (metacestodes) of tapeworm parasites of the genus Echinococcus. The objective of the present study was to determine the seroprevalence of equine hydatidosis in Iran by latex agglutination test. This study also served to correlate sex and age with mentioned results in cases. Therefore, 193 serum samples were collected from clinically healthy horses at 9 race clubs in Kerman, Yazd and Golestan provinces, Iran. According to the results, antibodies against hydatidosis were detected in 6 sera (3.11 %) among 193 samples. Results showed two male and four female horses were sero-positive against hydatidosis. In conclusion, present study shows that antibodies against hydatidosis have been detected in Iran equine population. Therefore, it seems that Iranian horse clubs should improve their management and health levels to increase their proficiencies.

This paper aims to contribute in modeling and implementation, over a system on chip SoC, of a powerful technique for phonemes recognition in continuous speech. A neural model known by its efficiency in static data recognition, named SOM for self organization map, is developed into a recurrent model to incorporate the temporal aspect in these applications. The obtained model RSOM will subsequently introduced to ensure the diversification of the genetic algorithm GA populations to expand even more the search space and optimize the obtained results. We assigned a chromosomal vision to this model in an effort to improve the information recognition rate.

Background Hip fracture–related pain both before and after surgery is generally reported as severe by most patients. Various regional pain control modalities have been described in order to reduce pain in these patients. Objectives Because of the challenges of lumbar plexus block (LPB) and the fact that the effect of combined femoral nerve block/spinal anesthesia in controlling pain after orthopedic surgeries has not been investigated, in this study, we compared the feasibility and efficacy of the 2 techniques in the perioperative management of proximal hip fractures. Patients and Methods The study included 32 patients with femoral intertrochanteric fracture who were randomly divided into the following 2 groups of 16 patients each: combined femoral nerve block/spinal anesthesia group (group I) and LPB group (group II). Patients in group I received 0.17% bupivacaine with 0.7% lidocaine, 20–25 mL for femoral nerve block and bupivacaine 0.5% plus 0.5 mL pethidine (25 mg) for spinal block and patients in group II received 0.17% bupivacaine with 0.7% lidocaine, 30–35 mL. Results The time for performing the block (12.2 ± 3.3 vs. 4.93 ± 1.6 min, P = 0.001) and achieving the block (7.7 ± 0.9 vs. 2.4 ± 1.0 min, P = 0.001) were significantly longer in the combined femoral nerve block/spinal anesthesia group than in the LPB group. Duration of analgesia in the combined femoral nerve block/spinal anesthesia group was longer than that in the LPB group, but the difference was not significant (17 ± 7.3 vs. 16.5 ± 8.5 h, P = 0.873). There were no significant differences in hemodynamic parameters regarding the method of anesthesia in the 2 groups. Conclusions This study confirms that the combination of femoral nerve block with spinal anesthesia is safe and comparable with LPB and can provide more effective anesthesia and longer lasting analgesia for intertrochanteric surgery.

With the evolution of the next generation networks several applications have emerged to be used through the web. Applications allowing the analysis and the recognition of documents are emerged to be used through Internet. Document pre-processing output may affect the efficiency of document analysis and recognition systems. In order to ameliorate the efficiency of such systems, an objective evaluation of pre-processing steps is necessary. The authors propose a new framework for automatic evaluation of binarization approaches. The evaluation of binarization approaches is based on the comparison between binary images and their ground-truths. For that reason a method for the generation of ground-truth is proposed. This method is evaluated using the benchmarking dataset DIBCO 2009. The comparison between binarization methods’ results is based on several evaluation metrics as FM and PSNR. The proposed framework is tested on printed images (250 images) from the Google-Books collection and on handwritten images (60 images) from the IAM historical database.

Image denoising is an important pre-processing step for many image analysis and computer vision system. It refers to the task of recovering a good estimate of the true image from a degraded observation without altering and changing useful structure in the image such as discontinuities and edges. In this paper, we propose a new approach for image denoising based on the combination of two non linear diffusion tensors. One allows diffusion along the orientation of greatest coherence while the other allows diffusion along orthogonal directions. The idea is to track perfectly the local geometry of the degraded image and applying anisotropic diffusion mainly along the preferred structure direction. To illustrate the effective performance of our method, we present some experimental results on a test and real photographic color images.

“Anesthesia” for awake craniotomy is a unique clinical condition that requires the anesthesiologist to provide changing states of sedation and analgesia, to ensure optimal patient comfort without interfering with electrophysiologic monitoring and patient cooperation, and also to manipulate cerebral and systemic hemodynamics while guaranteeing adequate ventilation and patency of airways. Awake craniotomy is not as popular in developing countries as in European countries. This might be due to the lack of information regarding awake craniotomy and its benefits among the neurosurgeons and anesthetists in developing countries. From the economic perspective, this procedure may decrease resource utilization by reducing the use of invasive monitoring, the duration of the operation, and the length of postoperative hospital stay. All these reasons also favor its use in the developing world, where the availability of resources still remains a challenge. In this case report we presented a successful awake craniotomy in patient with a frontal bone mass.

In general, digital images can be classified into photographs, textual and mixed documents. This taxonomy is very useful in many applications, such as archiving task. However, there are no effective methods to perform this classification automatically. In this paper, we present a method for classifying and archiving document into the following semantic classes: photographs, textual and mixed documents. Our method is based on combining low-level image features, such as mean, Standard deviation, Skewness. Both the Decision Tree and Neuronal Network Classifiers are used for classification task.

Vision is one of the very essential human senses and it plays the most important role in human perception about surrounding environment. Hence, over thousands of papers have been published on these subjects that propose a variety of computer vision products and services by developing new electronic aids for the blind. This paper aims to introduce a proposed system that restores a central function of the visual system which is the identification of surrounding objects. This method is based on the local features extraction concept. The simulation results using SFIT algorithm and keypoints matching showed good accuracy for detecting objects. Thus, our contribution is to present the idea of a visual substitution system based on features extractions and matching to recognize and locate objects in images.

This paper presents an intelligent strategy for anomaly detection in induction machines using the map SOM. It involves the most significant parameters of SOM, such as the topological structure of the map, the Kohonen learning algorithm, the activity diagram in a micro light and frequencies characteristics of anomalies. A comparative study of the anomaly detection performance was conducted under the SOM neural map and the vibration analysis method. Eventually, a simulation is made on Matlab for the SOM learning accompanied by monitoring of the experimental vibration analysis in the NDC (Non Destructive Control) Laboratory. It will be a more synthetic analysis.

Introduction: Muscle rigidity and generalized spasm can cause severe pain in patients with tetanus. Administration of high dose sedative or narcotic agents can increase respiratory failure and prolong mechanical ventilation support. Case Presentation: In this report, ultrasound-guided sciatic nerve block was performed in a 25-year-old patient with tetanus to progress his respiratory drive which was decreased due to high dose sedative administration. This procedure accelerates the weaning process and extubation. Conclusions: Nerve blocks may be appropriate for the particular patient with tetanus to tolerate the pain, reduce depth of sedation, accelerate extubation and subsequently decrease complications of tetanus relating to long time intubation.

Waste burial in uncontrolled landfills can cause serious environmental damages and unpleasant consequences. Leachates produced in landfills have the potential to contaminate soil and groundwater resources. Leachate management is one of the major issues with respect to landfills environmental impacts. Improper design of landfills can lead to leachate spread in the environment, and hence, engineered landfills are required to have leachate monitoring programs. The high cost of such programs may be greatly reduced and cost efficiency of the program may be optimized if one can predict leachate contamination level and foresee management and treatment strategies. The aim of this study is to develop two expert systems consisting of Artificial Neural Network (ANN) and Principal Component Analysis-M5P (PCA-M5P) models to predict Chemical Oxygen Demand (COD) load in leachates produced in lab-scale landfills. Measured data from three landfill lysimeters, including rainfall depth, number of days after waste deposition, thickness of top and bottom Compacted Clay Liners (CCLs), and thickness of top cover over the lysimeter, were utilized to develop, train, validate, and test the expert systems and predict the leachate COD load. Statistical analysis of the prediction results showed that both models possess good prediction ability with a slight superiority for ANN over PCA-M5P. Based on test datasets, the mean absolute percentage error for ANN and PCA-M5P models were 4% and 12%, respectively, and the correlation coefficient for both models was greater than 0.98. Developed models may be used as a rough estimate for leachate COD load prediction in primary landfill designs, where the effect of a top and/or bottom liner is disputed.

Background Preemptive analgesia may be considered as a method not only to alleviate postoperative pain but also to decrease analgesic consumption. Different regimens are suggested, but there is currently no standard. Objectives The aim was to measure the efficacy of preemptive analgesia with pregabalin, acetaminophen, naproxen, and dextromethorphan in radical neck dissection surgery for reducing the intensity of pain and morphine consumption. Patients and methods This study was conducted as a randomized double-blind clinical trial. Eighty adult patients (18 to 60 years of age) under the American society of anesthesiologists (ASA) physical status I and II undergoing elective radical neck dissection were enrolled. Patients were randomized into two groups of 40 with a simple randomization method. The case group received a combination of 15 mg/kg acetaminophen, 2.5 mg/kg pregabalin, 7 mg/kg naproxen, and 0.3 mg/kg dextromethorphan administered orally one hour prior to surgery. Postoperative pain was assessed with the universal pain assessment tool (UPAT) at 0, 2, 4, 6, 12, and 24 hours after surgery. Subjects received morphine based on postoperative pain control protocol. Total administered morphine doses were noted. Results Postoperative pain rates at 0, 2, 4, 6, 12, and 24 hours after surgery were significantly lower for the case group than the control group (P values = 0.014, 0.003, 0.00, 0.00, and 0.00, respectively). Total morphine doses for the preemptive analgesia group were 45% lower than those of the other group. Side effects were similar for both groups. Conclusions A single preoperative oral dose of pregabalin, acetaminophen, dextromethorphan, and naproxen one hour before surgery is an effective method for reducing postoperative pain and morphine consumption in patients undergoing radical neck dissection.

The main objective in production blasting is to achieve a proper fragmentation. In this paper, rock fragmentation the Sarcheshmeh copper mine has been predicted by developing a model using artificial neural network. To construct the model, parameters such as burden to spacing ratio, hole-diameter, stemming, total charge-per-delay and point load index have been considered as input parameters. A model with architecture 9-8-5-1 trained by back propagation method was found to be optimum. To compare performance of the neural network, statistical method was also applied. Determination coefficient (R 2) and root mean square error were calculated for both the models, which show absolute superiority of neural network over traditional statistical method.

Removing noise in magnetic resonance images (MRI) is a crucial issue in the field of medical image processing. These images are infected by Rician noise which is a non-additive noise, allows to reduce the image contrast and causes random fluctuations. Our paper proposed a new method for 3D MRI denoising based on new combination between non-local means filter and the diffusion tensor with adaptative MAD estimator Rician noise. The performance of our proposed algorithm was evaluated with respect to different quantitative measures, compared to other denoising methods which illustrate that our proposed denoising algorithm efficiently removes noise and preserves more details.

Content-Based Image Retrieval (CBIR) becomes necessary when dealing with large collections of images. Recently, interesting retrieval methods are based on Bag-of-Words (BoW) model. It allows the projection of an image in a vector space called the word space. Nevertheless, this space can be further refined in order to reflect the semantic content of an image. Indeed, it can be transformed into a lower dimensional vector space called the topic space. In this paper, we propose a retrieval system based on an unsupervised learning: First, a Self-Organizing Map (SOM) is learnt to construct the word space from the feature space. Then, the word space is transformed into the topic space using the Latent Dirichlet Allocation (LDA) model. Once learnt, our model allows to infer a compact and semantic image representation. Experiments are performed using a set of “vehicule” images from Pascal VOC 2007 dataset. Evaluation shows that the proposed retrieval system leads to encouraging results.

The need to integrate automatic communication systems in daily applications and technology progress has encouraged speech synthesis sector development. Many variety of voice systems synthesis exist but diphones concatenation method appears to be efficient to contribute to speech synthesis of many languages. The first task in any development of speech synthesis system is to prepare the data base used for this aim. This paper describes the process of building an Arabic speech corpus based on diphones. We developed an acoustic data base of arabic diphones by studying the different possible combinations of phonemes to list all the possible diphones.

The moderately halophilic bacterium Nesterenkonia sp. strain F, which was isolated from Aran-Bidgol Lake (Iran), has the ability to produce acetone, butanol and ethanol (ABE) as well as acetic and butyric acids under aerobic and anaerobic conditions. This result is the first report of ABE production with a wild microorganism from a family other than Clostridia and also the first halophilic species shown to produce butanol under aerobic cultivation. The cultivation of Nesterenkonia sp. strain F under anaerobic conditions with 50 g/l of glucose for 72 h resulted in the production of 105 mg/l of butanol, 122 mg/l of acetone, 0.2 g/l of acetic acid and 2.5 g/l of butyric acid. Furthermore, the strain was cultivated on media with different glucose concentrations (20, 50 and 80 g/l) under aerobic and anaerobic conditions. Through fermentation with a 50 g/l initial glucose concentration under aerobic conditions, 66 mg/l of butanol, 125 mg/l of acetone, 291 mg/l of ethanol, 5.9 g/l of acetic acid and 1.2 g/l of butyric acid were produced. The enzymes pertaining to the fermentation pathway in the strain were compared with the enzymes of Clostridium spp. and the metabolic pathway of fermentation used by Nesterenkonia sp. strain F was investigated.

The perceived inability to economically provide conventional petroleum to meet the growing energy demands is facing a diverse and broad set of challenges. The major technical and commercial drawbacks of the existing biofuels (bioethanol or biodiesel) have prompted the continuing development of more advanced biofuels such as biobutanol. Acetone–butanol–ethanol (ABE) fermentation is an old process which recently attracted new interests for the production of butanol as an advanced biofuel. Efficient use of low cost lignocellulosic wastes as a carbon source for ABE fermentation can be a proper approach for the economical production of biobutanol. This chapter focuses on the utilization of lignocellulosic materials in ABE fermentation process. It explains the ABE fermentation process especially the processes that were economically used in the Soviet Union, China, and South Africa in the twentieth century. It also summarizes different technologies that have been suggested for the utilization of lignocelluloses for biobutanol production.

The two-dimensional images are often insufficient to achieve a perfect diagnosis in the medical area; on the other hand the three-dimensional images allow having an interesting deductive vision and there denoising has become a necessity and an essential need. Many methods have been proposed to reduce noise and to preserve edge, are usually used for 2D images and they have been extended to 3D data. The Non-Local Means filter has become the most popular one for denoising medical images, based on a weighted average of voxels inside a search window. In this work, we present a new method in the field of 3D image denoising based on combination between Non-local Means filters and the diffusion tensor. Our proposed algorithm is to normalize the weight average by adding iteratively an anisotropic diffusion stencil. The performance and efficiency of the algorithm are estimated by calculating various quality metrics and compared with other methods.

There are many challenges addressed in MRC compression, such as the segmentation of the document into foreground and background layers, represented as a binary mask. Usually, the resulting quality of a MRC compression method depends on the segmentation algorithm used to compute the binary mask. The aim of this work is to leverage state-of-the-art techniques in document segmentation allowing further content, such as text or images, to be exploited in an efficient way. Then, a novel methodology is proposed that leads to increasing document segmentation performance for MRC compression scheme.

In this work, a new algorithm for rare events detection is proposed. Different from traditional accuracy oriented approaches [1], our approach employs the dressed sigmoid function presented in our previous work [2] to estimate the presence of rare events in the image. Our detection methodology is performed in two steps. In the first instance, a quick search for rare events using a sliding window is performed. At the second step, we propose to improve the efficiency of the detection method using the entropy criterion as we exploited it in our segmentation approach. Different results are tested on a set of SAS (Synthetic Aperture Sonar images and synthesized images.

The progress in fields science, information technology and communication have allowed, since the 1970s, developing new electronic aids for the blind in order to overcome the difficulties that the dog and cane do not respond. Among the systems of electronic substitution, are the sensory substitution systems that capture a low-resolution picture of the visual scene and transforming them into an another sensory modality (tactile or auditory). The recognition and localization of objects can address the need for mobility of the blind by travel assistance, aid to navigation and identify objects. In this paper, we propose a system of visual substitution that restores a central function of the visual system which is the identification of surrounding objects. Indeed, we are interested in evaluating fast and robust algorithms to recognize and locate objects in images. The perspectives of this work is to consider a substitute tool in the recognition of the environment for blind or visual impaired people, relying on a robust system for recognizing objects in a video scene and an auditory system to identify this information.

Organosolv pretreatment was used to improve solid-state anaerobic digestion (SSAD) for methane production from three different lignocellulosic substrates (hardwood elm, softwood pine, and agricultural waste rice straw). Pretreatments were conducted at 150 and 180°C for 30 and 60 min using 75% ethanol solution as an organic solvent with addition of sulfuric acid as a catalyst. The statistical analyses showed that pretreatment temperature was the significant factor affecting methane production. Optimum temperature was 180°C for elmwood while it was 150°C for both pinewood and rice straw. Maximum methane production was 152.7, 93.7, and 71.4 liter per kg carbohydrates (CH), which showed up to 32, 73, and 84% enhancement for rice straw, elmwood, and pinewood, respectively, compared to those from the untreated substrates. An inverse relationship between the total methane yield and the lignin content of the substrates was observed. Kinetic analysis of the methane production showed that the process followed a first-order model for all untreated and pretreated lignocelluloses.

This paper presents a robust object tracking method based on the methodologies of statistical texture analysis of 2D images based on the theory of monogenic signal analysis, jointed with the color histogram. This novel feature extraction method is embedded thereafter in the mean shift framework. Compared with methods of state-of-the-art mean shift trackers, this method proves to be more discriminant and less sensitive to noise. The experimental results proved that our proposed method can achieve robust tracking performances in complex situations with fewer mean shift iterations.

BACKGROUND: Lumbar plexus block (LPB) is one of the anesthetic options in the elderly patients undergoing hip surgeries. LPB could be safe because it targets somatic nerve in psoas region. Effectiveness of LPB is attributed to the sufficient analgesia provided intraoperatively as well as postoperatively. Adequate muscle relaxation and immobility during surgery refers to its acceptability. OBJECTIVES: In this study, LPB was used as the anesthetic method to manage the elderly patients subjected to hip surgery. PATIENTS AND METHODS: A total of 50 patients aged 51 to 100 years were enrolled in this study. LPB was accomplished after a mild sedation and with a modified method using patient's fingertip width (FTW) as the distance unit to determine needle entry point under electrical nerve stimulation assistance. After targeted injection, procedure time, establishment time, block duration, surgery time, hemodynamic variables, and surgeon satisfaction score were documented and analyzed. Propofol in trivial doses was infused intraoperatively to provide clinical sedation. RESULTS: Mean patient's age was 73 ± 12 years with ASA II/III. Procedure time was 5.65 ± 1.24 minutes, establishment time was 130 ± 36 seconds, block duration was 13.1 ± 8 hours, surgery time was 149.7 ± 32.2 minutes, and surgeon satisfaction score was 9.8 ± 0.1. There was no complication and no failure. Hemodynamic stability was pleasantly achieved. CONCLUSIONS: By preserving hemodynamic stability, LPB in conjunction with a light sedation could be considered as a reliable prudent satisfying anesthetic option in management of hip fractures in the elderly patients with three beneficial characteristics of safety, effectiveness, and acceptability.

In this paper, we present a novel fusion approach 2D and 3D face recognition based on score level fusion using Relevance Vector Machine. RVM uses a combination of kernel functions on training data for classification and compared to SVM. Several novelties are introduced to make the recognition robust to facial expressions. These novelties include: Automatic invariant feature in the pose and in the facial expressions is calculated from 2D facial image and from the point cloud and to generate a local descriptor. These two descriptors are reduced separately by the Principal Component Analysis to provide two scores which are normalized and then are fused to improve the recognition performance. The proposed approach is tested on a CASIA-3D database, the total number of scans is 4674 among which 38 scans per person are available. The results are found to be promising, and show the potential of our approach, we obtain a 99.4% verification rate at 0.01 FAR.

Texture analysis is a major challenge for computer vision, since image textures process are widely used to perform tasks such as segmentation and object tracking. The performance of the tracking algorithm relies heavily on the accuracy of relevant information extracted from the object to track. Our hypothesis is that by adding log-Gabor filter to color features, and then embedded it to the mean shift framework, tracking performance will enhance outstandingly. Apart from the classical color histogram, and in order to expedite the extraction of parameters of the target model in complex situations, the Log-Gabor filter is employed jointly with color feature in this paper. The design method and implementation scheme of Log-Gabor filter and its feature encoding for texture extraction are described in detail. Compared with the LBP texture-color histogram based algorithms, our proposed method extracts effectively pertinent information in the target region, which describe better and characterize more robustly the target. Experimental results show that the proposed Log-Gabor filtering with color feature method, improves substantially the tracking efficiency and accuracy with fewer mean shift iterations than the tracking algorithm using LBP texture.

The Block Matching is a temporal compression technique used in the video encoding. The main purpose of this method is to determine the displacements of each block of pixels between two successive frames. This technique, performed in the step of motion estimation, occupies the majority of the total time of video coding. The aim of this work is to give a comparative study various search algorithms by Block Matching. This study does not focus only on the complexity and computation time of each algorithm, but it also gives objective and subjective quality assessment of decoded video frame by means of each search algorithm.

The object of image compression is to minimize redundant information. This redundancy is due to correlation between adjacent pixels in a digital image. Different approaches with variable efficacies deal with this type of redundancy; the decomposition into sub-bands either through discrete cosine transforms or through discrete wavelet transforms is generally used. In this work, we are interested in spatial sub-bands coding with wavelet decomposition and we present in this context a comparative study between 5/3 and 9/7 wavelet filters and the variation of the decoded image quality depending on the level of wavelet decomposition. We support our study by objective and subjective quality assessments.