Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

A MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM USING COMPRESSED FINGER IMAGES

Finger image recognition remains one of the most prominent biometric identification methods. However, storage of finger databases needs allocation of huge secondary storage devices. In addition, there has been limited success in obtaining a satisfactory system due to the complexity of the problem. In this article, a low-cost, high-speed, multimodal biometric identification system trained with compressed finger images is presented with the objective to increase the overall matching confidence level. For this, three finger images of the left or right hand, or both, for one person are matched and the output decision is combined. A prototype optical-based finger-data acquisition system using the CCD (charge coupled device) digital still camera is adopted to capture a complete impression of finger area required for accurately identifying an individual. The acquired images then are compressed with a Coif5 wavelet packet-based scheme to increase the overall performance and eliminate bulk storage requirements. The finger image features are extracted with an adaptive neural network for the implementation of a three-finger multimodal system to achieve a peak identification rate of 100% (99.4% on average) in 0.15s for a database of 50 persons and 450 test images.     

Variation in patient dose due to differences in calibration and dosimetry protocols

For precise and accurate patient dose delivery,the dosimetry system must be calibrated properly according to the recommendations of standard dosimetry protocols such as TG-51 and TRS-398. However, the dosimetry protocol followed by a calibration laboratory is usually different from the protocols that are followed by different clinics, which may result in variations in the patient dose.Our prime objective in this study was to investigate the effect of the two protocols on dosimetry measurements.Dose measurements were performed for a Co-60 teletherapy unit and a high-energy Varian linear accelerator with 6 and 15 MV photon and 6, 9, 12, and 15 MeV electron beams, following the recommendations and procedures of the AAPM TG-51 and IAEA TRS-398 dosimetry protocols. The dosimetry systems used for this study were calibrated in a Co-60 radiation beam at the Secondary Standard Dosimetry Laboratory(SSDL) PINSTECH,Pakistan, following the IAEA TRS-398 protocol. The ratio of the measured absorbed doses to water in clinical setting,D_w(TG-51/TRS-398), was 0.999 and 0.997 for 6 and15 MV photon beams,whereas these ratios were 1.013,1.009, 1.003, and 1.000 for 6, 9, 12, and 15 MeV electron beams, respectively. This difference in the absorbed dosesto-water D_w ratio may be attributed mainly due to beam quality(K_Q) and ion recombination correction factor.     

Enhancing the Tribological Behavior of Lubricating Oil by Adding TiO2, Graphene,and TiO2/Graphene Nanoparticles

This article investigates the tribological behavior of nanoparticles (NPs) of titanium dioxide anatase TiO₂ (A), graphene, and TiO₂ (A) + graphene added to the pure base oil group ΙΙ (PBO-GΙΙ). The morphology of these two nanostructures of TiO₂ (A) and graphene was characterized by transmission electron microscopy (TEM). Oleic acid (OA) was blended as a surfactant into the formulation to help stabilize the NPs in the lubricant oil. A four-ball test rig was used to determine the tribological performance of six different samples, and an image acquisition system was used to examine and measure the wear scar diameter of the stationary balls. Field emission–scanning electron microscopy (FE-SEM) was used to examine the wear morphology. Energy-dispersive X-ray spectroscopy (EDX), element mapping, and Raman spectroscopy were employed to confirm the presence of (TiO₂ (A) + graphene) and the formation of a tribolayer/film on the mating surfaces. Moreover, a 3D optical surface texture analyzer was utilized to investigate the scar topography and tribological performance. The experiments proved that adding (0.4?wt% TiO₂ (A) + 0.2?wt% graphene) to the PBO-GΙΙ optimized its tribological behavior. These excellent results can be attributed to the dual additive effect and the formation of a tribofilm of NPs during sliding motion. Furthermore, the average reductions in the coefficient of friction (COF), wear scar diameter (WSD), and specific wear rate (SWR) were 38.83, 36.78, and 15.78%, respectively, for (0.4?wt% TiO₂ (A) + 0.2?wt% graphene) nanolubricant compared to plain PBO-GΙΙ lubricant.     

The physicochemical parameters during dry heating strongly influence the gelling properties of whey proteins

In this study we determined the composition (proportion of native proteins, soluble and insoluble aggregates) and quantified the gelling properties (gel strength and water holding capacity) of pre-texturized whey proteins by dry heating under controlled physicochemical conditions. For this purpose, a commercial whey protein isolate was dry heated at 80 °C (up to 6 days), 100 °C (up to 24 h) and 120 °C (up to 3 h) under controlled pH (2.5, 4.5 or 6.5) and water activity (0.23, 0.32, or 0.52). Gelling properties were quantified on heat-set gels prepared from reconstituted pre-texturized proteins at 10% and pH 7.0. The formation of dry-heat soluble aggregates enhanced the gelling properties of whey proteins. The maximal gelling properties was achieved earlier by increasing pH and water activity of powders subjected to dry heating. An optimized combination of the dry heating parameters will help to achieve better gelling properties for dry heated whey proteins.     

Tribological Characteristics of Calophyllum inophyllum–Based TMP (Trimethylolpropane) Ester as Energy-Saving and Biodegradable Lubricant

The purpose of this research is an experimental study of Calophyllum inophyllum (CI)-based trymethylolpropane (TMP) ester as an energy-saving and biodegradable lubricant and compare it with commercial lubricant and paraffin mineral oil using a four-ball tribometer. CI-based TMP ester is a renewable lubricant that is nonedible, biodegradable, and nontoxic and has net zero greenhouse gases. The TMP ester was produced from CI oil, which has high lubricity properties such as higher density, higher viscosity at both 40°C and 100°C and higher viscosity index (VI). Experiments were conducted during 3,600 s with constant load of 40 kg and constant sliding speed of 1,200 rpm at temperatures of 50, 60, 70, 80, 90, and 100°C for all three types of lubricant. The results show that CI TMP ester had the lowest coefficient of friction (COF) as well as lower consumption of energy at all test temperatures, but the worn surface roughness average (R_a) and wear scar diameter were higher compared to paraffin mineral oil and commercial lubricant. Before 80°C, CI TMP ester actually has a higher flash temperature parameter (FTP) than paraffin mineral oil and as the temperature increases, the FTP of TMP ester decreases. The worn surfaces of the stationary balls were analyzed by scanning electron microscopy (SEM) and results show that CI TMP ester has the highest wear compared to paraffin mineral oil and lowest wear compared to commercial lubricant. However, CI TMP ester is environmentally desired, competitive to commercial lubricant, and its use should be encouraged.     

Impact of β-glucan on debittering,bioaccessibility and storage stability of skim milk fortified with shrimp oil nanoliposomes

Shrimp oil was encapsulated in nanoliposomes and fortified into skim milk. Shrimp oil nanoliposomes (SONL) were thermodynamically stable when added into skim milk at 10 mL 100 mL⁻¹. Mild bitterness in fortified skim milk caused by the SONL was masked by adding β-glucan at various levels (0.05–0.2 g 100 mL⁻¹). With the addition of SONL, fortified skim milk appeared more reddish in colour due to the presence of astaxanthin. Addition of β-glucan resulted in the increase in viscosity of the fortified milk by forming network of junction zones. During the storage of skim milk fortified with SONL and 0.1 g 100 mL⁻¹ β-glucan at 4 °C for 15 days, no major quality changes took place. Simulated in vitro digestion studies revealed that 45.41 g 100 g⁻¹ eicosapentaenoic acid (EPA) and 48.86 g 100 g⁻¹ docosahexaenoic acid (DHA) from shrimp oil were bioaccessible for absorption in the gut after digestion.     

Industrie 4.0 Readiness: Green Computing in Relation with Key Performance Indicator for a Manufacturing Industry

Mobile Networks and Applications - An effective measure of Key Performance Indicators (KPIs) is of great significance to enhance the capabilities of a decision support system (DSS) of any...     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Tensile behavior of staple fiber yarns,part IV: experimental verification of predicted stress–strain curves

In this paper, specific stress–strain curves were predicted for viscose, cotton, and polyester yarns spun by ring and rotor technologies. The average fiber-specific stress–strain curves were used to predict yarn-specific stress–strain curves. The predicted yarn-specific stress–strain curves captured the experimental yarn-specific stress–strain curves well both in shape and position for the three studied yarn types. However, higher prediction error was observed relatively in polyester yarns due to more slippage of fibers in these yarns. All rotor yarns exhibited higher difference between experimental and predicted results which might be the result of the nature of the twist in rotor yarn.