Reverse Link Performance of DS-CDMA Cellular Systems through Closed-Loop Power Control, Base Station Assignment, and Antenna Arrays in 2D Urban Environment

The interference reduction capability of antenna arrays, base station assignment, and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. In this paper, we propose smart step closed-loop power control (SSPC) algorithm and base station assignment method based on minimizing the transmitter power (BSA-MTP) technique for direct sequence-code division multiple access (DS-CDMA) receiver in a 2D urban environment. This receiver consists of conjugate gradient adaptive beamforming and matched filter in two stages using antenna arrays. In addition, we study an analytical approach for the evaluation of the impact of power control error (PCE) on the DS-CDMA cellular systems. The simulation results indicate that the SSPC algorithm and the BSA-MTP technique can significantly improve the network bit error rate in comparison with conventional methods. Our proposed methods can also significantly save total transmit power and extend battery life in mobile units. In addition, we show that the convergence speed of the SSPC algorithm is faster than that of conventional algorithms. Finally, we discuss two parameters of PCE and channel propagation conditions (path-loss parameter and variance of shadowing) and their effects on the capacity of the system via some computer simulations.     

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models

Magnetic Resonance Materials in Physics, Biology and Medicine - Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However,...     

An effective characterization procedure for petroleum reservoir fluids using molecular type methods and cubic equations of state

Two eminent molecular type composition methods (paraffins, naphthenes, and aromatics [PNA] and saturates, aromatics, and polynuclear aromatics [SAP]) are employed to construct new characterization procedures for predicting the phase behaviour of petroleum fluids using a modified Peng–Robinson equation of state. The PNA and SAP methods divide a petroleum fraction into (PNA) and (SAP) homologous groups, respectively. Two generalized models are developed to predict the physical properties ($\mathrm{Mw},\mathrm{SG},T_b$) and equation of state (EOS) parameters ($T_c,P_c,\omega$) for both PNA and SAP sub-fractions. Each generalized model covers 18 different correlations in a single mathematical form that enables the model to return 18 outputs for PNA and SAP sub-fraction parameters. A new lumping method is also developed to convert triple PNA or SAP pseudo-components into single characterized fractions. Accordingly, seven different characterization procedures are introduced and compared with one another. The first two procedures are completely constructed based on the proposed models, and the other procedures encompass the models already developed. The results obtained from the simulation of the differential liberation test for 12 diverse reservoir fluids and bubble pressure prediction for 40 oil samples revealed that the first two methods (1 and 2) could enhance the abilities of the traditional characterization procedures for reservoir fluid modelling. The mean value of average absolute relative deviations (AARDs) over a total of 52 oil samples is about 6.5% for the proposed methods and is about 13.2% for the best previously existing methods. Moreover, an efficient workflow is provided for the parameter tuning process, which is notably capable of reducing the level of prediction errors using only three adjustable parameters.     

Catalytic performance of spinel-type Ni-Co Oxides for Oxidation of Carbon Monoxide and Toluene

Topics in Catalysis - This study aims to investigate the catalytic activity of spinel-type oxides (NixCoyO4) for the oxidation of toluene and carbon monoxide at low temperatures. A series of Ni/Co...     

Mechanics of bipedal and quadrupedal locomotion on dry and wet granular media

The effectiveness of exploration robots is contingent upon their capability and efficiency to locomote on contorted and multifaceted terrains. Traditional wheeled robots do not suffice at overcoming such terrains and complications they introduce. The proposed work explores the performance of WhegRunner, a whegged (i.e., wheel-legged) robotic platform used to examine the mechanics of running on granular media at different saturation levels. In particular, the effect of bipedal/quadrupedal gait, saturation level, stride length, and stride frequency on robot's forward body velocity and cost of transport (COT) was studied. To increment nominal stride length, the robot was fitted with different number of spoked whegs from 3 to 7. In addition, eight evenly spaced motor speeds ranging from 2.33 to 7.43 Hz were used to observe the effect of stride frequency. Similar trends were observed between the two gaits, with the quadrupedal gait showing better overall performance. On dry sand, wider strides were attained at reduced motor speeds with lower spoked whegs. However, on wet sand (15% and 30% saturation), wider strides were achieved with higher motor speeds and lower spoked whegs. As a result, lower spoked whegs accomplished higher body forward velocity and lower COT as saturation increased. The acquired knowledge elucidates underexplored mechanics of locomotion on granular media at different saturation levels. The WhegRunner can be used as a platform to obtain a greater insight on how to develop more proficient exploration robots, ones which can face complex and deformable terrains and mediums.     

Data-driven safe gain-scheduling control

Data-based safe gain-scheduling controllers are presented for discrete-time linear parameter-varying systems (LPV) with polytopic models. First, $\lambda$-contractivity conditions are provided under which the safety and stability of the LPV systems are unified through Minkowski functions of the safe sets. Then, a data-based representation of the closed-loop LPV system is provided, which requires less restrictive data richness conditions than identifying the system dynamics. This sample-efficient closed-loop data-based representation is leveraged to design data-driven gain-scheduling controllers that guarantee $\lambda$-contractivity and, thus, invariance of the safe sets. It is also shown that the problem of designing a data-driven gain-scheduling controller for a polyhedral (ellipsoidal) safe set amounts to a linear program (a semi-definite program). The motivation behind direct learning of a safe controller is that identifying an LPV system requires satisfying the persistence of the excitation (PE) condition. It is shown in this paper, however, that directly learning a safe controller and bypassing the system identification can be achieved without satisfying the PE condition. This data-richness reduction is of vital importance, especially for LPV systems that are open-loop unstable, and collecting rich samples to satisfy the PE condition can jeopardize their safety. A simulation example is provided to show the effectiveness of the presented approach.     

Effect of Using CuO–Oil Nanofluid on Surface Roughness and Thermal Performance during Superfinishing Process

This research aims to investigate the effect of adding copper oxide nanoparticles to the oil Gr-6004 base fluid and its concentration changes from 0.1% to 0.4% on the surface roughness of gudgeon pin and the thermal conductivity of the nanofluids during the superfinishing process. The main novelty of this investigation is analyzing the impact of utilizing CuO/oil Gr-6004 nanofluid on thermal conductivity of oil and surface quality of gudgeon pin during superfinishing process. Based on the results, adding nanoparticles to the oil Gr-6004 has significantly reduced the surface roughness. In addition, by increasing the concentration of nanoparticles to 0.4%, the surface roughness has decreased by 57% compared to oil Gr-6004. Also, by adding nanoparticles, the thermal conductivity of the nanofluids has increased to 19.5%. In addition, dispersing CuO nanoparticles into base fluid reduces oil temperature by 17.44%.     

Zinc-based metal-organic frameworks: synthesis and recent progress in biomedical application

Journal of Inorganic and Organometallic Polymers and Materials - This review based on Zn-based MOF is summarized on new insights for targeted drug delivery of medicinal compounds and developed for...