Plasma immersion ion implantation for SOI synthesis: SIMOX and ion-cut

We have demonstrated feasibility to form silicon-on-insulator (SOI) substrates using plasma immersion ion implantation (PIII) for both separation by implantation of oxygen and ion-cut. This high throughput technique can substantially lower the high cost of SOI substrates due to the simpler implanter design as well as ease of maintenance. For separation by plasma implantation of oxygen wafers, secondary ion mass spectrometry analysis and cross-sectional transmission electron micrographs show continuous buried oxide formation under a single-crystal silicon overlayer with sharp Si/SiO₂ interfaces after oxygen plasma implantation and high-temperature (1300°C) annealing. Ion-cut SOI wafer fabrication technique is implemented for the first time using PIII. The hydrogen plasma can be optimized so that only one ion species is dominant in concentration and there are minimal effects by other residual ions on the ion-cut process. The physical mechanism of hydrogen induced silicon surface layer cleavage has been investigated. An ideal gas law model of the microcavity internal pressure combined with a two-dimensional finite element fracture mechanics model is used to approximate the fracture driving force which is sufficient to overcome the silicon fracture resistance.     

Microwave-Assisted Synthesis of Fine Particle Oxides Employing Wet Redox Mixtures

Interaction of electromagnetic radiation with a physical mixture of metal nitrates and amides/hydrazides is observed to initiate high-temperature reactions, useful for realizing several high-temperature ceramic materials. A judicious choice of such redox mixtures undergoes exothermic reactions when they couple with microwave radiation. The coupling of electromagnetic radiation with metal salts and amides/hydrazides depends on the dielectric properties of the individual components in the reaction mixture. The approach has been used to prepare γ-Fe₂O₃, Fe₃O₄, MgCr₂O₄, α-CaCr₂O₄, and La_0.7Ba_0.3MnO₃.     

Electrical transport properties of iron (II) molybdate

Electrical conductivity, thermoelectric power and static dielectric constant of iron (II) molybdate have been measured in the temperature range 300 to 1000 K on pressed pellets of polycrystalline sample. It has been found that FeMoO₄ is a p-type semiconductor with energy gap 4.1 eV. Different conduction mechanisms have been found below and above 700 K. Below 700 K conduction is due to a small polaron hopping mechanism and above 700 K conduction is due to large polarons as well as normal band conduction mechanism. Activation energy W, ₀ (T) and charge carrier mobility have been estimated in the two temperature ranges 300 to 700 K and 700 to 1000 K. Dielectric constant increases slowly with temperature up to 700 K and above 700 K, it increases exponentially with temperature.     

Modification of glassy carbon electrode with multi-walled carbon nanotubes and iron(III)-porphyrin film: Application to chlorate, bromate and iodate detection

In this study, multi-wall carbon nanotubes (MWCTs) is evaluated as a transducer, stabilizer and immobilization matrix for the construction of amperometric sensor based on iron-porphyrin. 5,10,15,20-Tetraphenyl-21H,23H-porphine iron(III) chloride (Fe(III)P) adsorbed on MWCNTs immobilized on the surface of glassy carbon electrode. Cyclic voltammograms of the Fe(III)P-incorporated-MWCNTs indicate a pair of well-defined and nearly reversible redox couple with surface confined characteristics at wide pH range (2-12). The surface coverage (Γ) and charge transfer rate constant (k_s) of Fe(III)P immobilized on MWCNTs were 7.68 × 10⁻⁹ mol cm⁻² and 1.8 s⁻¹, respectively, indicating high loading ability of MWCNTs for Fe(III)P and great facilitation of the electron transfer between Fe(III)P and carbon nanotubes immobilized on the electrode surface. Modified electrodes exhibit excellent electrocatalytic activity toward reduction of ClO₃⁻, IO₃⁻ and BrO₃⁻ in acidic solutions. The catalytic rate constants for catalytic reduction of bromate, chlorate and iodate were 6.8 × 10³, 7.4 × 10³ and 4.8 × 10² M⁻¹ s⁻¹, respectively. The hydrodynamic amperometry of rotating-modified electrode at constant potential versus reference electrode was used for detection of bromate, chlorate and iodate. The detection limit, linear calibration range and sensitivity for chlorate, bromate and iodate detections were 0.5 μM, 2 μM to 1 mM, 8.4 nA/μM, 0.6 μM, 2 μM to 0.15 mM, 11 nA/μM, and 2.5 μM, 10 μM to 4 mM and 1.5 nA/μM, respectively. Excellent electrochemical reversibility of the redox couple, good reproducibility, high stability, low detection limit, long life time, fast amperometric response time, wide linear concentration range, technical simplicity and possibility of rapid preparation are great advantages of this sensor. The obtained results show promising practical application of the Fe(III)P-MWCNTs-modified electrode as an amperometric sensor for chlorate, iodate and bromate detections.     

TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data

Maintaining a fluid and safe traffic is a major challenge for human societies because of its social and economic impacts. Various technologies have considerably paved the way for the elimination of traffic problems and have been able to effectively detect drivers’ violations. However, the high volume of the real-time data collected from surveillance cameras and traffic sensors along with the data obtained from individuals have made the use of traditional methods ineffective. Therefore, using Hadoop for processing large-scale structured and unstructured data as well as multimedia data can be of great help. In this paper, the TVD-MRDL system based on the MapReduce techniques and deep learning was employed to discover effective solutions. The Distributed Deep Learning System was implemented to analyze traffic big data and to detect driver violations in Hadoop. The results indicated that more accurate monitoring automatically creates the power of deterrence and behavior change in drivers and it prevents drivers from committing unusual behaviors in society. So, if the offending driver is identified quickly after committing the violation and is punished with the appropriate punishment and dealt with decisively and without negligence, we will surely see a decrease in violations at the community level. Also, the efficiency of the TVD-MRDL performance increased by more than 75% as the number of data nodes increased.

     

Isothermal oxidation behavior of FeCo–2V intermetallic alloy

Isothermal oxidation behavior and the nature of oxide layer formed during oxidation of FeCo–2V alloy were characterized in the temperature range of 500–600 °C. Oxidation kinetics of the alloy follows a parabolic rate law. SEM and XRD studies indicate the formation of an iron rich outer oxide layer and an inner solute rich layer containing cobalt and vanadium rich oxides. The oxidation mechanism of the FeCo–2V alloy is similar to that of low alloy steels. During the initial stages, preferential oxidation of iron and cobalt occurs at the alloy surface and leads to the formation of a solute rich inner layer. Continued oxidation occurs through oxidation of iron and cobalt at the outer layer and internal oxidation of inner layer. The iron rich oxide layer formed at the surface on oxidation of FeCo alloy is semi-conducting in nature and may not provide the necessary insulating barrier required at the surface to minimize eddy current losses during A.C. applications.     

Finite element prediction of material removal rate due to electro-chemical spark machining

Electro-chemical spark machining (ECSM) is an innovative hybrid machining process, which combines the features of the electro-chemical machining (ECM) and electrodischarge machining (EDM). Unlike ECM and EDM, ECSM is capable of machining electrically non-conducting materials. This paper attempts to develop a thermal model for the calculation of material removal rate (MRR) during ECSM. First, temperature distribution within zone of influence of single spark is obtained with the application of finite element method (FEM). The nodal temperatures are further post processed for estimating MRR. The developed FEM based thermal model is found to be in the range of accuracy with the experimental results. Further the parametric studies are carried out for different parameters like electrolyte concentration, duty factor and energy partition. The increase in MRR is found to increase with increase in electrolyte concentration due to ECSM of soda lime glass workpiece material. Also, the change in the value of MRR for soda lime glass with concentration is found to be more than that of alumina. MRR is found to increase with increase in duty factor and energy partition for both soda lime glass and alumina workpiece material.     

An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders

Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) are severe diseases that affect the eyes due to damage in blood vessels. Computer-aided automated grading will help clinicians conduct disease diagnoses at ease. Experiments of automated image processing with deep learning techniques using CNN produce promising results, especially in the medical imaging domain. However, the disease grading tasks in retinal images using CNN struggle to retain high-quality information at the output. A novel deep learning model based on variational auto-encoder to grade DR and DME abnormalities in retinal images is proposed. The objective of the proposed model is to extract the most relevant retinal image features efficiently. It focuses on addressing less relevant candidate region generation and translational invariance present in images. The experiments are conducted in IDRID dataset and evaluated using accuracy, U-kappa, sensitivity, specificity and precision metrics. The results outperform compared with other state-of-art techniques.     

Architectural design and FPGA implementation of radix-4 CORDIC processor

A new scaled radix-4 CORDIC architecture that incorporates pipelining and parallelism is presented. The latency of the architecture is n/2 clock cycles and throughput rate is one valid result per n/2 clocks for n bit precision. A 16 bit radix-4 CORDIC architecture is implemented on the available FPGA platform. The corresponding latency of the architecture is eight clock cycles and throughput rate is one valid result per eight clock cycles. The entire scaled architecture operates at 56.96 MHz of clock rate with a power consumption of 380 mW. The speed can be enhanced with the upgraded version of FPGA device. A speed-area optimized processor is obtained through this architecture and is suitable for real time applications.