Beneficiation studies on recovery and in-depth characterization of ilmenite from red sediments of badlands topography of Ganjam District,Odisha, India

This paper deals with the recovery of ilmenite mineral from red sediments of badlands topography and suggested flowsheet with material balance. The results of these investigations reveal that the red sediment samples contain 33.2% total heavy mineral, in which ilmenite mineral concentrate is 28.71% (by weight). The ilmenite concentrate recovered from red sediment sample by physical beneficiation process, which included scrubbing, desliming, gravity concentration, magnetic and electrostatic separation, contains 99.41% grade with 97.3% recovery. The ilmenite mineral concentrate recovered from red sediments is also suitable for industrial applications. The characterization studies on ilmenite reveal that the TiO₂ percentage is marginally increasing from 46.69% to 47.86% with increasing magnetic intensity from 0.46 to 1.55 T.     

存储器类型综述及DDR接口设计的实现

在过去的数年里,电子市场,确切地说是存储器市场,经历了巨大的变化.在2000年电子工业低迷时期之前,电子系统设计师很少考虑他们下一个设计中元器件的成本,而更关注它们能够达到的最商性能.     

A Chemically Orthogonal Hole Transport Layer for Efficient Colloidal Quantum Dot Solar Cells

Colloidal quantum dots (CQDs) are of interest in light of their solution-processing and bandgap tuning. Advances in the performance of CQD optoelectronic devices require fine control over the properties of each layer in the device materials stack. This is particularly challenging in the present best CQD solar cells, since these employ a p-type hole-transport layer (HTL) implemented using 1,2-ethanedithiol (EDT) ligand exchange on top of the CQD active layer. It is established that the high reactivity of EDT causes a severe chemical modification to the active layer that deteriorates charge extraction. By combining elemental mapping with the spatial charge collection efficiency in CQD solar cells, the key materials interface dominating the subpar performance of prior CQD PV devices is demonstrated. This motivates to develop a chemically orthogonal HTL that consists of malonic-acid-crosslinked CQDs. The new crosslinking strategy preserves the surface chemistry of the active layer beneath, and at the same time provides the needed efficient charge extraction. The new HTL enables a 1.4× increase in charge carrier diffusion length in the active layer; and as a result leads to an improvement in power conversion efficiency to 13.0% compared to EDT standard cells (12.2%).     

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR (Continuous stirred tank reactor) is employed in process control and chemical industries to improve response characteristics and system efficiency. It has a highly nonlinear characteristic that includes complexities in its control and design. Dynamic performance is compassionate to change in system parameters which need more effort for planning a significant controller for CSTR. The reactor temperature changes in either direction from the defined reference value. It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature, and deviation from reference values may cause degradation of biomass quality. Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential. In this paper, a conventional Proportional Integral Derivative (PID) controller is designed. The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters. Hence, A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller's limitation. In the proposed technique, PID parameters are tuned by Particle Swarm Optimization (PSO). It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response. In this article, a multi-objective function is proposed for PSO based controller design of CSTR.     

Performance analysis of minimal path fault tolerant routing in NoC

Occurrence of faults in Network on Chip (NoC) is inevitable as the feature size is continuously decreasing and processing elements are increasing in numbers. Faults can be revocable if it is transient. Transient fault may occur inside router, or in the core or in communication wires. Examples of transient faults are overflow of buffers in router, clock skew, cross talk, etc.. Revocation of transient faults can be done by retransmission of faulty packets using oblivious or adaptive routing algorithms. Irrevocable faults causes non-functionality of segment and mainly occurs during fabrication process. NoC reliability increases with the efficient routing algorithms, which can handle the maximum faults without deadlock in network. As transient faults are temporary and can be easily revoked using retransmission of packet, permanent faults require efficient routing to route the packet by bypassing the nonfunctional segments. Thus, our focus is on the analysis of adaptive minimal path fault tolerant routing to handle the permanent faults. Comparative analysis between partial adaptive fault tolerance routing West-First, North-Last, Negative-First, Odd Even, and Minimal path Fault Tolerant routing (MinFT) algorithms with the nodes and links failure is performed using NoC Interconnect RoutinG and Application Modeling simulator (NIRGAM) for the 2D Mesh topology. Result suggests that MinFT ensures data transmission under worst conditions as compared to other adaptive routing algorithms.     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.     

Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

Recently, there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy (DR). DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world. Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images. This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy (DLLSHDM-DR) on Retinal Fundus Images. The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method. In the DLLSHDM-DR technique, image preprocessing is initially performed to improve the quality of the fundus image. Besides, the DLLSHDM-DR applies HybridNet for producing a collection of feature vectors. For retinal image classification, the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer (EPO) with a Deep Recurrent Neural Network (DRNN). The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our work. To assuring the improved performance of the DLLSHDM-DR model, a wide range of experiments was tested on the EyePACS dataset. The comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.     

Automated Artificial Intelligence Empowered White Blood Cells ClassificationModel

White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes data normalization and data augmentation process (rotation and zooming) at the initial stage. In addition, the residual network (ResNet) approach was used for feature extraction in which the initial hyperparameter values of the ResNet model are tuned by the use of AO algorithm. Finally, Bayesian neural network (BNN) classification technique has been implied for the identification of WBC images into distinct classes. The experimental validation of the AOTL-WBCC methodology is performed with the help of Kaggle dataset. The experimental results found that the AOTL-WBCC model has outperformed other techniques which are based on image processing and manual feature engineering approaches under different dimensions.