A novel weighted vehicular network clustering scheme

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An approach to convergence between LTE and DSRC

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A survey on classification algorithms of brain images in Alzheimer’s disease based on feature extraction techniques

Abstract: Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results

An approach for classification of Alzheimer’s disease using deep neural network and brain magnetic resonance imaging (MRI)

Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1 × 1, 3 × 3, and 5 × 5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.Web of Science123art. no. 67

Intelligent Reflecting Surface Assisted Localization: Opportunities and Challenges

Future generation communication systems are aiming to provide a tremendous high data rate with low-latency high reliable and three-dimensional coverage. To achieve such a challenging goal, it is required to have very precise location information related to the mobile terminal. The advancement of signal processing techniques and communication technologies enables the path for improving localization performance. Recently, intelligent reflecting surface (IRS) has been widely considered as the key element for the future generation of wireless communication. Over the past few years, the performance of IRS-assisted networks is extensively investigated from the point of view of communication purposes and its improvement. However, by virtue of its potential, IRS finds its application for wireless localization. In this paper, we discuss and summarize the works that have already been carried out targeting localization performance improvement. In addition, we figured out the associated challenges and the opportunities to scale up the localization accuracy. Particularly in this paper, the authors have discussed the challenging issues such as channel modeling, channel estimation, system architecture, hardware impairment, IRS deployment strategies, phase optimization, mobility management, and near-field environments. Although these challenges are associated with opportunities to make the IRS-assisted system more effective and efficient

Intelligent Reflecting Surface Assisted Localization: Opportunities and Challenges

Future generation communication systems are aiming to provide a tremendous high data rate with low-latency high reliable and three-dimensional coverage. To achieve such a challenging goal, it is required to have very precise location information related to the mobile terminal. The advancement of signal processing techniques and communication technologies enables the path for improving localization performance. Recently, intelligent reflecting surface (IRS) has been widely considered as the key element for the future generation of wireless communication. Over the past few years, the performance of IRS-assisted networks is extensively investigated from the point of view of communication purposes and its improvement. However, by virtue of its potential, IRS finds its application for wireless localization. In this paper, we discuss and summarize the works that have already been carried out targeting localization performance improvement. In addition, we figured out the associated challenges and the opportunities to scale up the localization accuracy. Particularly in this paper, the authors have discussed the challenging issues such as channel modeling, channel estimation, system architecture, hardware impairment, IRS deployment strategies, phase optimization, mobility management, and near-field environments. Although these challenges are associated with opportunities to make the IRS-assisted system more effective and efficient

Hybrid Precoding Algorithm for Millimeter-Wave Massive MIMO-NOMA Systems

In this paper, the performance of the millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) systems is investigated under multiple user scenarios. The performance of the system has been analyzed in terms of spectral efficiency (SE), energy efficiency (EE), and computational complexity. In the case of the mMIMO system, the linear precoder with matrix inversion becomes less efficient due to its high computational complexity. Therefore, the design of a low-complex hybrid precoder (HP) is the main aim of this paper. Here, the authors have proposed a symmetric successive over-relaxation (SSOR) complex regularized zero-forcing (CRZF) linear precoder. Through simulation, this paper demonstrates that the proposed SSOR-CRZF-HP performs better than the conventional linear precoder with reduced complexity