IETE JOURNAL OF RESEARCH 是由印度电子和电信工程师学会 (IETE) 出版的双月刊。它发表描述原始研究工作或新产品/工艺开发的科学和技术论文。偶尔会针对新的和新兴的研究领域提出特殊问题。 IETE 研究期刊对有兴趣跟踪在电子、电信、计算机科学与工程和信息技术等广泛领域进行的原始研究和开发工作的研究人员、工程师、科学家、教师、管理人员和学生很有用.

IETE Journal of Research is a bi-monthly publication by the Institution of Electronics and Telecommunication Engineers (IETE) in India. It publishes scientific and technical papers that describe original research work or the development of new products/processes. Special issues may occasionally be dedicated to new and emerging research areas. The IETE Journal of Research is valuable for researchers, engineers, scientists, educators, managers, and students interested in tracking original research and development work across a wide range of fields, including electronics, telecommunications, computer science and engineering, and information technology.

IETE Journal of Research 是由印度电子和电信工程师学会 (IETE) 出版的双月刊。该期刊发表关于原始研究工作或新产品/工艺开发的科学和技术论文。偶尔会针对新的和新兴的研究领域发布特刊。IETE Journal of Research 对于希望跟踪电子、电信、计算机科学与工程以及信息技术等广泛领域的原始研究和开发工作的研究人员、工程师、科学家、教师、管理人员和学生非常有价值。

IETE Journal of Research

In view of the fact that a driver's smoking behavior seriously affects the driving safety, a feature pyramid network (FPN)-based smoking behavior identification method has been studied in order to reduce the occurrence of the driver smoking. Most of the existing research has been focused on detection and recognition based on movements while smoking or smog characteristics. Therefore, the probability of misjudgment in such methods is high, thus to address this issue, the present work proposes a method based on FPN to detect the driver's smoking behavior. FPN has been combined with the dilated convolution technique in order to detect a small target object in the driver's image and recognize their smoking behavior. By using the driver behavior images collected from the vehicle platform, a simulation experiment was carried out by employing the behavior identification method proposed in this work. The experimental results show that the accuracy of the proposed method is 94.75%, the recall rate is 96%, the precision rate is 95.05% and the area under the receiver operating characteristic curve is 95.5%.

Zuopeng Zhao

Haihan Zhao

Chen Ye

Xinzheng Xu

Kai Hao

Hualin Yan

Lan Zhang

Yi Xu

FPN-D-Based Driver Smoking Behavior Detection Method

With the increase in traffic on cloud servers due to the shifting of various services, information security has become a matter of major concern. This paper describes the security issues influencing cloud computing, compares the performance of traditional and the latest security techniques, and focuses on the challenges facing its implementation. Traditional schemes like RSA, ElGamal, and Pailler Algorithms do not allow operations to be carried out on the encrypted data, whereas the Homomorphic Encryption Scheme allows computation in the encrypted state without decrypting it to the original form. Thus, the security is ensured and also, there is no need to share the secured key. Fully Homomorphic Encryption Algorithms like: Gentry's Encryption Scheme, Dijk-Gentry-Halevi-Vaikunthanathan Scheme (DGHV), Brakerski-Vaikuntanathan Schemes (BGV) and Multi-key fully Homomorphic encryption are advanced techniques that provide security for the cloud are also described.

Ganesh Kumar Mahato

Swarnendu Kumar Chakraborty

A Comparative Review on Homomorphic Encryption for Cloud Security

Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier technique in communication system owing to its robustness against multipath fading and less complexity. Deep learning (DL) approach is more accurate and efficient than traditional approaches, which seeks more attention not only in the fields of natural language processing, video processing, speech and audio processing but also in the field of communication systems. The application of DL technique in OFDM systems supports better system performance, peak-to-average power ratio (PAPR) reduction, and improvement in spectral efficiency. This paper presents a detailed review of recent developments in DL techniques for OFDM systems to improve its performance in terms of bit error rate, signal-to-noise ratio, and PAPR. Various DL frameworks available for architectural design and processing are also explained in this article. The paper is concluded with a discussion on research gaps and challenges for future investigation and development.

M. Meenalakshmi

Saurabh Chaturvedi

Vivek K. Dwivedi

Deep Learning Techniques for OFDM Systems

正交频分复用系统中的深度学习技术

Ranjan K Mallik

Shiban K Koul

Arun Kumar

Scanning the Issue

扫描本期刊

This paper proposes a low-cost and high-throughput hardware accelerator for the deblocking filter in the encoder and decoder of high-efficiency video coding (HEVC). The total image is split into CTUs (coding tree units) of size 64 × 64 pixels each, which are further divided into 32 × 32 blocks and individually processed. Parallelism is simultaneously applied in this design to filter two luma edges and one chroma edge. In order to minimize the area utilization, the same filter is used for filtering both horizontal and vertical edges. The proposed architecture takes 36 clock cycles to process a 32 × 32 block of the image. The design is implemented in Virtex-6 and Zybo FPGA boards and also in the Cadence tool. The pipelining techniques are used effectively at various levels to provide a high-throughput design, which supports the deblocking of a 16 K UHD (ultra-high definition) video at 134 frames per second (fps).

P. Saravanan

R. Megalingam

S. Sanjana

T. Sharon Priyadharshini

S. Prashanth

P. Vignesh Kanna

Low-Cost and High-Throughput Hardware Accelerator for Deblocking Filter in HEVC

用于HEVC去块滤波器的低成本高吞吐量硬件加速器

S. Sivakumar

M. Mercy Theresa

K. Sudha

K. Sangeethalakshmi

Secure Wireless Sensor Networks: A Weighted K-NN and RNN-based Approach for Attack Detection and Localization

安全无线传感器网络：基于加权K-NN和RNN的攻击检测与定位方法

P. Vimal kumar

C. Balasubramanian

Tuned Linear Threshold Model for Tracking Influential Node in Social Network

社交网络中跟踪有影响力节点的调谐线性阈值模型

A. Hema Malini

M. Selvi

Dual-Discriminator Conditional Generative Adversarial Network-based LDPC-Coded OFDM Free-Space OCS for Improving BER Analysis

基于双判别器条件生成对抗网络的LDPC编码OFDM自由空间光通信以改善误码率分析

Suresh K

H. Faheem Nikhat

S. Poonkodi

Pugalenthi R

A Novel Approach for Lung Cancer Segmentation and Classification Using MSF-Customized ResNet152

一种使用MSF定制ResNet152的肺癌分割与分类新方法

Human activity recognition (HAR) leverages sensors such as accelerometers and gyroscopes to discern human physical activities, offering transformative insights for smart healthcare applications, from Parkinson's disease monitoring to diabetes management. While deep learning (DL) methods have emerged as frontrunners in HAR using wearable sensors, they often struggle with challenges stemming from long-term dependencies in sequential data and intricate feature extraction from expansive datasets. Addressing these gaps, this paper introduces a novel ensemble-based deep neural network that seamlessly integrates four distinct classifiers: CNN-LSTM, LSTM-CNN, CNN-BiLSTM, and BiLSTM-CNN. This combination enhances feature extraction and captures the detailed long-term patterns that have been challenging for traditional HAR models. By also using ensemble learning, our model becomes more consistent and reliable in its predictions. Benchmark evaluations on mHealth, UCI-HAR, and WISDM datasets validate the model's superiority, with accuracy scores of 99.91%, 98.10%, and 99.48% respectively. Further, the k-fold cross-validation technique is used to assess the performance results in terms of the mean F1-score, mean accuracy at k = 5. These compelling results underscore the model's capacity to address the inherent limitations of existing HAR systems, positioning it as a groundbreaking tool for advanced human activity recognition in smart healthcare scenarios.

Vaibhav Soni

Himanshu Yadav

Vijay Bhaskar Semwal

Sudhakar Tripathi

Improving Human Activity Recognition in Smart Healthcare with Ensemble Deep Learning

提高智能医疗中人体活动识别的集成深度学习方法

Abbreviation	IETE J. Res.
Journal Impact	1.44
Quartiles(Global)	TELECOMMUNICATIONS(Q4)

ISSN	0377-2063, 0974-780X
h-index	35

Publication Information	Publisher: Taylor and Francis Ltd.，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1993，Proportion of original research papers: 96.36%，Self Citation Rate:7.70%， Gold OA Rate: 0.07%
Average recruitment ratio	网友分享经验：容易

IETE Journal of Research

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