Integrating security and privacy in mmWave communications

Faisal, Ghadah M.; Alshadoodee, Hasanain Abdalridha Abed; Abbas, Haider Hadi; Gheni, Hassan Muwafaq; Al_Barazanchi, Israa

doi:10.11591/eei.v11i5.4314

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The dataset's popularity within the realm of computer vision research is owed to its challenging nature, often presenting intricate scenes and instances of occlusion that test the limits of algorithms. The Caltech pedestrian dataset [23] stands as a definitive reference for evaluating pedestrian detection methodologies. Spanning a continuous ten-hour sequence captured at 640×480 resolution and a frame rate of 30 frames per second, this dataset is meticulously annotated with bounding boxes encompassing both the entire scene and all individuals present.…”

Section: Methodsmentioning

confidence: 99%

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Khalaf,

M. Abdulrahman,

Al_Barazanchi

et al. 2024

TELKOMNIKA

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Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes.

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Section: Methodsmentioning

confidence: 99%

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Khalaf,

M. Abdulrahman,

Al_Barazanchi

et al. 2024

TELKOMNIKA

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“…It should serve as a good reference point for how well the default implementation is on the AWS setup compared to the local one. As a third heuristic MurmurHeuristic [12], [13] is chosen and xxHeuristic [14], [15] as the fourth heuristic. Both are recent developments and focus on the throughput.…”

Section: General Purpose Heuristic Functionsmentioning

confidence: 99%

A powerful heuristic method for generating efficient database systems

Abbas,

JosephNg,

Khalaf

et al. 2023

Bulletin EEI

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Heuristic functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the heuristic function performs badly, the load in the reduce part will not be balanced and access times spike. To investigate this problem closer, we run an optimal database program with numerous different heuristic functions on database. We will leverage the Amazon elastic MapReduce framework. The paper investigates on general purpose, implementation, and evaluation of heuristic algorithm for generating optimal database system, checksum, and special heuristic functions. With the analysis, we present the corresponding runtime results. For the coding part, the records counting part is hasty and can only work for local Hadoop part, it can be debugged and optimized for general purpose implement on Hadoop and Spark and turn into an effective performance monitor tool. As mentioned before, there are strange issue, also the performance of BLAKE2s is unexpectedly slow in that it’s widely accepted the performance of BLAKE2s is much better than MD5 and SHA256, we would like to figure out why the common-sense performance of heuristics is deferent from what we got in distributed frameworks.

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“…Deep learning models use big data to automatically acquire the intrinsic features of the data, which can better represent the intrinsic information within the data while avoiding the uncertainty and complexity associated with human intervention and can using directly use raw data as input to using for learn data as features layer by the layer via multi-layer models. The Long short-term memory network LSTM [8][9], deep trust network DBN [10], and deep convolutional neural network RNN [11] are among the extant research achievements. These approaches have been routinely utilized for short-term home power usage forecasting.…”

Section: Introductionmentioning

confidence: 99%

Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks

Saber¹,

Hassan²,

Jabbar³

et al. 2023

PEN

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The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical energy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute parameters of electrical energy consumption. The method considers the timeseries homes of the information and offers parallelization of large-scale facts processing with magnificent operational efficiency, considering the timeseries aspects of the information and the problematic inherent correlations between variables. The exams have been done using the UCI public dataset, and the experimental findings validate the method's efficacy, which has clear, sensible implications for setting up intelligent strength grid dispatching.

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Integrating security and privacy in mmWave communications

Cited by 4 publications

References 13 publications

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

A powerful heuristic method for generating efficient database systems

Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks

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