ORCID Profile
0000-0001-8574-8031
Current Organisations
Central Queensland University
,
COMSATS Institute of Information Technology - Abbottabad
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: World Scientific Pub Co Pte Lt
Date: 07-2014
DOI: 10.1142/S0219467814500119
Abstract: The automatic gender recognition of faces has many applications, for ex le surveillance, targeted advertisement and human computer interaction, etc. Humans have the ability to accurately determine the gender from faces, however, for a machine, it is a difficult task. Many studies have targeted this problem, but most of these studies have used images taken under constrained conditions. In Real-world systems have to process images with wide variations in lighting and pose that makes the classification task very challenging. We have analyzed the gender classification of real world faces. Faces from images are detected, aligned and represented using local binary pattern histograms. Adaptive boosting selects the discriminating features and boosted LBP features are used to train a support vector machine that provides a recognition rate of 95.5%.
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Association for Computing Machinery (ACM)
Date: 18-10-2019
DOI: 10.1145/3336121
Abstract: The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively.
Publisher: Elsevier BV
Date: 10-2020
Publisher: ACM
Date: 05-01-2017
Publisher: IEEE
Date: 10-2016
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Springer Science and Business Media LLC
Date: 14-12-2020
DOI: 10.1007/S12652-020-02749-7
Abstract: Energy-aware high-performance computing is becoming a challenging facet for streaming applications at edge devices in Internet-of-Things (IoT) due to the high computational complexity involved. Therefore, the number of processors has increased significantly on the multiprocessor system subsequently, Voltage Frequency Island (VFI) recently adopted for an effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, energy-aware scheduling of real-time streaming applications on edge-devices is investigated. First, an innovative re-timing based technique is developed to transform the dependent workload into an independent task model to avail resources and the wasted slack in the processors with a possible minimal prologue. Moreover, unlike the existing population-based optimization algorithms, a novel population-based algorithm, ARSH-FATI is proposed that can dynamically switch between explorative and exploitative search modes at run-time for performance trade-off. Finally, a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm is presented. Our static scheduler ARHS-FATI collectively performs task mapping and ordering. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based MPSoCs.
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICCD.2017.33
Location: Pakistan
No related grants have been discovered for Umair Ullah Tariq.