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Enhancement in Isolation among Collinearly Placed Microstrip Patch Antenna Arrays
Irfan Ali, Tunio,Hernan, Dellamaggiora,Umair, Saeed,Ayaz Ahmed, Hoshu,Ghulam, Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1
Strong surface waves among collinearly arranged patch antenna arrays pose unwanted inter element coupling particularly when high permittivity dielectric materials are used. In order to avert those waves, a novel Defected Ground Structure (DGS) is carved out systematically between two E-plane patch antenna elements. The introduced low profile μ shaped structure consequently improves impedance bandwidth and reflection coefficient by suppressing surface waves considerably. Parametric simulation results are analyzed and discussed.
A Review of Structural Testing Methods for ASIC based AI Accelerators
Umair, Saeed,Irfan Ali, Tunio,Majid, Hussain,Fayaz Ahmed, Memon,Ayaz Ahmed, Hoshu,Ghulam, Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1
Implementing conventional DFT solution for arrays of DNN accelerators having large number of processing elements (PEs), without considering architectural characteristics of PEs may incur overwhelming test overheads. Recent DFT based techniques have utilized the homogeneity and dataflow of arrays at PE-level and Core-level for obtaining reduction in; test pattern volume, test time, test power and ATPG runtime. This paper reviews these contemporary test solutions for ASIC based DNN accelerators. Mainly, the proposed test architectures, pattern application method with their objectives are reviewed. It is observed that exploitation of architectural characteristic such as homogeneity and dataflow of PEs/ arrays results in reduced test overheads.
Assessing Efficiency of Handoff Techniques for Acquiring Maximum Throughput into WLAN
Mohsin Shaikha,Irfan Tunio,Baqir Zardari,Abdul Aziz,Ahmed Ali,Muhammad Abrar Khan International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.4
When the mobile device moves from the coverage of one access point to the radio coverage of another access point it needs to maintain its connection with the current access point before it successfully discovers the new access point, this process is known as handoff. During handoff the acceptable delay a voice over IP application can bear is of 50ms whereas the delay on medium access control layer is high enough that goes up to 350-500ms. This research provides a suitable methodology on medium access control layer of the IEEE 802.11 network. The medium access control layer comprises of three phases, namely discovery, reauthentication and re-association. The discovery phase on medium access control layer takes up to 90% of the total handoff latency. The objective is to effectively reduce the delay for discovery phase to ensure a seamless handoff. The research proposes a scheme that reduces the handoff latency effectively by scanning channels prior to the actual handoff process starts and scans only the neighboring access points. Further, the proposed scheme enables the mobile device to scan first the channel on which it is currently operating so that the mobile device has to perform minimum number of channel switches. The results show that the mobile device finds out the new potential access point prior to the handoff execution hence the delay during discovery of a new access point is minimized effectively.
Distributed Incremental Approximate Frequent Itemset Mining Using MapReduce
Mohsin Shaikh,Irfan Ali Tunio,Syed Muhammad Shehram Shah,Fareesa Khan Sohu,Abdul Aziz,Ahmad Ali International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5
Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.