Shahidatul Sadiah

Optimizing wirelength involves predicting the total length of wires needed to connect different components within a chip during cell placement. It is a fundamental challenge in very-large-scale integration (VLSI) of integrated circuit (IC) design, as it directly impacts the overall performance and manufacturability of chips. Accurate wire-length estimation in the early stages of the design process is critical for guiding subsequent optimization tasks. This paper proposes a novel hybrid linear regression wirelength (hybrid-LRWL) method that combines the strengths of existing methods rectilinear Steiner minimal tree (RSMT) for low-degree nets and a statistical learning-based approach for high-degree nets. Additionally, it compares the performance of three well-established wirelength estimation techniques: half-perimeter wirelength (HPWL), rectilinear minimum spanning tree (RMST), and RSMT. The methods were evaluated using the International Symposium on Physical Design (ISPD) 2011 benchmark suite, considering accuracy and computational efficiency. The experimental results demonstrated that the proposed hybrid method achieves superior accuracy, with a mean error of less than 0.05% in total wirelength, closely approximating RSMT results. The proposed method reduces computational time up to 3.6 times faster than traditional RSM-based methods. The results establish a strong framework for accurate and efficient wirelength estimation in VLSI design for modern, high-performance ICs.

Medium access control (MAC) protocol and routing strategy significantly influence Wireless Network-on-Chip (WiNoC) performance and energy characteristics. Conventional WiNoC MAC typically utilizes a daisy-chained ring topology, which limits wireless channel performance due to maximum waiting times when a radio hub misses the token before packet arrival. Additionally, indiscriminate use of wireless transmission results in channel congestion even when efficient wired paths are available. This paper proposes DA+BMAC, a Distance-Aware (DA) Bidirectional Medium Access Control (BMAC) scheme for mesh WiNoC architecture. BMAC employs bidirectional links allowing token reversal when it overshoots an idle radio hub, reducing worst-case token wait time. DA+BMAC implements distance-aware routing to reserve single-hop wireless transmission exclusively for source-destination pairs beyond a specific distance threshold, while dedicating wired interconnects with higher aggregate bandwidth to short-range communication. Comprehensive validation using a cycle-accurate Noxim simulator on an 8×8 mesh with 16 radio hubs demonstrates up to 11.49× throughput improvement and 15% energy savings compared to a baseline token-ring WiNoC. Scalability analysis confirms performance benefits extend to larger networks (256 and 1024 cores), making DA+BMAC suitable for future many-core systems. The proposed approach has been validated using both synthetic traffic distributions (random, shuffle, transpose, and hotspot) and real-application traces (Barnes and Fluidanimate) from PARSEC and SPLASH-2 benchmark suites, confirming its effectiveness across diverse workloads.

Recently, crowd sensing has been intensively researched, due to the rapid growth of sensor-integrated mobile devices. Crowd sensing is a participatory sensing service where a server gathers and analyzes sensing data submitted from mobile devices of lots of users. In crowd sensing, the user's anonymity is desired, since the server gathers sensitive data from the participants including their GPS locations and moving path. However, the anonymous data submission may compromise the trust of the sensing data, because anonymous users may submit inappropriate sensing data, but they cannot be traced. Therefore, as the system to achieve both anonymity and trust in crowd sensing, ARTSense has been proposed. In the system, the trust of the sensing data is assessed on the sensed environment, other users' sensing, and the reputation of the user, and furthermore the reputation of the user is anonymously managed on the feedback from the trust assessment for the data. However, the anonymous reputation system of ARTSense has the efficiency problem, i.e., the user needs to wait a random time after the data submission phase before requesting the reputation update, which causes the communication delay. In this paper, we propose an efficient anonymous reputation system for crowd sensing, which can be integrated to the trust assessment in ARTSense. In the proposed system, during the data submission, the reputation update is anonymously completed. This is because the server does not manage the reputation of each user, but each user manages his/her reputation in the user side, where the the validity of the reputation is ensured by a certificate and anonymously checked by zero-knowledge proofs. Therefore, the proposed system achieves the better efficiency with no delay.

Previous studies proved that in addition to spatial characteristics function in extracting roughness information, there is potential roughness dependability across changes in the temporal mechanisms of textured surfaces. In the present study, we created temporal coding mechanism by converting fine spatial structure surface into a temporal drifting pattern. We proposed that regular spatial structure of gratings with variation of temporal mechanism have different influence toward roughness perception. Results showed massive individual differences of roughness perception between subjects, suggesting that a combination of spatial and temporal mechanisms accounts for perceptual judgments of roughness. We suggest that visual roughness judgment was determined by spatial in grating stimulation, and improvement is needed in spatial components before temporal coding of visual roughness can be declared.