Browsing by Author "Yen, Chia-Heng"

Now showing 1 - 3 of 3

Construction of Delay-Driven GNR Routing Tree
(2019-01-01)
Yan, Jin-Tai
;
Yen, Chia-Heng
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
It is known that graphene nanoribbon (GNR) based devices and interconnects can be treated to be better alternative in nano-scale designs. In this paper, given a source pin and a set of target pins inside a GNR grid-based routing plane, based on the consideration of the wirelength and bending delay in graphene nanoribbon, an efficient routing algorithm can be proposed to construct a delay-driven GNR routing tree with minimizing the total wirelength for the target pins. Compared with Das's algorithm [9] on total wirelength and maximum source-to-target delay, the experimental results show that our proposed routing algorithm only uses 8.05% of the extra wirelength to reduce 23.13% of the maximum delay in the construction of a delay-driven GNR routing tree for 6 tested examples on the average.
Feasible Assignment of Micro-Bumps in 3D ICs
(2018-01-01)
Yan, Jin-Tai
;
Yen, Chia-Heng
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
For the signal connections between two adjacent dies in 3D ICs, the redistributed layers (RDLs) routing from 10 pads to micro-humps plays an important role. In this paper, given a set of nets on the upper and lower RDLs between two adjacent dies, based on the separation of one 2-pin net on the upper and lower RDLs using one micro-hump, an efficient two-phase algorithm can he proposed to assign feasible micro bumps onto the nets to minimize the overlapping degree and the X-type intersections on the routing regions for single-layer RDI, routing. Compared with Kuan's algorithm and Yan's algorithm in the micro-hump assignment for single-layer RBI, routing, the experimental results show that our proposed algorithm can use less CPU time to assign feasible micro humps onto the given nets and obtain 100% routability with shorter wirelength in 5 tested examples.
Prediction of Metastasis in Head and Neck Cancer from Computed Tomography Images
(2018-01-01)
Lo, Tzu-Yun
;
Wei, Pei-Yin
;
Yen, Chia-Heng
;
Lirng, Jiing-Feng
;
Yang, Muh-Hwa
The current medical method for determining whether the malignant tumor of the head and neck metastasizes to the lymph is to interpret the pathological section of the patient's lymph. This study proposes a support vector machine (SVM) based method Pred-Meta to predict metastasis of a malignant tumor from a patient's computed tomography (CT) image. Pred-Meta utilizes three feature types, including texture, morphology, and grayscale, and an optimal feature selection method cooperated with SVM. The data set consists of 75 samples from 70 patients in head and neck cancer provided by Taipei Veterans General Hospital of Taiwan with a record of lymphatic metastasis. Pred-Meta using leave-one-out cross-validation achieved 100% in predicting metastasis. The merit of the Pred-Meta method is its non-invasiveness and low cost. Auxiliary physicians screen out patients with high risk of diversion in the early stages to help plan treatment guidelines. The limitation of Pred-Meta suffers from the small number of training samples. It is expected that Pred-Meta would perform better in testing independent cohort when the number of training samples significantly increases.