arXiv subjects

Weijia Han

Publications and source records attributed to Weijia Han.

22 records · Page 2Linked to original sources

arXiv2018

Ordered topographically patterned silicon by insect-inspired capillary submicron stamping

Insect-inspired capillary submicron stamping and subsequent surface-limited metal-assisted chemical etching (MACE) with ammonium bifluoride as HF source is employed for high-throughput production of ordered topographically patterned silicon (tpSi). Insect feet often possess hairy contact elements through which adhesive secretion is deployed. Thus, arrays of adhesive secretion drops remain as footprints on contact surfaces. Stamps for insect-inspired capillary submicron stamping having surfaces topographically patterned with contact elements mimic the functional principles of such insect feet. They contain spongy continuous nanopore networks penetrating the entire stamps. Any ink (organic, aqueous) may be supplied from the backside of the nanoporous stamps to the contact elements. We generated ordered arrays of AgNO3 dots extending mm^2 on Si by manual stamping with cycle times of a few seconds under ambient conditions; at higher load ordered holey AgNO3 films were obtained. Surface-limited MACE correspondingly yielded either macroporous tpSi or Si pillar arrays. Inkjet printing of polymer solutions onto tpSi yielded patterns of polymer blots conformally covering the tpSi. Such blot patterns could potentially represent a starting point for the development of persistent and scratch-resistant identity labels or QR codes on silicon surfaces.

physics.app-ph↗

arXiv2015

Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem.

cs.IT↗

arXiv2015

Cognitive Learning of Statistical Primary Patterns via Bayesian Network

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users. A large number of applications demand for more comprehensive knowledge on primary user behaviors in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship among primary users by introducing a Bayesian network (BN) based framework. How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community. Besides, another key problem in this learning scenario is that the CR has to identify how many variables are in the BN, which is usually considered as prior knowledge in statistical learning applications. To solve such two issues simultaneously, this paper proposes a BN structure learning scheme consisting of an efficient structure learning algorithm and a blind variable identification scheme. The proposed approach incurs significantly lower computational complexity compared with previous ones, and is capable of determining the structure without assuming much prior knowledge about variables. With this result, cognitive users could efficiently understand the statistical pattern of primary networks, such that more efficient cognitive protocols could be designed across different network layers.

cs.LG↗

arXiv2013

On the Performance of Cooperative Spectrum Sensing under Quantization

In cognitive radio, the cooperative spectrum sensing (CSS) plays a key role in determining the performance of secondary networks. However, there have not been feasible approaches that can analytically calculate the performance of CSS with regard to the multi-level quantization. In this paper, we not only show the cooperative false alarm probability and cooperative detection probability impacted by quantization, but also formulate them by two closed form expressions. These two expressions enable the calculation of cooperative false alarm probability and cooperative detection probability tractable efficiently, and provide a feasible approach for optimization of sensing performance. Additionally, to facilitate this calculation, we derive Normal approximation for evaluating the sensing performance conveniently. Furthermore, two optimization methods are proposed to achieve the high sensing performance under quantization.

cs.IT↗