Message from general co-chairs and program co-chairs

published_or_final_versionThe 4th International Joint Conference on Computational Sciences and Optimization (CSO 2011) Kunming and Lijiang, Yunnan Province, China, April 15-19, 2011. In Proceedings of the Computational Sciences and Optimization, 2011, p. 28-30

Joint Transceiver Design for Dual-Functional Full-Duplex Relay Aided Radar-Communication Systems

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms

Numerical simulation of earthquake-induced liquefactions considering the principal stress rotation

Dynamic loadings such as earthquake loadings can generate considerable principal stress rotation (PSR) in the saturated soil. The PSR without changes of principal stress magnitudes can generate additional excess pore water pressures and plastic strains, thus accelerating liquefaction in undrained conditions. This paper simulates a centrifuge model test using the fully coupled finite element method considering the PSR. The impact of PSR under the earthquake loading is taken into account by using an elastoplastic soil model developed on the basis of a kinematic hardening soil model with the bounding surface concept. The soil model considers the PSR by treating the stress rate generating the PSR independently. The capability of this soil model is verified by comparing the numerical predictions and experimental results. It also indicates that the PSR impact can not be ignored in predictions of soil liquefaction

Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks

This paper addresses the joint spectrum sharing and power allocation problem for device-to-device (D2D) communications underlaying a cellular network (CN). In the context of orthogonal frequency-division multiple-access systems, with the uplink resources shared with D2D links, both centralized and decentralized methods are proposed. Assuming global channel state information (CSI), the resource allocation problem is first formulated as a nonconvex optimization problem, which is solved using convex approximation techniques. We prove that the approximation method converges to a suboptimal solution and is often very close to the global optimal solution. On the other hand, by exploiting the decentralized network structure with only local CSI at each node, the Stackelberg game model is then adopted to devise a distributed resource allocation scheme. In this game-theoretic model, the base station (BS), which is modeled as the leader, coordinates the interference from the D2D transmission to the cellular users (CUs) by pricing the interference. Subsequently, the D2D pairs, as followers, compete for the spectrum in a noncooperative fashion. Sufficient conditions for the existence of the Nash equilibrium (NE) and the uniqueness of the solution are presented, and an iterative algorithm is proposed to solve the problem. In addition, the signaling overhead is compared between the centralized and decentralized schemes. Finally, numerical results are presented to verify the proposed schemes. It is shown that the distributed scheme is effective for the resource allocation and could protect the CUs with limited signaling overhead

Adaptive clutter filter design for micro-ultrasound color flow imaging of small blood vessels

In micro-ultrasound, which uses imaging frequencies above 20 MHz, obtainingcolor flow images (CFI) of small blood vessels using is not a trivial taskbecause it is more challenging to suppress tissue clutter properly given thestronger blood signal power at high imaging frequencies and the slow bloodvelocity inside the microcirculation. To improve clutter suppression inmicro-ultrasound CFI, this paper presents an adaptive clutter filtering approachthat is based on a two-stage eigen-analysis of slow-time ensemblecharacteristics. The approach first identifies tissue pixels in the imaging viewby examining whether high-frequency contents are absent in the principalslow-time eigen-components for each pixel as computed from single-ensembleeigen-decomposition. It then computes the filtered slow-time ensemble for eachpixel by finding the least-squares projection residual between the pixel'sslow-time ensemble and the clutter eigen-components estimated from amulti-ensemble eigen-decomposition of tissue slow-time ensembles within aspatial window. In this filtering approach, the clutter eigen-components arechosen based on whether their mean frequency lies within a spectral band. Toanalyze the efficacy of the proposed adaptive filter, both in-vitro experimentsand Field II simulations were carried out. For the experiments, raw CFI datawere acquired using a 64-element, 33 MHz linear array prototype (pulse duration:2 cycles, PRF: 1 kHz, transmit focus: 8mm, F-number: 5). Their imaging viewcorresponded to the cross-section of a 0.9mm-diameter tube that was placed ontop of an unsuspended table where ambient vibrations may appear; flow velocity(5, 7, 10, 15 mm/s) within the tube was controlled using a syringe pump. For thesimulations, raw CFI data was computed for both plug and parabolic flowprofiles, and tissue motion was modeled as 0.5 mm/s sinusoidal vibrations. Forall flow velocities tested in our in-vitro study, the proposed adaptive filterimproved the flow detection sensitivity as compared to existing ones. In theslow-flow case (5 mm/s), we observed over 70% increase in flow detectionsensitivity (assuming a 5% false alarm rate). This effectively reduced flashingartifacts in the resulting CFIs and gave a more consistent visualization of theflow tube. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA.,, 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1206-120

Design of a programmable micro-ultrasound research platform

To foster innovative uses of micro-ultrasound in biomedicine, it is beneficial to develop flexible research-purpose systems that allow researchers to easily reconfigure its system-level operations such as transmit firing sequence and receive processing. In this paper, we present the development of a programmable micro-ultrasound research platform that is capable of realizing various micro-imaging algorithms. The research platform comprises a linear-array-based scanning front-end and a PC-based data processing back-end, which employs a graphical processing unit (GPU) as the processor core. The front-end operations can be configured from the PC via the parallel port and the two blocks are synchronized by an external clock. Acquired data from the front-end is first digitized and relayed to the PC through an data acquisition card (200 MHz, 14-bit). They are then transferred to the GPU (GTX 275) in which the image formation is carried out via multi-thread processing. Results are displayed on-screen in real-time and can be saved to the PC's hard disk for offline analysis. Through a module-based programming approach, this platform can facilitate realization of custom-designed imaging algorithms developed by researchers. In this work, B-mode imaging and adaptive color flow imaging have been implemented as demonstrations of the research platform's programmability. The performance results show that real-time processing frame rates can be achieved for both imaging modes. © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA., 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1980-198

Multi-Objective Optimization for UAV-Assisted Wireless Powered IoT Networks Based on Extended DDPG Algorithm

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives

Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning

In this paper, a UAV-assisted wireless powered communication system for IoT network is studied. Specifically, the UAV performs as base station (BS) to collect the sensory information of the IoT devices as well as to broadcast energy signals to charge them. Considering the devices' limited data storage capacity and battery life, we propose a multi-objective optimization problem that aims to minimize the average data buffer length, maximize the residual battery level of the system and avoid data overflow and running out of battery of devices. Since the services requirements of IoT devices are dynamic and uncertain and the system can not be full observed by the UAV, it is challenging for UAV to achieve trajectory planning. In this regard, a deep Q network (DQN) is applied for UAV's flight control. Simulation results indicate that the DQN-based algorithm provides an efficient UAV's flight control policy for the proposed optimization problem

Effective Assessment of Power Standing Device to Adults with Permanent Lower Limb Paralysis

Introduction: Standing routine is a known beneficial daily activity for both healthy and disabled persons, especially those with permanent lower limb paralysis. However, the prescription of standing device for adults with permanent paralysis was inadequate and non-standard in existing local practice because of lack of good design and evidence based funding support. Objective: In view of the availability of new advances in power standing device, we aim to perform an effective health technology assessment (HTA) from professional and users perspectives to develop the decision pathway in prescription for long term home use. Methodology: A functional test and social cost analysis was performed on one high cost new standing mobile devices in recent market. A practical workshop and surveys were performed to collect feedback from 24 healthcare professionals and 8 expert users on a spectrum of new standing mobile device. Results: From the survey results, there was consensus among all participants that ‘Standing’ as daily routine at home is essential and beneficial. 62.5% of healthcare professionals would provide training to users and their cares to facilitate users to perform standing at home. Eight factors were identified from factor analysis in affecting the choice of standing devices for home use by healthcare professionals and users. Users scored high (mean=9.25/10) in “compliance with the new power standing mobile device”. The cost analysis showed considerable savings in social costs in using even the high-cost power standing mobile device. Discussion: The group welcomed power standing device with or without mobile function to support their standing activity at home. A possible clinical decision for prescribing different standing devices with identified factors was summarized. Conclusion: More recent researches have reported the negative health issues associated with prolonged sitting. With more innovative product designs, the power standing devices with or without mobile function is a new concept welcomed by both healthcare professionals and users in promotion of their health, preventing complications as well as independent living in home environment. A larger scale of HTA with structured cost-effectiveness analysis is essential to inform the healthcare resources planners