A new energy harvest system with a hula-hoop transformer, micro-generator and interface energy-harvesting circuit

This study presents a synthesis of a new energy harvest system that consists of a hula-hoop transformer, a micro-generator and an interface energy harvest circuit. The hula-hoop transformer mainly comprises a main mass sprung in one translational direction and a free-moving mass attached at one end of a rod, the other end of which is hinged onto the main mass. The transformer is capable of transforming linear reciprocating motions to rotary ones based on the concepts similar to the hula hoop motions. The transformer is subsequently integrated with a miniaturized rotary generator in size of 10?×?10?×?2?mm³ and its compact energy harvest circuit chip. The designed generator consists of patterned planar copper coils and a multi-polar hard magnet ring made of NdFeB. The genetic algorithm (GA) is next applied to optimize the critical dimensions of the miniaturized generator. The optimized generator offers 4.5 volt and 7.23?mW in rms at 10,000?rpm. With micro-generator successfully fabricated, a novel energy harvest circuit employing a new dual phase charge pump, power management circuit, a low dropout regulator and battery charger is designed and fabricated via the 0.35???m process. This charge pump circuit owns the merit of automatic conversion of low-power AC signals by the micro-generator to DC ones. Experiments were conducted to show the favorable performance of the proposed energy harvest system. This is the first work that invents a motion transformer from ubiquitous reciprocating to rotational motions. In this way, higher-efficient energy conversion via compact-sized rotational electromagnetic generators can be realized as opposed to popular piezoelectric structures.     

Moment Matched Gaussian Kernel and Region Representative Likelihood for Performance Improvement of PMF-based TRN

This paper proposes two techniques to improve the performance of Point Mass Filter (PMF) based Terrain Referenced Navigation (TRN). The first is the kernel     

Magnetic landmark-based position correction technique for mobile robots with hall sensors

We propose a precise position error compensation and low-cost relative localization method in structured environments using magnetic landmarks and hall sensors. The proposed methodology can solve the problem of fine localization as well as global localization by tacking landmarks or by utilizing various patterns of magnetic landmark arrangement. In this paper, we consider two patterns of implanted permanent magnets on the surface, namely, at each vertex of regular triangles or rectangles on a flat surface. We show that the rectangular configuration of the permanent magnetic bars is better for a robust localization under sensor noise. For the experiments, permanent magnet sets in rectangular configuration are placed on the floor as landmarks at regular intervals, and magnetic hall sensors are installed at the bottom of a mobile robot. In our implementation, the accuracy after the error compensation is less than 1 mm in the position and less than 1° in the orientation. Due to the low cost and accuracy of the proposed methodology, it would be one of the practical solutions to the pose error correction of a mobile robot in structured environments.     

Transformation of a thin-walled solid model into a surface model via solid deflation

A simplified geometric model with lower dimensionality, such as a mid-surface model, is often preferred over a detailed solid model for the analysis process, if the analysis results are not seriously impacted. In order to derive a mid-surface model from a thin-walled solid model, in this paper, we propose a novel approach called the solid deflation method. In this method, a solid model is assumed to be created by using air to inflate a shell that comprises the surface of the solid model. First, the model is simplified by the removal of any detailed features whose absence would not alter its overall shape. Next, the solid model itself can be converted into a degenerate solid model with zero thickness. Finally, a surface model is generated by splitting large faces paired in the thinned solid model, selecting one face per pair for creating a sheet model, and sewing the selected faces. Using this method, a more practical and usable mid-surface model can be very efficiently generated from a solid model because it can circumvent not only the tedious trimming and extension processes of the medial axis transformation method but also the time-consuming patch joining process of the mid-surface abstraction approach.     

Detecting pattern-based outliers

Outlier detection targets those exceptional data that deviate from the general pattern. Besides high density clustering, there is another pattern called low density regularity. Thus, there are two types of outliers w.r.t. them. We propose two techniques: one to identify the two patterns and the other to detect the corresponding outliers.     

Semantic passage segmentation based on sentence topics for question answering

We propose a semantic passage segmentation method for a Question Answering (QA) system. We define a semantic passage as sentences grouped by semantic coherence, determined by the topic assigned to individual sentences. Topic assignments are done by a sentence classifier based on a statistical classification technique, Maximum Entropy (ME), combined with multiple linguistic features. We ran experiments to evaluate the proposed method and its impact on application tasks, passage retrieval and template-filling for question answering. The experimental result shows that our semantic passage retrieval method using topic matching is more useful than fixed length passage retrieval. With the template-filling task used for information extraction in the QA system, the value of the sentence topic assignment method was reinforced.     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Distributed filtering and synchronization of diffusively state‐coupled heterogeneous systems

This paper studies distributed filtering‐based ssynchronization of diffusively state‐coupled heterogeneous systems. For given heterogeneous subsystems and a network topology, sufficient conditions for the filtering‐based synchronization are developed with a guaranteed performance. The estimation and synchronization error dynamics are obtained in a decoupled form, and it is shown that the filter and the controller can be designed separately by LMIs. The feasibility of the proposed design method using LMIs is discussed, and the main results are validated through examples with various setup. Copyright © 2016 John Wiley & Sons, Ltd.