Design of a multiple bloom filter for distributed navigation routing

Unmanned navigation of vehicles and mobile robots can be greatly simplified by providing environmental intelligence with dispersed wireless sensors. The wireless sensors can work as active landmarks for vehicle localization and routing. However, wireless sensors are often resource scarce and require a resource-saving design. In this paper, a multiple Bloom-filter scheme is proposed to compress a global routing table for a wireless sensor. It is used as a lookup table for routing a vehicle to any destination but requires significantly less memory space and search effort. An error-expectation-based design for a multiple Bloom filter is proposed as an improvement to the conventional false-positive-rate-based design. The new design is shown to provide an equal relative error expectation for all branched paths, which ensures a better network load balance and uses less memory space. The scheme is implemented in a project for wheelchair navigation using wireless camera motes. © 2013 IEEE

Improvement of tofu texture in China

Nowadays, in China, the problem of protein inadequacy still exists. To solve this problem,low cost, good quality vegetable proteins are considered to be one of the best choices. Amongthem, as a good vegetable protein resource, soybean has aroused the attention of manyresearchers. Tofu, a kind of soybean product, was invented in ancient time in China andnowadays, it is still a popular daily dish in some Asian countries like China, Japan, Korea, etcand is considered as a good vegetable protein resource. For the traditional food culture and thestyle of Chinese dishes, Chinese prefer hard tofu. At present, the production of tofu is mainlydone by small-scale factories with handmade way. Such kind of factories can produce hardtofu meeting the palatability of Chinese consumers. But, the shelf life of tofu is very short, insummer, only half a day, which limits the distribution of tofu. At the same time, the labordensity is higher, the quality control of tofu is mainly by experiences and a uniform quality isdifficult to be realized. In 1980s’, some industrial lines were imported. But, such kind ofindustrial production line uses glucono-delta-lactone (GDL) as coagulant. Tofu produced bythis way is somewhat acidic, which is not preferred by consumers, and the texture of tofu isvery soft, which is not suitable for most of the Chinese dishes. Lack of information onproducing hard tofu by industrial line limit the industrial production of hard tofu in China.Based on the present situation of hard tofu production in China, this study takes theimprovement of tofu texture in China as the objective.To improve the texture of tofu, firstly, a proper evaluation method for the texture of tofu isnecessary. Stress-strain, stress relaxation and creep tests were conducted. Results showed thatparameters from stress relaxation and creep behaviors analyzed by using viscoelastic models(taking Maxwell model, serialized by a Spring model and Dashpot model, and Vigot model,paralleled by a Spring model and a Dashpot model as primary models) can reflect both theviscous and elastic changes of tofu with different coagulants and protein concentrations. Itwas suggested that this viscoelastic evaluation method was effective for the texture evaluationof tofu.After determining the proper texture evaluation method for tofu, in this study, the effects ofsoymilk concentration (protein) concentration, types and concentration of different coagulants(CaSO4, GDL, MgCl2) on the texture of tofu and the effects of okara addition on the texture oftofu were investigated. For the improvement of tofu texture in China, from the view of proteinconcentration, types and concentration of coagulants, the addition of okara, followingconclusions were drawn:(1) A higher soymilk concentration (protein concentration) can lead to a harder tofu. Theviscosity of soymilk also increased with increasing the protein concentration. A too highviscosity might cause the difficulties of pipe transportation. In industrial practice, thebalance between protein concentration and a proper viscosity should be considered.(2) Different coagulants showed different properties to the tofu production. For CaSO4 andGDL, increasing the amount of coagulant resulted in a harder tofu. MgCl2 showed anarrow range of optimal concentrations and too high concentration of MgCl2 caused aductile break of tofu. When at the same concentration of protein, GDL and MgCl2 caninduce harder tofu than CaSO4. The reaction of MgCl2 with soy protein is rapider thanthat of GDL and CaSO4.(3) It is possible to add paste-like okara to soymilk for the production of tofu. At a certainrange, the texture of tofu was not significantly affected by the addition of okara. CaSO4showed different response to the addition of okara comparing with GDL. 15% addition ofokara increased the break stress of CaSO4-coagulated tofu while the addition of okaradecreased the break stress of GDL-tofu.Based on the results in this study, it can be suggested that for industrial hard tofuproduction in China, using properly high concentration of soymilk, mixture of coagulants,such as CaSO4 with MgCl2, GDL with MgCl2, increasing the concentration of coagulants likeCaSO4 and GDL and adding okara can be reasonable alternatives.Thesis (Ph. D. in Agriculture)--University of Tsukuba, (A), no. 3531, 2004.3.25Includes bibliographical reference

A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accurac