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

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

BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

An adaptive ensemble approach to ambient intelligence assisted people search

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks

Virulence of H5N1 virus in mice attenuates after in vitro serial passages

The virulence of A/Vietnam/1194/2004 (VN1194) in mice attenuated after serial passages in MDCK cells and chicken embryos, because the enriched large-plaque variants of the virus had significantly reduced virulence. In contrast, the small-plaque variants of the virus and the variants isolated from the brain of mice that were infected with the parental virus VN1194 had much higher virulence in mice. The virulence attenuation of serially propagated virus may be caused by the reduced neurotropism in mice. Our whole genome sequence analysis revealed substitutions of a total of two amino acids in PB1, three in PB2, two in PA common for virulence attenuated variants, all or part of which may be correlated with the virulence attenuation and reduced neurotropism of the serially propagated VN1194 in mice. Our study indicates that serial passages of VN1194 in vitro lead to adaptation and selection of variants that have markedly decreased virulence and neurotropism, which emphasizes the importance of direct analysis of original or less propagated virus samples

Control of spatially homogeneous distribution of heteroatoms to produce red TiO2 photocatalyst for visible-light photocatalytic water splitting

The authors thank National Natural Science Fundation of China (Nos. 51825204, 51572266, 21633009, 51629201), the Major Basic Research Program, Ministry of Science and Technology of China (2014CB239401), the Key Research Program of Frontier Sciences CAS (QYZDB-SSW-JSC039) for the financial support. G. L. is grateful for the award of the Newton Advanced Fellowship.The strong band-to-band absorption of photocatalysts spanning the whole visible light region (400-700 nm) is critically important for solar-driven photocatalysis. Although it is actively and widely used as photocatalyst for various reactions in the past four decades, TiO2 has a very poor ability to capture the whole-spectrum visible light. Here, by controlling the spatially homogeneous distribution of boron and nitrogen heteroatoms in anatase TiO2 microspheres with a predominance of high-energy {001} facets, a strong visible light absorption spectrum with a sharp edge beyond 680 nm is achieved. The red TiO2 with the homogeneous doping of boron and nitrogen obtained shows no increase in defects like Ti3+ that are commonly observed in doped TiO2. More importantly, it has the ability to induce photocatalytic water oxidation to produce oxygen under the irradiation of visible light beyond 550 nm and also photocatalytic reducing water to produce hydrogen under visible light. These results demonstrate the great promise of using the red TiO2 for visible light photocatalytic water splitting and also provide an attractive strategy for realizing the wide-spectrum visible light absorption of wide-bandgap oxide photocatalysts.PostprintPeer reviewe

VIMA: General Robot Manipulation with Multimodal Prompts

Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens. Accordingly, we develop a new simulation benchmark that consists of thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and a four-level evaluation protocol for systematic generalization. We design a transformer-based robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. VIMA features a recipe that achieves strong model scalability and data efficiency. It outperforms alternative designs in the hardest zero-shot generalization setting by up to $2.9\times$ task success rate given the same training data. With $10\times$ less training data, VIMA still performs $2.7\times$ better than the best competing variant. Code and video demos are available at https://vimalabs.github.io/Comment: ICML 2023 Camera-ready version. Project website: https://vimalabs.github.io