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Register a new userBinarySDG: binary sensor data generation with R
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Added by
Marco Piangerelli
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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The scarcity of Smart Home data is still a pretty big problem, and in a world where the size of a dataset can often make the difference between a poor performance and a good performance for problems related to machine learning projects, this needs to be resolved. But whereas the problem of retrieving real data cant really be resolved, as most of the time the process of installing sensors and retrieving data can be found to be really expensive and time-consuming, we need to find a faster and easier solution, which is where synthetic data comes in. Here we propose BinarySDG (Binary Synthetic Data Generator) as a flexible and easy way to generate synthetic data for binary sensors.
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Public space utilization is crucial for urban developers to understand how efficient a place is being occupied in order to improve existing or future infrastructures. In a smart cities approach, implementing public space monitoring with Internet-of-Things (IoT) sensors appear to be a viable solution. However, choice of sensors often is a challenging problem and often linked with scalability, coverage, energy consumption, accuracy, and privacy. To get the most from low cost sensor with aforementioned design in mind, we proposed data processing modules for capturing public space utilization with Renewable Wireless Sensor Network (RWSN) platform using pyroelectric infrared (PIR) and analog sound sensor. We first proposed a calibration process to remove false alarm of PIR sensor due to the impact of weather and environment. We then demonstrate how the sounds sensor can be processed to provide various insight of a public space. Lastly, we fused both sensors and study a particular public space utilization based on one month data to unveil its usage.
The purpose of this paper is to apply characteristics of residual stress that causes cantilever beams to bend for manufacturing a micro-structured gas flow sensor. This study uses a silicon wafer deposited silicon nitride layers, reassembled the gas flow sensor with four cantilever beams that perpendicular to each other and manufactured piezoresistive structure on each micro-cantilever by MEMS technologies, respectively. When the cantilever beams are formed after etching the silicon wafer, it bends up a little due to the released residual stress induced in the previous fabrication process. As air flows through the sensor upstream and downstream beam deformation was made, thus the airflow direction can be determined through comparing the resistance variation between different cantilever beams. The flow rate can also be measured by calculating the total resistance variations on the four cantilevers.
Overheating has been acknowledged as a major issue in testing complex SOCs. Several power constrained system-level DFT solutions (power constrained test scheduling) have recently been proposed to tackle this problem. However, as it will be shown in this paper, imposing a chip-level maximum power constraint doesnt necessarily avoid local overheating due to the non-uniform distribution of power across the chip. This paper proposes a new approach for dealing with overheating during test, by embedding thermal awareness into test scheduling. The proposed approach facilitates rapid generation of thermal-safer test schedules without requiring time-consuming thermal simulations. This is achieved by employing a low-complexity test session thermal model used to guide the test schedule generation algorithm. This approach reduces the chances of a design re-spin due to potential overheating during test.
This paper reports on our research in developing a micro power generation system based on gas turbine engine and piezoelectric converter. The micro gas turbine engine consists of a micro combustor, a turbine and a centrifugal compressor. Comprehensive simulation has been implemented to optimal the component design. We have successfully demonstrated a silicon-based micro combustor, which consists of seven layers of silicon structures. A hairpin-shaped design is applied to the fuel/air recirculation channel. The micro combustor can sustain a stable combustion with an exit temperature as high as 1600 K. We have also successfully developed a micro turbine device, which is equipped with enhanced micro air-bearings and driven by compressed air. A rotation speed of 15,000 rpm has been demonstrated during lab test. In this paper, we will introduce our research results major in the development of micro combustor and micro turbine test device.
The GDMC AI settlement generation challenge is a PCG competition about producing an algorithm that can create an interesting Minecraft settlement for a given map. This paper contains a collection of written experiences with this competition, by participants, judges, organizers and advisors. We asked people to reflect both on the artifacts themselves, and on the competition in general. The aim of this paper is to offer a shareable and edited collection of experiences and qualitative feedback - which seem to contain a lot of insights on PCG and computational creativity, but would otherwise be lost once the output of the competition is reduced to scalar performance values. We reflect upon some organizational issues for AI competitions, and discuss the future of the GDMC competition.
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