369 research outputs found

    An innovative two-stage data compression scheme using adaptive block merging technique

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    Test data has increased enormously owing to the rising on-chip complexity of integrated circuits. It further increases the test data transportation time and tester memory. The non-correlated test bits increase the issue of the test power. This paper presents a two-stage block merging based test data minimization scheme which reduces the test bits, test time and test power. A test data is partitioned into blocks of fixed sizes which are compressed using two-stage encoding technique. In stage one, successive blocks are merged to retain a representative block. In stage two, the retained pattern block is further encoding based on the existence of ten different subcases between the sub-block formed by splitting the retained pattern block into two halves. Non-compatible blocks are also split into two sub-blocks and tried for encoded using lesser bits. Decompression architecture to retrieve the original test data is presented. Simulation results obtained corresponding to different ISCAS′89 benchmarks circuits reflect its effectiveness in achieving better compression

    Test Stimuli Segmentation and Coding Method

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    Test vector coding and data transmission are the key technologies in the design-for-test of digital integrated circuits (IC). Existing parallel input methods of test stimuli can reduce test application times; however, they need to occupy multiple input ports. Thus, a novel method of test stimuli coding and data transmission was proposed to reduce the test application time of the test vectors and reduce the number of input ports required for the parallel input of test stimuli. This method was based on the segmentation of test stimuli. First, the test stimuli were evenly segmented into eight-bit wide. Second, the eight-bit data of each segment were encoded to the five-bit data according to the compatibility between the test data of each segment. The eight-bit test stimuli input can be completed in one or two clock cycles of automatic test equipment (ATE) by using the five input ports of the chip. The corresponding decoding circuit was added inside the netlist of the circuit to realize the rapid input of the test stimuli. Lastly, the ISCAS\u2789 benchmark circuit was used to conduct experiments, results of this coding method were then compared with those of the serial input method. Results show that the encoding method proposed in this study can save an average of 37% of the parallel input data width and 81.7% of the test stimuli input time. The proposed method in this study can also reduce the test application time and the cost of the IC test. The findings of this study can provide guidance for improving the scan testing method of digital IC

    PCM telemetry data compression study, phase 1 Final report, 15 Sep. 1964 - 15 Aug. 1965

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    Pulse Code Modulation /PCM/ telemetry data compression study using S-6 Explorer XVII DAT

    Efficient compression of motion compensated residuals

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Multiprocessing techniques for unmanned multifunctional satellites Final report,

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    Simulation of on-board multiprocessor for long lived unmanned space satellite contro

    State-of-the-Art Sensors Technology in Spain 2015: Volume 1

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    This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

    SRML: Space Radio Machine Learning

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    Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft\u27s operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector\u27s electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference

    Highly efficient low-level feature extraction for video representation and retrieval.

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    PhDWitnessing the omnipresence of digital video media, the research community has raised the question of its meaningful use and management. Stored in immense multimedia databases, digital videos need to be retrieved and structured in an intelligent way, relying on the content and the rich semantics involved. Current Content Based Video Indexing and Retrieval systems face the problem of the semantic gap between the simplicity of the available visual features and the richness of user semantics. This work focuses on the issues of efficiency and scalability in video indexing and retrieval to facilitate a video representation model capable of semantic annotation. A highly efficient algorithm for temporal analysis and key-frame extraction is developed. It is based on the prediction information extracted directly from the compressed domain features and the robust scalable analysis in the temporal domain. Furthermore, a hierarchical quantisation of the colour features in the descriptor space is presented. Derived from the extracted set of low-level features, a video representation model that enables semantic annotation and contextual genre classification is designed. Results demonstrate the efficiency and robustness of the temporal analysis algorithm that runs in real time maintaining the high precision and recall of the detection task. Adaptive key-frame extraction and summarisation achieve a good overview of the visual content, while the colour quantisation algorithm efficiently creates hierarchical set of descriptors. Finally, the video representation model, supported by the genre classification algorithm, achieves excellent results in an automatic annotation system by linking the video clips with a limited lexicon of related keywords
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