CMOS analog map decoder for (8,4) hamming code

Journal ArticleAbstract-Design and test results for a fully integrated translinear tail-biting MAP error-control decoder are presented. Decoder designs have been reported for various applications which make use of analog computation, mostly for Viterbi-style decoders. MAP decoders are more complex, and are necessary components of powerful iterative decoding systems such as Turbo codes. Analog circuits may require less area and power than digital implementations in high-speed iterative applications. Our (8, 4) Hamming decoder, implemented in an AMI 0.5- m process, is the first functioning CMOS analog MAP decoder. While designed to operate in subthreshold, the decoder also functions above threshold with a small performance penalty. The chip has been tested at bit rates up to 2 Mb/s, and simulations indicate a top speed of about 10 Mb/s in strong inversion. The decoder circuit size is 0.82 mm2, and typical power consumption is 1 mW at 1 Mb/s

Analog decoding of product codes

Journal ArticleAbstract - A method is presented for analog softdecision decoding of block product codes (block turbo codes). Extrinsic information is exchanged as analog signals between component row and column decoders. The component MAP decoders use low-power analog computation in subthreshold CMOS circuits to implement the sum-product algorithm. An example decoder design is presented for a (16,ll)? Hamming code

Analog MAP decoder for (8, 4) hamming code in subthreshold CMOS

Journal ArticleAn all-MOS analog implementation of a MAP decoder is presented for the (8, 4) extended Hamming code. This paper describes the design and analysis of a tail-biting trellis decoder implementation using subthreshold CMOS devices. A VLSI test chip has recently returned from fabrication, and preliminary test results indicate accurate decoding up to 20 MBit/s

Analog MAP decoder for (8, 4) hamming code in subthreshold CMOS

Journal ArticleAbstract - An all-MOS analog tail-biting MAP decoder is presented for an (8,4) Hamming code. The decoder implements a probability propagation algorithm using subthreshold CMOS networks. Physical results verify the expected behavior of the decoderand demonstrate robustness of analog decoding circuits

Analog decoding of product codes

Journal ArticleA design approach is presented for soft-decision decoding of block product codes ("block turbo codes") using analog computation with MOS devices. Application of analog decoding to large code sizes is also considered with the introduction of serial analog interfaces and pipeline schedules

Muller C-element based Decoder (MCD): A Decoder Against Transient Faults

This work extends the analysis and application of a digital error correction method called Muller C-element Decoding (MCD), which has been proposed for fault masking in logic circuits comprised of unreliable elements. The proposed technique employs cascaded Muller C-elements and XOR gates to achieve efficient error-correction in the presence of internal upsets. The error-correction analysis of MCD architecture and the investigation of C-element’s robustness are first introduced. We demonstrate that the MCD is able to produce error-correction benefit in a high error-rate of internal faults. Significantly, for a (3,6) short-length LDPC code, when the decoding process is internally error-free the MCD achieves also a gain in terms of decoding performance by comparison to the well-known Gallager Bit-Flipping method. We further consider application of MCD to a general-purpose fault-tolerant model, coded Dual Modular Redundancy (cDMR), which offers low-redundancy error-resilience for contemporary logic systems as well as future nanoeletronic architectures

Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

The primary energy consumer of smart buildings are Heating, Ventilation, and Air-Conditioning (HVAC) systems, approximately 30% of the building energy use, which usually operate on a fixed schedule. Currently, most modern buildings still condition rooms with a set-point assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Occupancy-based controls can achieve significant energy savings by temporally matching the building energy consumption and building usage, conservative user behavior can save a third of expended energy.  In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, three techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we propose a method based on uncertain basis functions for modeling and prediction purposes. In literature, basis functions (e.g., radial basis functions, wavelets) are fixed; instead, we assume that the basis functions are random. We consider basis functions with three different distributions, which are Gaussian, Laplace and Uniform, respectively. Finally, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for HVAC control strategy.  To demonstrate effectiveness of proposed approach, a simulation-based experimental analysis is carried out using occupancy data. We define the estimation accuracy as the total number of correct estimations divided by the total number of estimations, and both Root Mean Squared Error (RMSE) and estimation accuracy analysis are provided. All the proposed estimation techniques could achieve at least 70% accuracy rate. Generally, accuracy for binary states estimation is much higher than that of detailed occupancy. For GSPS model, more training data improves performance of estimation. It should be remarked that although some mismatch exist for non-zero jumps, estimation performance tracks the zero base line (non-occupied status) perfectly. Therefore, the estimation techniques are effective for binary estimation with over 90% accuracy. Finally, the estimated occupancy is applied into temperature set algorithm to generate reference temperature curve. By adjusting temperature set curve, we can achieve significant energy without sacrificing customer’s comfort.  In this paper, we propose three real-time occupancy estimation methods that can be incorporated into HVAC controls . We have shown the effectiveness of all the proposed approaches by simulation examples. We have seen great potential of energy saving by integrating the proposed technique into real HVAC control system.   Â

Multi-campaign Ship and Aircraft Observations of Marine Cloud Condensation Nuclei and Droplet Concentrations

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP2EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosol-cloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates

Measurement report: Cloud and environmental properties associated with aggregated shallow marine cumulus and cumulus congestus

Marine clouds are found to clump together in regions or lines, readily discernible from satellite images of the ocean. While clustering is also a feature of deep storm clouds, we focus on smaller cloud systems associated with fair weather and brief localized showers. Two aircraft sampled the region around these shallow systems: one incorporated measurements taken within, adjacent to, and below the clouds, while the other provided a survey from above using remote sensing techniques