Design and Implementation of a Flexible and Memory Efficient Operating System for Sensor Nodes

Sensor network nodes exhibit characteristics of both embedded systems and general-purpose systems.A sensor network operating system is a kind of embedded operating system, but unlike a typical embedded operating system, sensor network operatin g system may not be real time, and is constrained by memory and energy constraints. Most sensor network operating systems are based on event-driven approach. Event-driven approach is efficient in terms of time and space.Also this approach does not require a separate stack for each execution context. But using this model, it is difficult to implement long running tasks, like cryptographic operations. A thread based computation requires a separate stack for each execution context, and is less efficient in terms of time and space. In this paper, we propose a thread based execution model that uses only a fixed number of stacks. In this execution model, the number of stacks at each priority level are fixed. It minimizes the stack requirement for multi-threading environment and at the same time provides ease of programming. We give an implementation of this model in Contiki OS by separating thread implementation from protothread implementation completely. We have tested our OS by implementing a clock synchronization protocol using it.

Optical microscopy methods for understanding learning and memory

Structural dynamics of dendritic spines is one of the key correlative measures of synaptic plasticity for encoding short-term and long-term memory. Optical studies of structural changes in brain tissue using confocal microscopy face difficulties of scattering. This results in low signal-to-noise ratio and thus limiting the imaging depth to few tens of microns. Multiphoton microscopy (MpM) overcomes this limitation by using low-energy photons to cause localized excitation and achieve high resolution in all three dimensions. Multiple low-energy photons with longer wavelengths minimize scattering and allow access to deeper brain regions at several hundred microns. In this article, we provide a basic understanding of the physical phenomena that give MpM an edge over conventional microscopy. Further, we highlight a few of the key studies in the field of learning and memory which would not have been possible without the advent of MpM.

A study on fear memory retrieval and REM sleep in maternal separation and isolation stressed rats

As rapid brain development occurs during the neonatal period, environmental manipulation during this period may have a significant impact on sleep and memory functions. Moreover, rapid eye movement (REM) sleep plays an important role in integrating new information with the previously stored emotional experience. Hence, the impact of early maternal separation and isolation stress (MS) during the stress hyporesponsive period (SHRP) on fear memory retention and sleep in rats were studied. The neonatal rats were subjected to maternal separation and isolation stress during postnatal days 5-7 (6 h daily/3 d). Polysomnographic recordings and differential fear conditioning was carried out in two different sets of rats aged 2 months. The neuronal replay during REM sleep was analyzed using different parameters. MS rats showed increased time in REM stage and total sleep period also increased. MS rats showed fear generalization with increased fear memory retention than normal control (NC). The detailed analysis of the local field potentials across different time periods of REM sleep showed increased theta oscillations in the hippocampus, amygdala and cortical circuits. Our findings suggest that stress during SHRP has sensitized the hippocampus amygdala cortical loops which could be due to increased release of corticosterone that generally occurs during REM sleep. These rats when subjected to fear conditioning exhibit increased fear memory and increased, fear generalization. The development of helplessness, anxiety and sleep changes in human patients, thus, could be related to the reduced thermal, tactile and social stimulation during SHRP on brain plasticity and fear memory functions. (C) 2014 Elsevier B.V. All rights reserved.

Co-Exploration of NLA Kernels and Specification of Compute Elements in Distributed Memory CGRAs

Coarse Grained Reconfigurable Architectures (CGRA) are emerging as embedded application processing units in computing platforms for Exascale computing. Such CGRAs are distributed memory multi- core compute elements on a chip that communicate over a Network-on-chip (NoC). Numerical Linear Algebra (NLA) kernels are key to several high performance computing applications. In this paper we propose a systematic methodology to obtain the specification of Compute Elements (CE) for such CGRAs. We analyze block Matrix Multiplication and block LU Decomposition algorithms in the context of a CGRA, and obtain theoretical bounds on communication requirements, and memory sizes for a CE. Support for high performance custom computations common to NLA kernels are met through custom function units (CFUs) in the CEs. We present results to justify the merits of such CFUs.

Memory-efficient spatial prediction image compression scheme

In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates the predicted error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. In quantization phase, we used a modified SPIHT algorithm to achieve efficiency in memory requirements. The memory constraint plays a vital role in wireless and bandwidth-limited applications. A single reusable list is used instead of three continuously growing linked lists as in case of SPIHT. This method is error resilient. The performance is measured in terms of PSNR and memory requirements. The algorithm shows good compression performance and significant savings in memory. (C) 2006 Elsevier B.V. All rights reserved.

The third post-Newtonian gravitational wave polarizations and associated spherical harmonic modes for inspiralling compact binaries in quasi-circular orbits

The gravitational waveform (GWF) generated by inspiralling compact binaries moving in quasi-circular orbits is computed at the third post-Newtonian (3PN) approximation to general relativity. Our motivation is two-fold: (i) to provide accurate templates for the data analysis of gravitational wave inspiral signals in laser interferometric detectors; (ii) to provide the associated spin-weighted spherical harmonic decomposition to facilitate comparison and match of the high post-Newtonian prediction for the inspiral waveform to the numerically-generated waveforms for the merger and ringdown. This extension of the GWF by half a PN order (with respect to previous work at 2.5PN order) is based on the algorithm of the multipolar post-Minkowskian formalism, and mandates the computation of the relations between the radiative, canonical and source multipole moments for general sources at 3PN order. We also obtain the 3PN extension of the source multipole moments in the case of compact binaries, and compute the contributions of hereditary terms (tails, tails-of-tails and memory integrals) up to 3PN order. The end results are given for both the complete plus and cross polarizations and the separate spin-weighted spherical harmonic modes.

Lenient Execution and Concurrent Execution of Re-entrant Routines: Efficient Implementation in Data Flow Systems

Implementation details of efficient schemes for lenient execution and concurrent execution of re-entrant routines in a data flow model have been discussed in this paper. The proposed schemes require no extra hardware support and utilise the existing hardware resources such as the Matching Unit and Memory Network Interface, effectively to achieve the above mentioned goals.

Calcium/Calmodulin Dependent Protein Kinase II Bound to NMDA Receptor 2B Subunit Exhibits Increased ATP Affinity and Attenuated Dephosphorylation

Calcium/calmodulin dependent protein kinase II (CaMKII) is implicated to play a key role in learning and memory. NR2B subunit of N-methyl-D-aspartate receptor (NMDAR) is a high affinity binding partner of CaMKII at the postsynaptic membrane. NR2B binds to the T-site of CaMKII and modulates its catalysis. By direct measurement using isothermal titration calorimetry (ITC), we show that NR2B binding causes about 11 fold increase in the affinity of CaMKII for ATP gamma S, an analogue of ATP. ITC data is also consistent with an ordered binding mechanism for CaMKII with ATP binding the catalytic site first followed by peptide substrate. We also show that dephosphorylation of phospho-Thr(286)-alpha-CaMKII is attenuated when NR2B is bound to CaMKII. This favors the persistence of Thr(286) autophosphorylated state of CaMKII in a CaMKII/phosphatase conjugate system in vitro. Overall our data indicate that the NR2B- bound state of CaMKII attains unique biochemical properties which could help in the efficient functioning of the proposed molecular switch supporting synaptic memory.

Alternating current conduction and impedance spectroscopy analysis on pulsed excimer laser ablated (Pb, La)TiO3 thin films

Lanthanum doped lead titanate (PLT) thin films were identified as the most potential candidates for the pyroelectric and memory applications. PLT thin films were deposited on Pt coated Si by excimer laser ablation technique. The polarization behavior of PLT thin films has been studied over a temperature range of 300 K to 550 K. A universal power law relation was brought into picture to explain the frequency dependence of ac conductivity. At higher frequency region ac conductivity of PLT thin films become temperature independent. The temperature dependence of ac conductivity and the relaxation time is analyzed in detail. The activation energy obtained from the ac conductivity was attributed to the shallow trap controlled space charge conduction in the bulk of the sample. The impedance analysis for PLT thin films were also performed to get insight of the microscopic parameters, like grain, grain boundary, and film-electrode interface etc. The imaginary component of impedance Z" exhibited different peak maxima at different temperatures. Different types of mechanisms were analyzed in detail to explain the dielectric relaxation behavior in the PLT thin films.

On-Chip Memory Architecture Exploration Framework for DSP Processor-Based Embedded System on Chip

Today's SoCs are complex designs with multiple embedded processors, memory subsystems, and application specific peripherals. The memory architecture of embedded SoCs strongly influences the power and performance of the entire system. Further, the memory subsystem constitutes a major part (typically up to 70%) of the silicon area for the current day SoC. In this article, we address the on-chip memory architecture exploration for DSP processors which are organized as multiple memory banks, where banks can be single/dual ported with non-uniform bank sizes. In this paper we propose two different methods for physical memory architecture exploration and identify the strengths and applicability of these methods in a systematic way. Both methods address the memory architecture exploration for a given target application by considering the application's data access characteristics and generates a set of Pareto-optimal design points that are interesting from a power, performance and VLSI area perspective. To the best of our knowledge, this is the first comprehensive work on memory space exploration at physical memory level that integrates data layout and memory exploration to address the system objectives from both hardware design and application software development perspective. Further we propose an automatic framework that explores the design space identifying 100's of Pareto-optimal design points within a few hours of running on a standard desktop configuration.

Ferrimagnetism, antiferromagnetism, and magnetic frustration in La2-xSrxCuRuO6 (0 <= x <= 1)

We studied structural and magnetic properties of a series of insulating double perovskite compounds, La2-xSrxCuRuO6 (0 <= x <= 1), representing doping via A-site substitution. The end members La2CuRuO6 and LaSrCuRuO6 form in monoclinic structure while the intermediate Sr doped compounds stabilize in triclinic structure. The Cu and Ru ions sit on alternate B sites of the perovskite lattice with similar to 15% antisite defects in the undoped sample while the Sr-doped samples show a tendency to higher ordering at B sites. The undoped (x = 0) compound shows a ferrimagnetic-like behavior at low temperatures. In surprising contrast to the usual expectation of an enhancement of ferromagnetic interaction on doping, an antiferromagnetic-like ground state is realized for all doped samples (x > 0). Heat capacity measurements indicate the absence of any long-range magnetic order in any of these compounds. The magnetic relaxation and memory effects observed in all compounds suggest glassy dynamical properties associated with magnetic disorder and frustration. We show that the observed magnetic properties are dominated by the competition between the nearest-neighbor Ru-O-Cu 180 degrees superexchange interaction and the next-nearest-neighbor Ru-O-O-Ru 90 degrees superexchange interaction as well as by the formation of antisite defects with interchanged Cu and Ru positions. Our calculated exchange interaction parameters from first principles calculations for x = 0 and x = 1 support this interpretation.

Graphene-MoS2 hybrid structures for multifunctional photoresponsive memory devices

Combining the electronic properties of graphene(1,2) and molybdenum disulphide (MoS2)(3-6) in hybrid heterostructures offers the possibility to create devices with various functionalities. Electronic logic and memory devices have already been constructed from graphene-MoS2 hybrids(7,8), but they do not make use of the photosensitivity of MoS2, which arises from its optical-range bandgap(9). Here, we demonstrate that graphene-on-MoS2 binary heterostructures display remarkable dual optoelectronic functionality, including highly sensitive photodetection and gate-tunable persistent photoconductivity. The responsivity of the hybrids was found to be nearly 1 x 10(10) A W-1 at 130 K and 5 x 10(8) A W-1 at room temperature, making them the most sensitive graphene-based photodetectors. When subjected to time-dependent photoillumination, the hybrids could also function as a rewritable optoelectronic switch or memory, where the persistent state shows almost no relaxation or decay within experimental timescales, indicating near-perfect charge retention. These effects can be quantitatively explained by gate-tunable charge exchange between the graphene and MoS2 layers, and may lead to new graphene-based optoelectronic devices that are naturally scalable for large-area applications at room temperature.

Energy Efficient Memory Decoder Design for Ultra-Low Voltage Systems

This paper presents a low energy memory decoder architecture for ultra-low-voltage systems containing multiple voltage domains. Due to limitations in scalability of memory supply voltages, these systems typically contain a core operating at subthreshold voltages and memories operating at a higher voltage. This difference in voltage provides a timing slack on the memory path as the core supply is scaled. The paper analyzes the feasibility and trade-offs in utilizing this timing slack to operate a greater section of memory decoder circuitry at the lower supply. A 256x16-bit SRAM interface has been designed in UMC 65nm low-leakage process to evaluate the above technique with the core and memory operating at 280 mV and 500 mV respectively. The technique provides a reduction of up to 20% in energy/cycle of the row decoder without any penalty in area and system-delay.

Deep Packet Inspection Using Message Passing Networks

We propose a solution based on message passing bipartite networks, for deep packet inspection, which addresses both speed and memory issues, which are limiting factors in current solutions. We report on a preliminary implementation and propose a parallel architecture.

Efficient computation of volume fractions for multi-material cell complexes in a grid by slicing

The paper presents a novel slicing based method for computation of volume fractions in multi-material solids given as a B-rep whose faces are triangulated and shared by either one or two materials. Such objects occur naturally in geoscience applications and the said computation is necessary for property estimation problems and iterative forward modeling. Each facet in the model is cut by the planes delineating the given grid structure or grid cells. The method, instead of classifying the points or cells with respect to the solid, exploits the convexity of triangles and the simple axis-oriented disposition of the cutting surfaces to construct a novel intermediate space enumeration representation called slice-representation, from which both the cell containment test and the volume-fraction computation are done easily. Cartesian and cylindrical grids with uniform and non-uniform spacings have been dealt with in this paper. After slicing, each triangle contributes polygonal facets, with potential elliptical edges, to the grid cells through which it passes. The volume fractions of different materials in a grid cell that is in interaction with the material interfaces are obtained by accumulating the volume contributions computed from each facet in the grid cell. The method is fast, accurate, robust and memory efficient. Examples illustrating the method and performance are included in the paper.