Dynamic reconfigurable multispectral imaging microscope

<div class="section abstract"><div class="htmlview paragraph">As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS. Typical results on a notional hybrid ground vehicle power system are presented to illustrate: 1) mission tasks power requirements and respective priority assignments; and 2) roles of situational awareness and dynamic reconfiguration layer in the operation of the proposed integrated energy management and control framework.</div></div>

Programming distributed applications in the IoT-edge environment is a cumbersome challenge. Developers are expected to seamlessly handle issues in dynamic reconfiguration, routing, state management, fault tolerance, and heterogeneous device capabilities. This demo presents DDFLOW, a macroprogramming abstraction and accompanying runtime that offers appropriate distributed system tooling to properly isolate application semantics from arbitrary deployment environments. Using DDFLOW leads to portable, visualizable, and intuitive applications. The accompanying runtime enables dynamic adaptation to improve end-to-end latency while preserving application behavior despite device failures. We evaluate DDFLOW on the Heliot platform, a hybrid emulation testbed for learning-enabled IoT systems. This demo complements the paper DDFLOW: Visualized Declarative Programming for Heterogeneous IoT Networks that is to be presented at IoTDI 2019 [6].

Resource virtualization on FPGA devices, achievable due to its dynamic reconfiguration capabilities, provides an attractive solution to save silicon area. Architectural synthesis for dynamically reconfigurable FPGA-based digital systems needs to consider the case of reducing the number of temporal partitions (reconfigurations) by enabling sharing of some functional units in the same temporal partition. This paper proposes a novel algorithm for automated datapath design from behavioral input descriptions (represented by an acyclic dataflow graph), which simultaneously performs temporal partitioning and sharing of functional units. The proposed algorithm attempts to minimize both the number of temporal partitions and the execution latency of the generated solution. Temporal partitioning, resource sharing, scheduling, and a simple form of allocation and binding are all integrated in a single task. The algorithm is based on heuristics and on a new concept of construction by gradually enlarging timing slots. Results show the efficiency and effectiveness of the algorithm when compared to existent approaches.

Part 3 of this Special Section on High-Performance Computing in Applied Remote Sensing continues to present the state-of-the-art research in incorporating high-performance computing facilities and algorithms for effective and efficient remote sensing applications. “Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries” by Moody et al. presents results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. “Graphics processor unit accelerated finite-difference time domain method for electromagnetic scattering from onedimensional large scale rough soil surface at low grazing incidence” by Jia et al. applies the Compute Unified Device Architecture (CUDA) technology to compute an electromagnetic scattering problem on GPUs and achieves significant speedup factors. “High-performance meshing processing of remote sensing data on large displays” by Hsieh et al. presents a technique that visualizes LIDAR point clouds on a tiled display wall, termed highly interactive-parallelized display (HIPerDisplay), which has twenty 24-inch liquid-crystal displays with a total resolution of 46 Mpixels. The results also showed that the HIPerDisplay offered superior performance for the processing of large LIDAR datasets. “Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images” by Wu et al. presents a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. “Hyperspectral band selection based on parallel particle swarm optimization and impurity function band prioritization schemes” by Chang et al. presents a framework for band selection in hyperspectral imagery that uses two techniques, referred to as particle swarm optimization (PSO) band selection and the impurity function band prioritization (IFBP) method. The experimental results demonstrate that the proposed PPSO/IFBP band selection method can not only improve computational speed but also offer a satisfactory classification performance. “Graphics processing unit implementation of the maximum likelihood solution to the inverse problem for retrieval of geophysical parameters from high-resolution sounder data” by Wei et al. explores the use of GPU in speedup of the maximum likelihood solution to an illposed retrieval problem. For the infrared atmospheric sounding interferometer high-resolution sounder having 8641 channels, the use of GPU on maximum likelihood estimation shows a promising speedup of 1986× compared to a single-threaded native CPU version.

The optimal single band for dorsal hand recognition is 890 nm based on the fusion of MFRAT and CompCode. The extracted vein information is limited within one spectrum only. One of the advantages of multispectral technique is to pursue higher recognition performance through the fusion of multiple bands. The main theoretical basis is that the images on different bands have complementary information, which is helpful to performance improvement. Obviously, adding more bands is similar to feature dimension increase, and the redundant information may also increase quickly especially when the bands are highly relevant to each other. So the multispetral band selection process is limited to high efficiency, and it demands that the selected bands have the maximum uncorrelation. Noting that recognition of visible region is obviously far worse and the texture of non-vein part is not reliable enough for efficient recognition, we plan to do the multiple band selection work in near infrared (NIR) region only. 422 dorsal hands of East Asians with different bands ranging from 700 to 1040 nm are used. This chapter tries to address two basic issues, the number of the bands for optimal group and which bands can explain the multispectral model more precisely. Unlike optimal single band selection, exhaustive method is not practicable for this task. The number of possible combination is immeasurable, especially when the optimal band number is unknown. Our scheme is to realize the task in two steps: First, divide the NIR region into several band classes according to special rules so that the number of optimal band is fixed; secondly, choose the bands from these classes to represent them with proper estimation criterion.

We employ output-discrepancy consensus to mitigate faulty modules of a Triple Modular Redundant (TMR) arrangement using dynamic partial reconfiguration. Traditionally, the fault-handling resilience of a TMR arrangement is limited to fault(s) in a single TMR instance over the entire mission duration. An additional permanent fault in any of two other TMR instances results in mission's failure. However, in this work, a novel Self-Configuring approach for Discrepancy Resolution (SCDR) is developed and assessed. In SCDR, the occurrence of faults in more than one module initiates the repair mechanism, then upon fault recovery, the system is configured into Concurrent Error Detection (CED) mode. The approach is validated by the complete recovery of a TMR realization of 25 stage Finite Impulse Response (FIR) filter implemented on a reconfigurable platform as a case study. The results show that a self-healing circuit can be realized exploiting the dynamic partial reconfiguration capability of FPGAs while requiring a streamlined operational data path compared to TMR.

In this paper, energy-efficient architecture for Variable Block Size Motion Estimation (VBSME) is proposed to fully utilize dynamic partial reconfiguration capability of programmable hardware fabric in the heterogeneous computing environment. Dynamic Partial Reconfiguration is a unique feature of FPGAs that makes best use of hardware resources and power by allowing adaptive algorithm to be implemented on the reconfigurable hardware fabric during runtime. We exploit this feature of FPGA to support run-time reconfiguration of the proposed modular hardware architecture for motion estimation (ME). The implemented scalable SAD array can provide different resolutions and frame rates for real time applications with multiple reconfigurable regions at the operating frequency of 90MHz.

We achieved external activation of local hot-spot sites in supracolloidal assembly structures. The concept was demonstrated by boosting surface-enhanced Raman scattering (SERS) efficiency by one order of magnitude through a heating-induced process. Our approach involves assembling gold nanoparticles with distinct dimensions, i.e. 16 and 80 nm, into well-defined planet-satellite-type arrangement structures using thermoresponsive (poly(N-isopropylacrylamide)) star polymer linkers. Insights into the assembly process were obtained by calculations within the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory framework. We observe one order of magnitude increase in SERS enhancement by a heating-induced volume-phase transition. This magnification aligns with simulations run using the finite-difference time-domain (FDTD) method. The implications of this adaptive supracolloidal concept are twofold: Firstly, our approach bypasses limitations of existing systems that are associated with the limited accessibility of electromagnetic hot-spot sites in strongly coupled, static assemblies of plasmonic nanoparticles, by providing the capability of dynamic hot-spot re-configuration. Second, these externally activated probes offer promising opportunities for the development of messenger materials and associated sensing strategies.

It is an important and basic work of remotely sensed data processing that optimal band selection influences the result of visual interpretation and the accuracy and efficiency of computer classification.The Guizhou karst mountainous area was studied,based on the OIF model,the optimal band selection of CBERS-CCD data in land use and land cover information extraction was obtained,and some useful conclusions were got: firstly,in the all possible three-band combinations,B3-4-1 with RGB is the best one for land use and land cover information extraction;secondly,the 5th band of CBERS-CCD data is very helpful to certain information extraction,such as paddy field and grassland,etc.And thirdly only the optimal band combination to extract information can not get enough classification precision and advanced information fusion and extraction methods to CBERS-CCD data needs to be exploited in the future.

The cancer remained as one of the major cause of mortality worldwide. Instead of traditional excitional biopsy, it will be ideal to put forward the cancer detection using invasive techniques, it can make the treatment more effective and simple. Here a self sufficient classification method has been introduced that combines both spectral and spatial information on hyper-spectral images of an animal tissue in distinguishing cancerous via healthy. An automated algorithm based on an optimal band selection and Minimum Spanning Forest (MSF) algorithm has been proposed for classification of hyperspectral images. The segmentation has been achieved by collecting the features from the selected markers in the optimal band. In optimal band selection, the unnecessary band may get eliminated and selects a range where there is a probability of having abnormality and the markers are being selected using the intensity feature available in the selected band. From these numerous markers available, the problematic region if available, may be differentiated using the MSF algorithm. The MSF is utilizing the pixel distribution of every marker. The SVM classifier is finally used to distinguish between the normal or abnormal based on the MSF features. The corresponding MSF based advanced cancer detection scheme is accurate and efficient, making it as a good noninvasive tool. It is not only applicable in early cancer detection, but also as an interpretive tool in tumor margin resection.

The purpose of the study was to assess the effect of dynamic capabilities on organizational performance of telecommunication firms in Mombasa County. The study specific objectives are to determine the effect of sensing capabilities, innovation, reconfiguration capabilities and integration capability on organizational performance. The study is anchored on organizational learning theory, dynamic capabilities theory, performance maximization theory and resource based view. The target population was 37 network facilities providers in Kenya. The study results showed that sensing capability, seizing capability, innovation capability and reconfiguration capability positively and significantly affect the performance of telecommunication companies in Kenya. The study recommends that telecommunication firms should improve their sensing capability, seizing capability, innovation capability and reconfiguration capability so as to increase the performance of telecommunication firms in Kenya.

Model for estimation of total nitrogen content in sandalwood leaves based on nonlinear mixed effects and dummy variables using multispectral images

Field-Programmable Gate Arrays (FPGAs) provide highly flexible platforms to implement sophisticated data processing for scientific space instruments. The dynamic partial reconfiguration (DPR) capability of FPGAs allows it to schedule HW tasks. While this feature adds another dimension of processing power that can be exploited without significantly increasing system complexity and power consumption, there are still several challenges for an efficient DPR use. State-of-the-art concepts concentrate either on resource-efficient implementations at design time or flexible HW task scheduling at runtime. In this paper we propose a balanced algorithm that considers both optimization goals and is well suited for resource-limited space applications.

A Givens rotation based scalable QRD core which utilizes an efficient pipelined and unfolded 2D multiply and accumulate (MAC) based systolic array architecture with dynamic partial reconfiguration (DPR) capability is proposed. The square root and inverse square root operations in the Givens rotation algorithm are handled using a modified look-up table (LUT) based Newton-Raphson method, thereby reducing the area by 71% and latency by 50% while operating at a frequency 49% higher than the existing boundary cell architectures. The proposed architecture is implemented on Xilinx Virtex-6 FPGA for any real matrices of sizem×n, where4≤n≤8andm≥nby dynamically inserting or removing the partial modules. The evaluation results demonstrate a significant reduction in latency, area, and power as compared to other existing architectures. The functionality of the proposed core is evaluated for a variable length adaptive equalizer.

Techniques for network reliability enhancement using the virtual path strategy are described. The digital path scheme in synchronous transfer mode (STM) networks and the virtual path scheme in asynchronous transfer mode (ATM) networks are compared in terms of path management. It is shown how the dynamic network reconfiguration capability is effectively provided with the virtual path scheme. Some virtual path applications for network reliability enhancement are proposed. >