330 GHz FMCW Image Sensor for Homeland Security Applications

A single-pixel imaging remote sensor operating at 330 GHz is described. It is based on a frequency modulated continuous wave (FMCW) and aimed at detection of concealed objects for ranges up to 40 m. The system consists of 2 horn-lens antennas integrated with a homodyne transceiver. The synthesized linear FMCW signal at X-band is multiplied by a factor of 32 to generate the transmitted signal. An intermediate frequency (IF) signal obtained in the output port of the 2-nd harmonic mixer is employed for image processing. Distance measurements were made by performing data acquisition unit based on LabView interface and resulting in a range resolution about 1 cm. Examples of 2D and 3D images reconstructed with this sensor are presented.     

On problems without polynomial kernels

Kernelization is a strong and widely-applied technique in parameterized complexity. A kernelization algorithm, or simply a kernel, is a polynomial-time transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size and parameter bounded by a function of the parameter in the input. A kernel is polynomial if the size and parameter of the output are polynomially-bounded by the parameter of the input.In this paper we develop a framework which allows showing that a wide range of FPT problems do not have polynomial kernels. Our evidence relies on hypothesis made in the classical world (i.e. non-parametric complexity), and revolves around a new type of algorithm for classical decision problems, called a distillation algorithm, which is of independent interest. Using the notion of distillation algorithms, we develop a generic lower-bound engine that allows us to show that a variety of FPT problems, fulfilling certain criteria, cannot have polynomial kernels unless the polynomial hierarchy collapses. These problems include k-Path, k-Cycle, k-Exact Cycle, k-Short Cheap Tour, k-Graph Minor Order Test, k-Cutwidth, k-Search Number, k-Pathwidth, k-Treewidth, k-Branchwidth, and several optimization problems parameterized by treewidth and other structural parameters.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.     

W-Hierarchies Defined by Symmetric Gates

The classes of the W-hierarchy are the most important classes of intractable problems in parameterized complexity. These classes were originally defined via the weighted satisfiability problem for Boolean circuits. Here, besides the Boolean connectives we consider connectives such as majority, not-all-equal, and unique. For example, a gate labelled by the majority connective outputs true if more than half of its inputs are true. For any finite set C\mathcal{C} of connectives we construct the corresponding W( C\mathcal{C} )-hierarchy. We derive some general conditions which guarantee that the W-hierarchy and the W( C\mathcal{C} )-hierarchy coincide levelwise. If C\mathcal{C} only contains the majority connective then the first levels of the hierarchies coincide. We use this to show that a variant of the parameterized vertex cover problem, the majority vertex cover problem, is W[1]-complete.     

Feasibility study of 2020 target costs for PEM fuel cells and lithium-ion batteries: A two-factor experience curve approach

Vehicles with electric drive trains are currently the subject of intense discussion by society. The cost trends of the individual components in the electric drive train are a central aspect of the future market success of the different vehicle drive systems.     

A Scalable Parallel Algorithm for Reachability Analysis of Very Large Circuits

This paper presents a scalable method for parallelizing symbolic reachability analysis on a distributed-memory environment of workstations. We have developed an adaptive partitioning algorithm that significantly reduces space requirements. The memory balance is maintained by dynamically repartitioning the state space throughout the computation. A compact BDD representation allows coordination by shipping BDDs from one machine to another. This representation allows for different variable orders in the sending and receiving processes. The algorithm uses a distributed termination protocol, with none of the memory modules preserving a complete image of the set of reachable states. No external storage is used on the disk. Rather, we make use of the network, which is much faster.We implemented our method on a standard, loosely-connected environment of workstations, using a high-performance model checker. Initial performance evaluation of several large circuits shows that our method can handle models too large to fit in the memory of a single node. The partitioning algorithm achieves reduction in space, which is linear in the number of workstations employed. A corresponding decrease in space requirements is measured throughout the reachability analysis. Our results show that the relatively slow network does not become a bottleneck, and that computation time is kept reasonably small.