Shape from shading using a single image of a Lambertian surface is inherently ambiguous. When the light source direction is known, the surface normal estimation has a cone-ambiguity, which worsens when the source is unknown. Recently, shape from heat conduction has emerged as an approach that leverages heat transport equations to estimate the Shape Laplacian operator, an intrinsic measure of shape. However, deriving surface normals from the Laplacian operator encounters a local binary convex/concave ambiguity. Our contribution introduces a novel theory to resolve these local shape ambiguities (excluding a few degeneracies) without relying on priors like smoothness, by combining the cues from shading and heat conduction. Our method ensures the mathematical constraints of both shading and the Laplacian are satisfied simultaneously, even with an unknown light source. We validate our theory through simulations of complex shapes and analyze its performance in the presence of noise. Index Terms-Shape Reconstruction, Heat Conduction, Concave/convex Ambiguity, Thermal Video

We present a novel theory that establishes the relation-ship between light transport in visible and thermal infrared, and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible, we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next, we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the ab-sorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on ap-plying energy conservation principle at each pixel indepen-dently. We validate our theory using real-world experi-ments on diffuse objects made of different materials that ex-hibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.

We aim to address key challenges in long-horizon embodied exploration and navigation by proposing a long-horizon object transport task called Long-HOT and a novel modular framework for temporally extended navigation. Agents in Long-HOT need to efficiently find and pick up target objects that are scattered in the environment, carry them to a goal location with load constraints, and optionally have access to a container. We propose a modular topological graph-based transport policy (HTP) that explores efficiently with the help of weighted frontiers. Our hierarchical approach uses a combination of motion planning algorithms to reach point goals within explored locations and object navigation policies for moving towards semantic targets at unknown locations. Experiments on both our proposed Habitat transport task and on MultiOn benchmarks show that our method outperforms baselines and prior works. Further, we analyze the agent’s behavior for the usage of the container and demonstrate meaningful generalization to harder transport scenes with training only on simpler versions of the task.

Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.

In this brief, we present a novel architecture for pseudorandom (PN) code acquisition based on statistical error compensation (SEC), which achieves significant power savings. SEC treats errors in hardware as noise in communication networks, and employs robust estimation theory to compensate for errors. We apply SEC to a 256-tap PN code acquisition filter in a 180-nm CMOS process. Multiple (five) dies were tested under voltage overscaling to achieve a near constant detection probability (Pdet) above 90%. The minimum energy consumption ranged from 72.89 to 210.59 pJ (ave 122.52 pJ) for supply voltages between 0.69 and 0.70 V. These operating conditions result in raw error rates of 85.83%-91.23% (ave 88.99%). Energy savings over a conventional errorfree design ranges from 2.4× to 5.8× (ave 3.86×). Energy savings over past work ranges from 1.55× to 3.79× (ave 2.52×). Improvement in error-tolerance over existing error-tolerant designs range from 2146× to 2281× (ave 2225×). The large energy savings were found to be due to a combination of voltage scaling and activity factor reduction. The proposed design achieves a 2.5× improvement in the figure of merit [normalized power/(#taps * precision * sample rate)] compared with conventional PN code acquisition filters.

Applications such as long-term environmental monitoring and large-scale surveillance demand reliable performance from sensor nodes while operating within strict energy constraints. There is often not enough power for sensors to make measurements all of the time. In these cases, one must decide when to run each sensor. To this end, we develop a one-step optimal sensor-scheduling algorithm based on expected-utility maximization. “Utility” is an application-specific measure of the benefit from a given sensor measurement. In sensing environments that can be modeled using a hidden Markov model, selecting the appropriate combination of sensors at each time instant enables maximization of the expected utility while operating within an energy budget. For some budgets, the utility-based algorithm shows more than 300% utility gains over a constant duty-cycle scheme designed to consume the same amount of energy. These benefits are dependent on the energy budget.

Future microprocessors increasingly rely on an unreliable CMOS fabric due to aggressive scaling of voltage and frequency, and shrinking design margins. Fortunately, many emerging applications can tolerate computational errors caused by hardware unreliabilities, at least during certain execution intervals. In this paper, we propose scalable stochastic processors, a computing platform for error-tolerant applications that is able to scale gracefully according to performance demands and power constraints while producing outputs that are, in the worst case, stochastically correct. Scalability is achieved by exposing to the application layer multiple functional units that differ in their architecture but share functionality. A mobile video encoding application here is able to achieve the lowest power consumption at any bitrate demand by dynamically switching between functional-unit architectures.

The stochastic sensor network-on-chip (SSNOC) was recently proposed as an effective computational paradigm for jointly achieving energy-efficiency and robustness in nanoscale processes. In this paper, we study the trends in energy-efficiency and robustness exhibited by an SSNOC architecture as the feature size scales from 130nm to 32nm for a PN-code acquisition application. The conventional architecture exhibits a 3 orders-of-magnitude loss in detection probability P_{det} due to process variations in the 130nm and smaller technology nodes. At the 130nm and 90nm nodes, the proposed SSNOC architecture recovers from this performance loss, and exhibits a 2 orders-of-magnitude smaller variation in P_det compared to the conventional architecture. However, for the 65nm and 45nm technology nodes, the SSNOC architecture with assistance from circuit level techniques such as adaptive body bias (ABB) and adaptive supply voltage (ASV) shows a 2-3 order-of-magnitude better detection performance. In addition, the SSNOC architecture with ABB/ASV achieves 22% to 31% energy savings. For the 32nm node, the current version of SSNOC with ABB/ASV is not robust enough and thus motivates the need to explore even more powerful versions of SSNOC.

Presented in this paper is an energy-efficient and variation-tolerant PN-code acquisition architecture for the wireless CDMA2000 standard. The architectures is based on the recently proposed stochastic sensor network-on-chip (SSNOC) computational paradigm. The latter employs the principles of statistically similar decomposition and robust estimation theory to compensate for timing errors due to process variations. Performance of the SSNOC-based PN-code acquisition architecture at the slow process corner indicates that the average probability of detection PDetimproves by up to 3 orders-of-magnitude over that of the conventional architecture, while the variation in PDet(sigma / mu) is reduced by up to 2 orders-of-magnitude over that of the conventional architecture while simultaneously achieving a power reduction of 39%.