Sökresultat

In this paper we propose a design methodology to partition dataflow applications on a multi clock domain architecture. This work shows how starting from a high level dataflow representation of a dynamic program it is possible to reduce the overall power consumption without impacting the performances. Two different approaches are illustrated, both based on the post-processing and analysis of the ca

Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex cor

The min-sum (MS) and approximate-min* (a-min*) algorithms are alternatives of the belief propagation (BP) algorithm for decoding low-density parity-check (LDPC) codes. To lower the BP decoding complexity, both algorithms compute two magnitudes at each check node (CN) and pass them to the neighboring variable nodes (VNs).In this work we propose a new algorithm, ga-min*, that generalizes the MS and

A pre-requisite for the design of wireless systems is the understanding of the propagation channel. While a wealth of propagation knowledge exists for bands below 6 GHz, the same can not be said for bands approaching millimeter-wave frequencies. In this paper, we present the design, implementation and measurement-based verification of a re-configurable 27.5-29.5 GHz channel sounder for measuring d

Large intelligent surfaces (LIS) have the potential to be the beyond-massive-MIMO solution, even further improving spectral efficiency, coverage, reliability and other performance measures. They also open up for entirely new services, such as precise localization, environment sensing, and wireless energy transfer. By constructing larger surfaces as a grid of panels, we can activate and deactivate

In this paper we present a technique that improves rendering performance for real-time ith ray in the presence of dynamic lights and objects. In particular we verify photon paths from the previous frame against dynamic objects in the current frame, and show how most photon paths are still valid. When using area lights, we use a data structure to store light distribution that tracks light paths all

In this contribution, a recently conducted measurement campaign in a suburban scenario for the Unmanned Aerial Vehicle (UAV) Air-to-Ground (A2G) radio channel is introduced.The downlink signals in an in-service Long Term Evolution (LTE) network were collected and utilized to extract the Channel Impulse Responses (CIRs). A high-resolution parameter estimation algorithm derived based on the Space-Al

Spatially-coupled serially concatenated codes (SC-SCCS) are a class of turbo-like codes constructed by interconnecting a sequence of SCCS using a set of block permutors. At short block lengths, however, the bit-error-rate (BER) performance of SC-SCCS constructed by independent block permutors exhibits a high error floor. In this paper, we propose an alternative method for constructing SC-SCCS to m

Stochastic sampling of light transport paths is key to Monte Carlo forward rendering, and previous studies have led to mature techniques capable of drawing high-contribution light paths in complex scenes. These sampling techniques have also been applied to differentiable rendering.In this paper, we demonstrate that path sampling techniques developed for forward rendering can become inefficient for

Aerosol particles, small solid or liquid particles suspended in a gas, are found everywhere in the atmosphere. Air pollution from such particles, mainly soot from combustion-derived activity is a leading cause of premature deaths in the world and have a large effect on the climate. Aerosol particles can interact with climate-related processes in the atmosphere either directly by absorbing or refle

We establish an exactly tight relation between reversiblepebblings of graphs and Nullstellensatz refutations of pebbling formulas,showing that a graph G can be reversibly pebbled in time t and space s if and only if there is a Nullstellensatz refutation of the pebbling formulaover G in size t + 1 and degree s (independently of the field in whichthe Nullstellensatz refutation is made). We use this

Systems mixing Boolean logic and arithmetic have been a long-standing challenge for verification tools such as SAT-based bit-vector solvers. Though SAT solvers can be highly efficient for Boolean reasoning, they scale poorly once multiplication is involved. Algebraic methods using Gröbner basis reduction have recently been used to efficiently verify multiplier circuits in isolation, but generally

Sliding mode control (SMC) is to keep the system to a stable differential manifold. Model predictive control (MPC) calculates the control input by solving an optimization problem on receding horizon. The method of receding horizon sliding control (RHSC) includes the predicted information into the SMC design by combining SMC and MPC. Considering the modeling error and measurement noise, there are m

Field-theory simulation by the complex Langevin method offers an alternative to conventional sampling techniques for exploring the forces driving biomolecular liquid–liquid phase separation. Such simulations have recently been used to study several polyampholyte systems. Here, we formulate a field theory corresponding to the hydrophobic/polar (HP) lattice protein model, with finite same-site repul

We prove that polynomial calculus (and hence also Nullstellensatz) over any field requires linear degree to refute that sparse random regular graphs, as well as sparse Erdős-Rényi random graphs, are 3-colourable.

We prove that unary Sherali-Adams requires proofs of size nΩ(d) to rule out the existence of an nΘ(1)-clique in Erdős-Rényi random graphs whose maximum clique is of size d ≤ 2 log n. This lower bound is tight up to the multiplicative constant in the exponent. We obtain this result by introducing a technique inspired by pseudo-calibration which may be of independent interest. The technique involves

This paper extends the application of quantile-based Bayesian inference to probability distributions defined in terms of quantiles of observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and parameter interpretability, making them useful for eliciting information about observables. To encode uncertainty in the quantiles elicited from experts, we p