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For many years, car antennas of various applications are co-integrated into the roof-mounted “shark fin” module. More recently, both hidden antennas and full glass roofs are becoming trendy in the car industry. Taken together, they point to the integration of car antennas into glass roof being an attractive car antenna solution. However, such a solution is challenging to realize due to the roof gl
Group exhibition. Participated with the work HEROES.Double Cheese Platter: Centenary of the Collection. Twenty Years of the Kunstmuseum StuttgartIn 1925, works from the City of Stuttgart art collection, then called the Städtische Gemäldesammlung (Municipal Painting Collection), were shown to the public for the first time in the Villa Berg. As the Galerie der Stadt Stuttgart (Stuttgart City Gallery
As a prominent category of side-channel attacks (SCAs), plaintext-checking (PC) oracle-based SCAs offer the advantages of generality and operational simplicity on a targeted device. At TCHES 2023, Rajendran et al. and Tanaka et al. independently proposed the multiple-valued (MV) PC oracle, significantly reducing the required number of queries (a.k.a., traces) in the PC oracle. However, in practice
We propose a new class of high-rate spatially coupled LDPC (SC-LDPC) codes based on the convolutional selforthogonal codes (CSOCs) first introduced by Massey. The SCLDPC codes are constructed by treating the irregular graph corresponding to the parity-check matrix of a systematic rate R=(n-1) / n CSOC as a convolutional protograph. The protograph can then be lifted using permutation matrices to ge
We introduce Thalassa, a framework designed to convert nonlinear systems of partial differential equations (PDEs) with a time-like component into tensor programs that solve these equations. These programs can run on GPUs as well as machine learning (ML) acceleration hardware, enabling scientific computing fields such as computational fluid dynamics, astrophysics, mechanics and biology to utilize a
The introduction of electric heavy trucks will lead to new charging power requirements on the power grid. This study examines how much charging power is required for the public fast charging of a fully electrified long-haul truck fleet. Probabilistic truck charging profiles are created using agent-based simulations, based on fully representative long-haul goods transport data from the study area.
Motivated by the well established idea that collective wisdom is greater than that of an individual, we propose a novel learning dynamics as a sort of companion to the Abelson model of opinion dynamics. Agents are assumed to make independent guesses about the true state of the world after which they engage in opinion exchange leading to consensus. We investigate the problem of finding the optimal
Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a ro
Digital control systems introduce unavoidable computational latencies. For some controllers this time delay inhibits practical use, even though they in theory could provide more efficient control. For example, solving an optimization problem each sampling period when using model predictive control. By sampling faster than the computation time and executing independent controllers on distributed ha
In this work, we study the problem of estimating the in-ear sound field using microphone measurements at nearby positions. Typically, the sound field is first interpolated to the position of the head under free-field assumptions, followed by filtering through pre-measured head-related transfer functions (HRTFs). While properties of free-field interpolation methods have been well studied with respe
This study investigates the incentives and barriers influencing AI ethics adoption in unregulated environments, drawing on semi‐structured interviews with AI professionals in Bangladesh. Our findings reveal that external pressures, such as international client requirements, drive companies to implement ethical measures, yielding both economic benefits and enhanced reputation. Conversely, tight dea
Inland surface waters are vital for the global carbon, energy, and water cycles; however, they are affected by climate change and human activities. Continuous monitoring of Water Surface Elevation (WSE) is essential for assessing water quantity and ensuring sustainable management. Traditional in-situ WSE measurements often face high costs and accessibility challenges. This study examines the use o
Post-compression of (sub)-picosecond laser pulses down to sub-100 fs durations has become a standard approach for high-peak-power sources at a high average power [1]. Here, we propose a new post-compression geometry suited for extreme temporal compression of energetic narrowband lasers, such as diode or electrically pumped four-level systems which can deliver kW-average powers [2], but typically e
Sparse tensor computations suffer from irregular memory access patterns that degrade cache performance. While software prefetching can mitigate this, existing compiler approaches lack the semantic insight needed for effective optimization. We present ASaP, an automatic software prefetching framework integrated within MLIR’s sparse tensor dialect. By leveraging semantic information-tensor formats a
We perform a validation analysis on the multipolar model of opinion dynamics. A general methodology for using the model on datasets of two correlated variables is proposed and tested using data on the relationship between COVID-19 vaccination rates and political participation in Sweden. The model is shown to successfully capture the opinion segregation demonstrated by the data and spatial correlat
Three different semiconductor nanowires are combined into a single optoelectronic artificial neuron. Our device provides a path towards low power computations with significantly reduced circuit footprint, thus addressing critical limitations in neuromorphic photonics. The false-coloured scanning electron micrograph shown in Figure 1a depicts the fabricated nanowire neuron. Two pin-doped InP nanowi
Intense ultrashort-pulsed laser systems based on Ti:sapphire chirped pulse amplifiers (CPA) operating at 800 nm have been a workhorse of ultrafast science for several decades. More recently, driven by the progress in laser technology, sources with central wavelengths further in the infrared have emerged as powerful tools, in particular to generate soft X-ray (SXR) radiation via high-harmonic gener
Photonics-based approaches to neuromorphic hardware offer advantages in speed and connectivity but often suffer from large device footprints.[1] Here, we present an ultra-compact artificial neuron built from semiconductor nanowires. A scanning electron micrograph of the nanowire device is presented in Figure 1a. Inside the dashed box, we show the neuron built from three III-V nanowires - two InP p
A multi-variable adaptive controller is derived as the explicit solution to a minimax dynamic game. The minimizing player selects the control action as a function of past state measurements and inputs. The maximizing player selects disturbances and model parameters for the underlying linear time-invariant dynamics. This leads to a Bellman equation that can be solved explicitly for the case with un
