Autonomous Unmanned Aerial Vehicle (UAV) swarms navigating complex environments require energy-efficient pathfinding algorithms to optimize performance and extend operational lifespan. To address the NP-hard problem of Multi-Agent Path Finding (MAPF), this paper introduces the Adaptive Algorithmic Determination for Advanced Pathfinding (AADAP) approach. AADAP is a white-box approach for a self-adaptive system that dynamically selects the most suitable pathfinding approaches from a diverse portfolio—including specific deterministic methods (e.g., Dijkstra’s), heuristic techniques (e.g., A*), bio-inspired algorithms (e.g., Ant Colony Optimization), evolutionary strategies (e.g., Genetic Algorithms), and probabilistic methods (e.g., Rapidly-Exploring Random Trees)—based on real-time environmental conditions and UAV swarm characteristics.By integrating with Geographic Resources Analysis Support System (GRASS GIS) for enhanced geospatial data synchronization, AADAP employs a decision-making mechanism that evaluates algorithm performance metrics in relation to environmental complexity and swarm dynamics. We evaluated AADAP using the Webots simulation environment with simulated DJI Matrice 300 RTK UAVs. The results indicate that AADAP excels in structured environments with a limited number of UAVs, outperforming deep learning-based methods and static algorithm selection approaches by approximately 14% in energy efficiency. Conversely, in complex terrains, deep learning methods demonstrate superior adaptability due to their data-driven learning capabilities.

Teaching in the area of embedded systems is challenging because both software and hardware development must be considered in an integrated manner. The experience gained through the practical hardware-related implementation of prototypes cannot simply be replaced by simulations. The question is, however, how can students gain as much practical experience as possible while taking into account real laboratory constraints? We present an approach that allows students to gain practical experience via virtual access to laboratory setups, which allows them to work in groups remotely and then continue their work in the labs, providing a similar experience as under normal lab conditions. For educators, we introduce a flexible environment that can be adopted to specialized embedded courses due to the used components and methodology of the ADDIE teaching model.

We give the first time-space trade-off lower bounds for resolution proofs that apply to superlinear space. In particular, we show that there are formulas of size $N$ that have resolution refutations of size (and space) $T(N)= N^{\Theta(\log N)}$ (and like all formulas have another resolution refutation of space $N$) but for which no resolution refutation can simultaneously have space $S(N) = T(N)^{o(1)}$ and size $T(N)^{O(1)}$. In other words, any substantial reduction in space results in a super-polynomial increase in total size. We also show somewhat stronger time-space trade-off lower bounds for regular resolution, which are also the first to apply to superlinear space. For any function $T$ that is at most weakly exponential, $T(N) = 2^{o(N^{1/4})}$, we give a tautology that has regular resolution proofs of size and space $T(N)$, but no such proofs with space $S(N) = T(N)^{1-\Omega(1)}$ and size $T(N)^{O(1)}$. Thus, any polynomial reduction in space has a superpolynomial cost in size. These tautologies are width 4 disjunctive normal form (DNF) formulas.

We present a new approach to text line aggregation that can work as both a line formation stage for a myriad of text segmentation methods (over all orientations) and as an extra level of filtering to remove false text candidates. The proposed method is centred on the processing of candidate text components based on local and global measures. We use orientation histograms to build an understanding of paragraphs, and filter noise and construct lines based on the discovery of prominent orientations. Paragraphs are then reduced to seed components and lines are reconstructed around these components. We demonstrate results for text aggregation on the ICDAR 2003 Robust Reading Competition data, and also present results on our own more complex data set. Copyright © 2014 SCITEPRESS.

We obtain asymptotically sharper lower bounds on resolution complexity for k-CNF's than was known previously. We show that for any large enough k there are k-CNF's which require resolution width (1-~O(k-1/4))n, regular resolution size 2(1-~O(k-1/4))n, and general resolution size (3/2)(1-~O(k-1/4))n.

We present size-space trade-offs for the polynomial calculus (PC) and polynomial calculus resolution (PCR) proof systems. These are the first true size-space trade-offs in any algebraic proof system, showing that size and space cannot be simultaneously optimized in these models. We achieve this by extending essentially all known size-space trade-offs for resolution to PC and PCR. As such, our results cover space complexity from constant all the way up to exponential and yield mostly superpolynomial or even exponential size blow-ups. Since the upper bounds in our trade-offs hold for resolution, our work shows that there are formulas for which adding algebraic reasoning on top of resolution does not improve the trade-off properties in any significant way.

There has been considerable interest lately in the complexity of distributions. Recently, Lovett and Viola (CCC 2011) showed that the statistical distance between a uniform distribution over a good code, and any distribution which can be efficiently sampled by a small bounded-depth AC0 circuit, is inverse-polynomially close to one. That is, such distributions are very far from each other. We strengthen their result, and show that the distance is in fact exponentially close to one. This allows us to strengthen the parameters in their application for data structure lower bounds for succinct data structures for codes. From a technical point of view, we develop new large deviation bounds for functions computed by small depth decision trees, which we then apply to obtain bounds for AC0 circuits via the switching lemma. We show that if such functions are Lipschitz on average in a certain sense, then they are in fact Lipschitz almost everywhere. This type of result falls into the extensive line of research which studies large deviation bounds for the sum of random variables, where while not independent, exhibit large deviation bounds similar to these obtained by independent random variables.

We give the first time-space tradeoff lower bounds for Resolution proofs that apply to superlinear space. In particular, we show that there are formulas of size N that have Resolution refutations of space and size each roughly Nlog2 N (and like all formulas have Resolution refutations of space N) for which any Resolution refutation using space S and length T requires T ≥ (N0.58 log2 N/S)Ω(log log N/log log log N). By downward translation, a similar tradeoff applies to all smaller space bounds.