A Multiresolution Approach for Large Real-World Camera Placement Optimization Problems

This paper addresses the camera placement problem for smart cities in 3-D space and proposes a heuristic algorithm that maximizes the weighted coverage rate while satisfying the budget constraint. We first discuss about the quantization of surveillance target and camera locations into discrete grid points and the setting of the weights of target grid points. We then present the visibility analysis with field of view in consideration of occlusions and different camera specifications. Based on these characteristics and constraints, we formulate the camera placement problem and propose a new heuristic algorithm called collaboration-based local search algorithm, which incorporates the local search into collaborative allocation. We evaluate the performance of the proposed algorithm in comparison with the greedy algorithm, binary genetic algorithm, and binary particle swarm optimization through simulation experiments with small and large problem sets. The simulation results show that the proposed algorithm outperforms the three existing algorithms in terms of the average weighted coverage rate and computation time.

Efficient deployment of cameras optimizes the costs and benefits in a multicamera surveillance network. Most camera placement algorithms are developed for surveillance regions with predefined camera locations, where optimal orientation for every given camera location is determined to maximize coverage. The existing approaches to optimizing the number of cameras, locations, and orientations focus on specific scenarios and are neither generic nor scalable across diverse surveillance scenarios. This article proposes a generic and scalable reward penalty score (RPS) algorithm for optimal camera placement in diverse indoor and outdoor scenarios to efficiently attain a specified coverage requirement. The camera placement decision is driven by maximizing total coverage over the surveillance region with a minimum number of cameras at optimal locations and orientations. Based on the RPS algorithm, we extend the existing Greedy grid voting (GGV) algorithm that was limited to optimizing camera orientations for predefined locations. The extended GGV (EGGV) algorithm is broader in scope and optimizes the number of cameras, their locations, and orientations. Overall, the RPS algorithm produces superior coverage results over indoor and outdoor scenarios compared to the existing camera placement algorithms on map images of diverse surveillance scenarios ranging from small to large.

BackgroundMicrobial genomes at the National Center for Biotechnology Information (NCBI) represent a large collection of more than 35,000 assemblies. There are several complexities associated with the data: a great variation in sampling density since human pathogens are densely sampled while other bacteria are less represented; different protein families occur in annotations with different frequencies; and the quality of genome annotation varies greatly. In order to extract useful information from these sophisticated data, the analysis needs to be performed at multiple levels of phylogenomic resolution and protein similarity, with an adequate sampling strategy.ResultsProtein clustering is used to construct meaningful and stable groups of similar proteins to be used for analysis and functional annotation. Our approach is to create protein clusters at three levels. First, tight clusters in groups of closely-related genomes (species-level clades) are constructed using a combined approach that takes into account both sequence similarity and genome context. Second, clustroids of conservative in-clade clusters are organized into seed global clusters. Finally, global protein clusters are built around the the seed clusters. We propose filtering strategies that allow limiting the protein set included in global clustering.The in-clade clustering procedure, subsequent selection of clustroids and organization into seed global clusters provides a robust representation and high rate of compression. Seed protein clusters are further extended by adding related proteins. Extended seed clusters include a significant part of the data and represent all major known cell machinery. The remaining part, coming from either non-conservative (unique) or rapidly evolving proteins, from rare genomes, or resulting from low-quality annotation, does not group together well. Processing these proteins requires significant computational resources and results in a large number of questionable clusters.ConclusionThe developed filtering strategies allow to identify and exclude such peripheral proteins limiting the protein dataset in global clustering. Overall, the proposed methodology allows the relevant data at different levels of details to be obtained and data redundancy eliminated while keeping biologically interesting variations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1112-8) contains supplementary material, which is available to authorized users.

A new active-vision system for 3-D object recognition in robotic assembly workcells is being developed. In this system, image analysis is simplified by premarking the objects with artificial markers and utilizing a mobile camera to acquire views of the objects prespecified by markers. Hence, the objects are represented by a small set of 2-D perspective projections. In this context, an important problem to be addressed is the determination of the number of markers and their locations on the objects, and of the placement of the mobile camera to guarantee the detection of at least one marker. The authors present a solution to the optimal arrangement of markers and camera location(s), given the objects' solid models and a set of constraints. >

In multi-camera motion capture systems, determining the optimal camera configuration (camera positions and orientations) is still an unresolved problem. At present, configurations are primarily guided by a human operator’s intuition, which requires expertise and experience, especially with complex, cluttered scenes. In this paper, we propose a solution to automate camera placement for motion capture applications in order to assist a human operator. Our solution is based on the use of a guided genetic algorithm to optimize camera network placement with an appropriate number of cameras. In order to improve the performance of the genetic algorithm (GA), two techniques are described. The first is a distribution and estimation technique, which reduces the search space and generates camera positions for the initial GA population. The second technique is an error metric, which is integrated at GA evaluation level as an optimization function to evaluate the quality of the camera placement in a camera network. Simulation experiments show that our approach is more efficient than other approaches in terms of computation time and quality of the final camera network.

Wireless camera sensor networks are used mostly in research today. However, the camera location in a space of study is a problem in maximizing the coverage of the camera. Instead of being a 2D computation, this computation is modeled in 3D projected to 2D walls. This research makes use of Genetic Algorithms – a search optimization algorithm to find the best placement of camera which will yield to a maximum coverage ratio. The results of the experiment show the length of time the algorithm computed, and the obtained the least number of cameras needed for the most coverage.

Single-cell RNA sequencing scRNA-seq provides critical insights into cellular diversity, essential for understanding complex biological dynamics. Traditional scRNA-seq analysis employs unsupervised clustering methods and supervised learning-based approaches to interpret cells or cell clusters. However, both of these approaches have limitations. Unsupervised learning-based methods struggle with selecting a single resolution, limiting their ability to reveal the multi-layered nature of cellular diversity. On the other hand, supervised learning-based methods lack the flexibility to adjust to the various levels of resolution needed to fully capture the complex spectrum of cell types and states. In response to these challenges, hierarchical clustering has emerged as a superior technique. It enables detailed exploration across various resolutions without predefined cluster counts, thus overcoming the limitations of both unsupervised and supervised methods. Nevertheless, the highdimensional nature of scRNA-seq poses significant analytical challenges. We introduce ALLSTATE, a novel pipeline that utilizes non-linear transition embedding for dimension reduction, facilitating hierarchical clustering in a computationally efficient manner. Our experiments demonstrate that ALLSTATE achieves satisfactory clustering performance and allows to explore the connections between cellular hierarchies and cell types at multiple levels of resolution. Additionally, ALLSTATE effectively captures complex cellular differentiation paths, offering a nuanced view of cellular heterogeneity with performance comparable to mainstream methods.

Multiresolution data representations provide an indispensable tool for the compression, progressive transmission, and visualization of scientific data. Wavelet transforms based on B-spline scaling functions are frequently used to obtain continuous surface- and volume representations at multiple levels of resolution. Starting with a fine-resolution data set, wavelet decomposition provides a sequence of coarser approximations based on B-spline scaling functions and a set of wavelet coefficients containing the geometric differences with respect to the finer levels. The inverse transform can be used to reconstruct the finer levels of resolution from these wavelet coefficients within linear computation time. Biorthogonal wavelet transforms facilitate local computation of decomposition and reconstruction. We survey the most relevant results regarding wavelets and present different biorthogonal wavelet constructions based on highly efficient and simple-to-use lifting operations. Our approaches are suitable for lossy and lossless compression of large-scale data sets.

Given a water distribution network, where should we place sensors toquickly detect contaminants? Or, which blogs should we read to avoid missing important stories?.These seemingly different problems share common structure: Outbreak detection can be modeled as selecting nodes (sensor locations, blogs) in a network, in order to detect the spreading of a virus or information asquickly as possible. We present a general methodology for near optimal sensor placement in these and related problems. We demonstrate that many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of submodularity. We exploit submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm. We also derive online bounds on the quality of the placements obtained by any algorithm. Our algorithms and bounds also handle cases where nodes (sensor locations, blogs) have different costs.We evaluate our approach on several large real-world problems,including a model of a water distribution network from the EPA, andreal blog data. The obtained sensor placements are provably near optimal, providing a constant fraction of the optimal solution. We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude. We also show how the approach leads to deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions.

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.

A method is presented for robust phase unwrapping based on instantaneous frequency estimation at multiple resolutions. Estimation is done of the frequency difference between resolution levels, resulting in asymptotically zero bias in the aliasing error. The frequency for the highest resolution level is found by the sum of difference frequencies, and the final frequency estimates are input to a least squares phase reconstruction algorithm. A hierarchical algorithm for frequency estimation at all resolution levels, based on the correlation Doppler centroid estimator, is derived. Also, an adaptive multiresolution approach is presented, in which the highest resolution level required for a frequency estimate without aliasing and thus a smooth phase reconstruction, is determined at each point. The method is investigated by simulations, and results show successful phase unwrapping without slope bias, even in the presence of undersampling and low coherence.

Conflict-Driven Clause Learning (CDCL) SAT solvers can automatically solve very large real-world problems. IntSat is a new technique extending CDCL to Integer Linear Programming (ILP). For some conflicts, IntSat generates no strong enough ILP constraint, and the backjump has to be done based on the conflicting set of bounds. The techniques given in this article precisely analyze how and when that set can be translated into the required new ILP constraint. Moreover, this can be done efficiently. This obviously strengthens learning and significantly improves the performance of IntSat (as confirmed by our experiments). We also briefly discuss extensions and other applications.

When neural networks are applied to large scale real-world classification problems, a major drawback is its inefficiency in utilizing network resources. A natural approach to overcome this drawback is to decompose the problem into several smaller sub

Discrete-space agglomeration model with social interactions: Multiplicity, stability, and continuous limit of equilibria

The efficient management (placement and orientation) of security cameras within a floor plan is a well-known and difficult problem that has gained attention recently. The objective is to locate the minimum number of cameras in the space to ensure all walls are within the view of at least one camera. Heuristic-based approaches have been developed for this NP-hard problem, unfortunately, most are only applicable to static situations. In modern applications, surveillance management must be resilient, and adapt if the environment changes. This paper introduces evolutionary-based approaches for active surveillance camera management. Using an evolutionary-based approach, a surveillance configuration (camera locations and orientations) is encoded as a chromosome and evolutionary processes are applied to identify better solutions over successive generations. The approach has the ability to identify efficient surveillance configurations (minimum number of cameras with maximum coverage), however, another advantage is the ability to adapt if the environment unexpectedly changes. Simulation results demonstrate this type of approach can manage surveillance cameras under dynamic conditions such as camera loss and the introduction obstacles better than traditional search methods.