In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly, enabling them to pinpoint potential locations where people might be trapped. Drones can cover larger areas in shorter timeframes compared to ground-based rescue efforts or even specially trained search dogs. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio “signatures.” Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

Experimental investigations of various dichotomous classification algorithms are carried out. Dichotomous classification, or Error-Correcting Output Codes (ECOCs) classification, is based on the construction of a binary code matrix. The rows of the matrix contain unique codewords of classes, and columns are called dichotomies. A dichotomous classification consists of two stages: coding (construction of a code matrix) and decoding, making a decision on the correspondence of an object to a class by analyzing the code matrix. In this study, an experimental comparison of newly proposed methods for constructing dichotomies and a comparison of different approaches to decoding by the available code matrix are proposed. Preliminary experiments show the prospects of proposed methods.

Mathematical science in tight integration with the natural and life sciences provide sophisticated models and algorithms of knowledge acquisition. Overall rapid development of nowadays science and technologies is a direct consequence of such interdisciplinary developments. The same time, there exist particular research domains, e.g., the biological research domain, that appears as a semi-exact discipline (except of its genomics part). This is due to insufficient level of readiness of mathematical, physical and chemical sciences, being in power to provide the proper interpretation of the real size biological interrelations. Enrichment of mathematics to this new sophisticated level may be a long lasting but unavoidable process. Current series of studies is driven by a number of applied problems that are focused around the unique integrative technique of getting knowledge from fragmented data and descriptions. A large number of links connect these ideas to the problems of: artificial intelligence, intelligent information management, high performance computation, and other research domains. Our objective is in provision of integrated solutions of inverse type combinatorial problems that will help to sustain a set of applied problems. The base set of applied problems we consider involves the radiation therapy planning, the wireless sensor network integrated-connectivity-coverage protocols with its decentralized management, and the network tomography scenario devoted to the issue of energy minimization in networks. As we will see the necessary mathematical technique of solving these problems is focused around the model description by the sets of constraints and relations, by integration of the partial knowledge about these models. Two scenarios have been considered. One is based on the use of projections and their interpretation, and the second is based on the local neighbourhood analysis (generic projections). Given projections and/or neighbourhoods (in inverse manner), it is to analyse the consistency issue; to know the properties of the set of solutions; and to reconstruct one or all solutions of these problems. In mathematical level the problem is related to the well-known open problems such as the Berge’s hypothesis about the simple hypergraphic degree sequences. The technique to be used in investigations involves Boolean domain studies and the n-cube geometry, Lagrangean relaxation together with integer linear programming, Minkowski geometry, Voronoi diagrams, and the Constraint satisfaction mathematics. Prototype solutions and demonstrations to the mentioned applied problems will be provided.

Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatica, Title: Shadow Minimization Boolean Function Reconstruction, Authors: Levon Aslanyan, Gyula Katona, Hasmik Sahakyan , A special class of monotone Boolean functions coming from shadow minimization theory of finite set-systems – KK-MBF functions – is considered. These functions are a descriptive model for systems of compatible groups of constraints, however, the class of all functions is unambiguously complex and it is sensible to study relatively simple subclasses of functions such as KK-MBF. Zeros of KK-MBF functions correspond to initial segments of lexicographic order on hypercube layers. This property is used to simplify the recognition. Lexicographic order applies priorities over constraints which is applicable property of practices. Query-based algorithms for KK-MBF functions are investigated in terms of their complexities.

This paper investigates the feasibility of using reinforcement learning to solve combinatorial optimization problems, in particular, the problem of query-based monotone Boolean function reconstruction. The monotone Boolean function reconstruction problem is a typical combinatorial problem that reconstructs the function unambiguously with a minimum number of queries about the value of the function at the defined points, based on the monotonicity of the function. The Shannon complexity of the problem is of the order of 2^n/sqrt(n), and the solution algorithm relies on complex constructions, which also add complexity in the form of memory and time. Additionally, there are problems of partial reconstruction, e.g., in the mining of associative rules, which do not fit into the developed solution formats. This necessitates exploring heuristic domains to attract additional resources to solve the problem. To this end, all elements of reinforcement learning - environment, agent, policy, etc. - are designed, and both exact and approximate algorithms are given to perform the necessary structural data transformations, as well as to calculate the reward, the value, and other operational data of the algorithm. The focal point of the considerations is a subclass of monotone Boolean functions related to the well-known shadow minimization theorem of layer-by-layer characterized functions. Preliminary experiments have been started and they require follow-up intensive actions.

A pattern recognition scenario, where instead of object classification into the classes by the learning set, the algorithm aims to allocate all objects to the same, the so-called "normal" class, is the research objective. Given the learning set L; the class K0 is called “normal”, and the reminder l classes K1, K2, ... , Kl from the environment K are “deviated”. The classification algorithm is for a recurrent use in a "classification, action" format. Actions Ai are defined for each “deviated” class Ki. Applied to an object x ∈ Ki, the action delivers update Ai(x) of the object. The goal is in constructing a classification algorithm A that applied repeatedly (small number of times) to the objects of L, moves the objects (correspondingly, the elements of K) to the “normal” class. In this way, the static recognition action is transferred to a dynamic domain. This paper is continuing the discussion on the “normal” class classification problem, its theoretical postulations, possible use cases, and advantages of using logical-combinatorial approaches in solving these dynamic recognition problems. Some light relation to the topics like reinforcement learning, and recurrent neural networks are also provided.