Low-level Processing Research Articles

Путь к Белому морю в XIX веке. Статья 1: Состояние дорог и условия перевозок

From the Vyatka region, ways led to the north, i.e. to Arkhangelsk and the White Sea coast. Grain, flour, flaxseed, and hemp from the region’s towns – Khlynov (Vyatka), Slobodskoy, Kotelnich, and Orlov – as well as goods from the Volga and Kama regions were exported along these routes every winter for several centuries. Through dense forests and then on river barges, the cargo was transported to Arkhangelsk. Back to the south they sent fish and imported goods. This paper examines the state of roads that ran from the central part of the Vyatka region to the river piers (mainly in the 19th century). The purpose of this study is to determine how the condition of roads and roadside facilities affected interregional trade in agricultural goods. This topic is considered here in the context of the so-called road tradition of Russia (in other words, road daily life, road culture, traditional travel culture). The road tradition is the conditions, circumstances, and situations that accompanied journeys by land over long distances. It was very difficult to get with cargo to the northern river piers along cart roads, since the way was long and ran through sparsely populated wooded areas. Winter roads were full of potholes; sledges would break down, and the horses would get completely exhausted. These problems hampered the economic activity of millions of people: costs were significant while earnings were low. In addition, the transported goods were raw materials or agricultural products with low level of processing. Bad roads stimulated the search for fundamentally different ways of delivering cargo to the White Sea.

Radar Signal Processing Architecture for Early Detection of Automotive Obstacles

With the mass adoption of automotive vehicles, road accidents have become a common occurrence. One solution to this problem is to employ safety systems that can provide early warning for potential accidents. These systems alert drivers to brake or take active control of a vehicle in order to make braking safer and smoother, thereby protecting drivers and all other road traffic participants. Most such safety systems utilize millimeter-wave radar as primary sensors, and one of the main challenges is real-time data processing from multiple sensors integrated into a single passenger car. When an obstacle is too close to a vehicle, often there is insufficient time to run higher-order digital signal processing algorithms; hence, the decision to brake must be made based on low-level hardware processing only. For that purpose, a hardware generator for the early detection of automotive obstacles that does not impede the operation of higher-order signal processing algorithms is described. The proposed generator is captured in the Chisel hardware design language and a method for reducing the overall ranging latency is presented. The system constraints are calculated using an exemplary radar front-end and the proposed generator parameters. The obtained analytical results are experimentally confirmed with a prototype composed of a typical industrial radar front-end while the signal processing back-end instance of the described generator was implemented on an FPGA board. The measurements demonstrate that with the fast proximity alert, objects can be detected in less than a hundred microseconds, thus considerably reducing the system reaction delay and braking distance.

Working memory load affects intelligence test performance by reducing the strength of relational item bindings and impairing the filtering of irrelevant information

There is a broad consensus that individual differences in working memory capacity (WMC) are strongly related to individual differences in intelligence. However, correlational studies do not allow conclusions about the causal nature of the relationship between WMC and fluid intelligence. While research on the cognitive basis of intelligence typically assumes that simpler lower-level cognitive processes contribute to individual differences in higher-order reasoning processes, a reversed causality or a third variable giving rise to two intrinsically uncorrelated variables may exist. In two studies (n1 = 65, n2 = 113), we investigated the causal nature of the relationship between WMC and intelligence by assessing the experimental effect of working memory load on intelligence test performance. Moreover, we tested if the effect of working memory load on intelligence test performance increased under time constraints, as previous studies have shown that the association between the two constructs increases if intelligence tests are administered with a strict time limit. We show that working memory load impaired intelligence test performance, but that this experimental effect was not affected by time constraints, which suggests that the experimental manipulations of working memory capacity and processing time did not affect the same underlying cognitive process. Using a computational modeling approach, we demonstrated that external memory load affected both the building and maintenance of relational item bindings and the filtering of irrelevant information in working memory. Our results confirm that WMC causally contributes to higher-order reasoning processes. Moreover, they support the hypothesis that working memory capacity in general and the abilities to maintain arbitrary bindings and to disengage from irrelevant information in particular are intrinsically related to intelligence.

Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions.

It has been shown that equivariant convolution is very helpful for many types of computer vision tasks. Recently, the 2D filter parametrization technique has played an important role for designing equivariant convolutions, and has achieved success in making use of rotation symmetry of images. However, the current filter parametrization strategy still has its evident drawbacks, where the most critical one lies in the accuracy problem of filter representation. To address this issue, in this paper we explore an ameliorated Fourier series expansion for 2D filters, and propose a new filter parametrization method based on it. The proposed filter parametrization method not only finely represents 2D filters with zero error when the filter is not rotated (similar as the classical Fourier series expansion), but also substantially alleviates the aliasing-effect-caused quality degradation when the filter is rotated (which usually arises in classical Fourier series expansion method). Accordingly, we construct a new equivariant convolution method based on the proposed filter parametrization method, named F-Conv. We prove that the equivariance of the proposed F-Conv is exact in the continuous domain, which becomes approximate only after discretization. Moreover, we provide theoretical error analysis for the case when the equivariance is approximate, showing that the approximation error is related to the mesh size and filter size. Extensive experiments show the superiority of the proposed method. Particularly, we adopt rotation equivariant convolution methods to a typical low-level image processing task, image super-resolution. It can be substantiated that the proposed F-Conv based method evidently outperforms classical convolution based methods. Compared with pervious filter parametrization based methods, the F-Conv performs more accurately on this low-level image processing task, reflecting its intrinsic capability of faithfully preserving rotation symmetries in local image features.

Low-level language processing in brain-injured patients.

Assessing cognitive function-especially language processing-in severely brain-injured patients is critical for prognostication, care, and development of communication devices (e.g. brain-computer interfaces). In patients with diminished motor function, language processing has been probed using EEG measures of command-following in motor imagery tasks. While such tests eliminate the need for motor response, they require sustained attention. However, passive listening tasks, with an EEG response measure can reduce both motor and attentional demands. These considerations motivated the development of two assays of low-level language processing-identification of differential phoneme-class responses and tracking of the natural speech envelope. This cross-sectional study looks at a cohort of 26 severely brain-injured patient subjects and 10 healthy controls. Patients' level of function was assessed via the coma recovery scale-revised at the bedside. Patients were also tested for command-following via EEG and/or MRI assays of motor imagery. For the present investigation, EEG was recorded while presenting a 148 s audio clip of Alice in Wonderland. Time-locked EEG responses to phoneme classes were extracted and compared to determine a differential phoneme-class response. Tracking of the natural speech envelope was assessed from the same recordings by cross-correlating the EEG response with the speech envelope. In healthy controls, the dynamics of the two measures were temporally similar but spatially different: a central parieto-occipital component of differential phoneme-class response was absent in the natural speech envelope response. The differential phoneme-class response was present in all patient subjects, including the six classified as vegetative state/unresponsive wakefulness syndrome by behavioural assessment. However, patient subjects with evidence of language processing either by behavioural assessment or motor imagery tests had an early bilateral response in the first 50 ms that was lacking in patient subjects without any evidence of language processing. The natural speech envelope tracking response was also present in all patient subjects and responses in the first 100 ms distinguished patient subjects with evidence of language processing. Specifically, patient subjects with evidence of language processing had a more global response in the first 100 ms whereas those without evidence of language processing had a frontopolar response in that period. In summary, we developed two passive EEG-based methods to probe low-level language processing in severely brain-injured patients. In our cohort, both assays showed a difference between patient subjects with evidence of command-following and those with no evidence of command-following: a more prominent early bilateral response component.

Impairment of central language processing in critically ill coronavirus disease 2019 patients with delirium.

Accumulating evidence indicates that coronavirus disease 2019 is a major cause of delirium. Given the global dimension of the current pandemic and the fact that delirium is a strong predictor of cognitive decline for critically ill patients, this raises concerns regarding the neurological cost of coronavirus disease 2019. Currently, there is a major knowledge gap related to the covert yet potentially incapacitating higher-order cognitive impairment underpinning coronavirus disease 2019 related delirium. The aim of the current study was to analyse the electrophysiological signatures of language processing in coronavirus disease 2019 patients with delirium by using a specifically designed multidimensional auditory event-related potential battery to probe hierarchical cognitive processes, including self-processing (P300) and semantic/lexical priming (N400). Clinical variables and electrophysiological data were prospectively collected in controls subjects (n = 14) and in critically ill coronavirus disease 2019 patients with (n = 19) and without (n = 22) delirium. The time from intensive care unit admission to first clinical sign of delirium was of 8 (3.5-20) days, and the delirium lasted for 7 (4.5-9.5) days. Overall, we have specifically identified in coronavirus disease 2019 patients with delirium, both a preservation of low-level central auditory processing (N100 and P200) and a coherent ensemble of covert higher-order cognitive dysfunctions encompassing self-related processing (P300) and sematic/lexical language priming (N400) (spatial-temporal clustering, P-cluster ≤ 0.05). We suggest that our results shed new light on the neuropsychological underpinnings of coronavirus disease 2019 related delirium, and may constitute a valuable method for patient's bedside diagnosis and monitoring in this clinically challenging setting.

An Efficient Deep-Learning-Based Super-Resolution Accelerating SoC With Heterogeneous Accelerating and Hierarchical Cache

This article presents an energy-efficient accelerating system-on-chip (SoC) for super-resolution (SR) image reconstruction on a mobile platform. With the rise of contactless communication and streaming services, the need for SR is growing. As one of the most basic low-level image processing algorithms, SR can reconstruct high-quality images from low-quality images which are noisy, compressed, or with damaged pixels. However, a massive amount of computation and considerable precision of pixel data pose challenges for acceleration in a resource and bandwidth constrained platform. SR has high energy consumption and long latency. While previous neural processing units (NPUs) reduced the precision to increase the efficiency and accelerate convolutional neural network (CNN) computation, few of them concentrated on both the output image quality and the performance of the entire system. The proposed SR SoC restores the high-quality image using a precision-optimized SR algorithm on an energy-efficient accelerating architecture and cache subsystem. It contributes three algorithm-hardware co-optimized features: 1) heterogeneous accelerating architecture (HAA) with only 8-bit floating-point (FP)-and-fixed-point (FXP) hybrid-precision for SR task; 2) tile-based hierarchical cache (THC) subsystem for the low energy and small footprint cost layer fusion; and 3) heterogeneous L1 data lifetime-aware optimized cache (DLOC) for the energy-efficient on-chip memory access. The prototype of SR SoC is fabricated in 65-nm technology and occupies a 10.0-mm2 die area. The proposed SR SoC can maintain the high reconstruction quality while consuming only 19% of the energy of an FXP16 system with homogeneous NPU. As a result, the SR SoC presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.6\times $ </tex-math></inline-formula> higher energy efficiency than the previous SR targeting NPU and achieves 107-frame-per-second (fps) framerates running <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> SR image generation to full high definition (FHD) scale at only 0.92-mJ/frame energy consumption.

Assessing Functional Brain Network Dynamics in Dyslexia from fNIRS Data.

Developmental dyslexia is characterized by a deficit of phonological awareness whose origin is related to atypical neural processing of speech streams. This can lead to differences in the neural networks that encode audio information for dyslexics. In this work, we investigate whether such differences exist using functional near-infrared spectroscopy (fNIRS) and complex network analysis. We have explored functional brain networks derived from low-level auditory processing of nonspeech stimuli related to speech units such as stress, syllables or phonemes of skilled and dyslexic seven-year-old readers. A complex network analysis was performed to examine the properties of functional brain networks and their temporal evolution. We characterized aspects of brain connectivity such as functional segregation, functional integration or small-worldness. These properties are used as features to extract differential patterns in controls and dyslexic subjects. The results corroborate the presence of discrepancies in the topological organizations of functional brain networks and their dynamics that differentiate between control and dyslexic subjects, reaching an Area Under ROC Curve (AUC) up to 0.89 in classification experiments.

Gauging response time distributions to examine the effect of facial expression inversion.

We used images of facial expressions (FEs) of emotion in a speeded Same/Different task to examine (i) distributional characteristics of response times (RTs) in relation to inter-stimulus similarity and (ii) the impact of inversion on FE processing. Stimuli were seven emotion prototypes, posed by one male and one female, and eight intermediate morphs. Image pairs (N = 225) were presented for 500 ms, upright or inverted, in a block design, each 100 times. For both upright and inverted FEs, RTs were a non-monotonic function: median values were longest for stimulus pairs of intermediate similarity, decreasing for both more-dissimilar and more-similar pairs. RTs of "Same" and "Different" judgments followed ex-Gaussian distributions. The non-monotonicity is interpreted within a dual-process decision model framework as reflecting the infrequency of identical pairs, shifting the balance between the Same and Different processes. The effect of stimulus inversion was gauged by comparing RT-based multidimensional scaling solutions for the two presentation modes. Solutions for upright and inverted FEs showed little difference, with both displaying some evidence of categorical perception. The same features appeared in hierarchical clustering solutions. This outcome replicates and reinforces the solutions derived from accuracy of "Different" responses reported in our earlier companion paper. We attribute this lack of inversion effect to the brief exposure time, allowing low-level visual processing to dominate Same/Different decisions while elevating early featural analysis, which is insensitive to face orientation but enables initial positive/negative valence categorization of FEs.

Deep Saliency Mapping for 3D Meshes and Applications

Nowadays, three-dimensional (3D) meshes are widely used in various applications in different areas (e.g., industry, education, entertainment and safety). The 3D models are captured with multiple RGB-D sensors, and the sampled geometric manifolds are processed, compressed, simplified, stored, and transmitted to be reconstructed in a virtual space. These low-level processing applications require the accurate representation of the 3D models that can be achieved through saliency estimation mechanisms that identify specific areas of the 3D model representing surface patches of importance. Therefore, saliency maps guide the selection of feature locations facilitating the prioritization of 3D manifold segments and attributing to vertices more bits during compression or lower decimation probability during simplification, since compression and simplification are counterparts of the same process. In this work, we present a novel deep saliency mapping approach applied to 3D meshes, emphasizing decreasing the execution time of the saliency map estimation, especially when compared with the corresponding time by other relevant approaches. Our method utilizes baseline 3D importance maps to train convolutional neural networks. Furthermore, we present applications that utilize the extracted saliency, namely feature-aware multiscale compression and simplification frameworks.

Feasibility of unconscious instrumental conditioning: A registered replication.

The extent to which high-level, complex functions can proceed unconsciously has been a topic of considerable debate. While unconscious processing has been demonstrated for a range of low-level processes, from feature integration to simple forms of conditioning and learning, theoretical contributions suggest that increasing complexity requires conscious access. Here, we focus our attention on instrumental conditioning, which has been previously shown to proceed without stimulus awareness. Yet, instrumental conditioning also involves integrating information over a large temporal scale and distinct modalities in order to deploy selective action, constituting a process of substantial complexity. With this in mind, we revisit the question of feasibility of instrumental conditioning in the unconscious domain. Firstly, we address the theoretical and practical considerations relevant to unconscious learning in general. Secondly, we aim to replicate the first study to show instrumental conditioning in the absence of stimulus awareness (Pessiglione etal., 2008), following the original design and supplementing the original crucial analyses with a Bayesian approach (Experiment 1). We found that apparent unconscious learning took place when replicating the original methods directly and according to the tests of awareness used. However, we could not establish that the full sample was unaware in a separate awareness check. We therefore attempted to replicate the effect yet again with improved methods to address the issues related to sensitivity and immediacy (Experiment 2), including an individual threshold-setting task and a trial-by-trial awareness check permitting exclusion of individual aware trials. Here, we found evidence for absence of unconscious learning. This result provides evidence that instrumental conditioning did not occur without stimulus awareness in this paradigm, supporting the view that complex forms of learning may rely on conscious access. Our results provides support for the proposal that perceptual consciousness may be necessary for complex, flexible processes, especially where selective action and behavioural adaptation are required.

Text Coherence Necessary for the Promotion of Japanese EFL Learner’s Predictive Inference Generation

Reading is a very complex process consisting of lower- and higher-level processing. Inference generation is necessary to achieve higher-level processing. It is challenging for Japanese EFL readers because they must use many cognitive resources in lower-level processing, such as word recognition and syntax analysis. This study investigated the relationship between Japanese EFL learners' predictive inference generation as higher-level processing and text coherence from the encoding perspective rather than activation. We used a cued recall task whose rates would be evidence of the reader's encoding of predictive inferences. The recall rates in the high predictability target version, which allowed readers to predict an outcome easily, were more significant than other text versions, and results were affected by the reader's proficiency. These findings suggest that EFL learners' encoding of predictive inference can be influenced under the specific text conditions in higher-level proficiency groups.

Altered task induced functional brain networks and small-world properties in autism.

Facial emotion recognition (FER) requires the integration of multi-dimensional information across various brain regions. Autistic individuals commonly experience difficulties in FER, a phenomenon often attributed to differences in brain connectivity. The nature of task-induced functional brain networks could provide insight into the neuromechanisms underlying FER difficulties in autism, however, to date, these mechanisms remain poorly understood. In this study, the task induced functional brain networks of 19 autistic and 19 gender, age, and IQ matched non-autistic individuals were examined during a complex FER task. Electroencephalogram (EEG)-based functional brain networks were examined, including the investigation of differences in the time-varying whole-brain functional networks and the exploration of the task induced small-world properties. The results showed statistically significant differences in the task-induced functional networks between autistic and non-autistic adults. Autistic adults compared to non-autistic adults showed a significant shift in the connectivity-based FER processing from the lower to the higher EEG frequency bands. These findings may provide evidence at a neural level for the notion that autistic individuals have a preference for bottom-up lower-level processing, or alterations in top-down global processing, potentially contributing to the FER difficulties observed in this population. Results also suggest that functional brain networks in autism show significantly altered task-induced whole-brain small-world properties as compared to non-autistic individuals during complex FER. This study motivates further investigation of the underlying networks-basis of altered emotion processing in autism.

Grounding Mental Representations in a Virtual Multi-Level Functional Framework.

According to the associative theory of learning, reactive behaviors described by stimulus-response pairs result in the progressive wiring of a plastic brain. In contrast, flexible behaviors are supposedly driven by neurologically grounded mental states that involve computations on informational contents. These theories appear complementary, but are generally opposed to each other. The former is favored by neuro-scientists who explore the low-level biological processes supporting cognition, and the later by cognitive psychologists who look for higher-level structures. This situation can be clarified through an analysis that independently defines abstract neurological and informational functionalities, and then relate them through a virtual interface. This framework is validated through a modeling of the first stage of Piaget's cognitive development theory, whose reported end experiments demonstrate the emergence of mental representations of object displacements. The neural correlates grounding this emergence are given in the isomorphic format of an associative memory. As a child's exploration of the world progresses, his mental models will eventually include representations of space, time and causality. Only then epistemological concepts, such as beliefs, will give rise to higher level mental representations in a possibly richer propositional format. This raises the question of which additional neurological functionalities, if any, would be required in order to include these extensions into a comprehensive grounded model. We relay previously expressed views, which in summary hypothesize that the ability to learn has evolved from associative reflexes and memories, to suggest that the functionality of associative memories could well provide the sufficient means for grounding cognitive capacities.

The Geometry of Causality: Multi-token Geometry of Interaction and Its Causal Unfolding

We introduce a multi-token machine for Idealized Parallel Algol (IPA), a higher-order concurrent programming language with shared state and semaphores. Our machine takes the shape of a compositional interpretation of terms as Petri structures, certain coloured Petri nets. For the purely functional fragment of IPA, our machine is conceptually close to Geometry of Interaction token machines, originating from Linear Logic and presenting higher-order computation as the low-level process of a token walking through a graph (a proof net) representing the term. We combine here these ideas with folklore ideas on the representation of first-order imperative concurrent programs as coloured Petri nets. To prove our machine computationally adequate with respect to the reference operational semantics, we follow game semantics and represent types as certain games specifying dependencies and conflict between computational events. Petri strategies are those Petri structures obeying the rules of the game extracted from the type. We show how Petri strategies unfold to concurrent strategies in the sense of concurrent games on event structures. This link with concurrent strategies not only allows us to prove adequacy of our machine, but also lets us generate operationally a causal description of the behaviour of programs at higher-order types, which is shown to coincide with that given denotationally by the interpretation in concurrent games.

Factors Affecting Teaching Ability of Physical Education Teacher Trainees and Development Strategies

Physical education teacher trainees are the main source of physical education teachers in China, and the training mode and educational philosophy of physical education teacher trainees in colleges and universities affect the personal ability of future physical education teachers. Influenced by the "disembodied education" model, education and teaching are regarded as a "higher-level process" that promotes mental development and is not related to "lower-level processes" such as physical experience, bodily sensation and sensory-motor system. It is not related to the "lower processes" such as bodily experience, body sensation, and sensory-motor system. This concept of separation of mind and body is one of the important factors that affect the development of practical ability of physical education teacher trainees. The research suggests that: universities should improve the special training sites for physical education teaching skills and optimize the curriculum of teacher education; teachers in universities should establish the teaching concept of "students' body as the main body"; teachers in universities should enrich the content and form of teaching skills evaluation; physical education teacher-training students should pay attention to the development of their teaching skills and actively participate in the teaching skills competitions held by colleges; and the teachers of colleges and universities should enrich the content and form of teaching skills evaluation; physical education students should pay attention to the development of their teaching skills and actively participate in the teaching skills competitions held by colleges.

No Link Between Speech-in-Noise Perception and Auditory Sensory Memory - Evidence From a Large Cohort of Older and Younger Listeners.

A growing literature is demonstrating a link between working memory (WM) and speech-in-noise (SiN) perception. However, the nature of this correlation and which components of WM might underlie it, are being debated. We investigated how SiN reception links with auditory sensory memory (aSM) - the low-level processes that support the short-term maintenance of temporally unfolding sounds. A large sample of old (N = 199, 60-79 yo) and young (N = 149, 20-35 yo) participants was recruited online and performed a coordinate response measure-based speech-in-babble task that taps listeners' ability to track a speech target in background noise. We used two tasks to investigate implicit and explicit aSM. Both were based on tone patterns overlapping in processing time scales with speech (presentation rate of tones 20 Hz; of patterns 2 Hz). We hypothesised that a link between SiN and aSM may be particularly apparent in older listeners due to age-related reduction in both SiN reception and aSM. We confirmed impaired SiN reception in the older cohort and demonstrated reduced aSM performance in those listeners. However, SiN and aSM did not share variability. Across the two age groups, SiN performance was predicted by a binaural processing test and age. The results suggest that previously observed links between WM and SiN may relate to the executive components and other cognitive demands of the used tasks. This finding helps to constrain the search for the perceptual and cognitive factors that explain individual variability in SiN performance.

Competitiveness of the agri-food sector of the Republic of Serbia

The competitiveness of the agri-food products is one of the key factors for promoting export and economic development, especially in developing countries. Competitiveness can be influenced by increasing productivity, applying modern knowledge, innovating processing capacities and producing a high-quality final product with high added value. The agriculture of Serbia is very important for economic growth. In order to improve the competitive advantage of the agri-food sector in foreign markets, it is important to increase its efficiency by introducing modern technologies and approaches. The subject of the paper is to examine the most important export agri-food products of Serbia, as well as the most important markets for their exports. Using the RCA and ARCA indices, the comparative advantages of these products in trade were examined, which is the goal of the research. Multiple regression was then also used to examine the impact on exports. Although significant agri-food products achieve comparative advantages, the decline in their competitiveness has a negative impact on exports. Low pricing policies, as well as inadequate product quality cannot maintain competitiveness in the long run. The structure of the export of agricultural products is also unsatisfactory, bearing in mind that they are products with a low level of processing, and low added value. Therefore, it is necessary to invest in modern equipment, develop innovative approaches, such as organic production and the introduction of innovations to improve quality standards, differentiate products and create a final product with high added value, as important prerequisites for improving competitiveness and promoting the export of the agri-food sector.

Структурно-функциональный анализ взаимодействия с организациями частного сектора экономики при ликвидации чрезвычайных ситуаций

Introduction. The article is devoted to the application of Structured Analysis and Design Technique (acronym - SADT) for the formalization of the processes of interaction of the management bodies of the emergency response with private sector organizations in the management of reserves of material resources for emergency response. The relevance lies in improving the decision-making mechanism for the use of material resources of economic entities for emergency response. The scientific significance lies in the formalization of the model of interaction between the management bodies of the Unified State System of Prevention and Liquidation of Emergency Situations and organizations of the private sector of the economy based on the methods of structural programming SADT. The practical significance lies in determining the stages of the construction of the model of interaction between the management bodies of the Unified State System of Prevention and Liquidation of Emergency Situations and organizations of the private sector of the economy, as well as determining a number of models and algorithms for the implementation of the proposed SADT model in a generalized form. The purpose of the study is to identify the relationship between the functions and structure of the elements of the system of interaction between the management bodies of the Unified State System of Prevention and Liquidation of Emergency Situations and organizations of the private sector of the economy in the management of reserves of material resources. Research methods. during the study, the SADT method of structural and functional analysis was used. In the modeling process, together with the IDEF0 standard, the IDEF3 low-level process modeling and analysis standard was used for a more detailed analysis of functional blocks (IDEF3 notation was used to create a workflow model, collect information about the state of the simulated system). Results and its discussion. The interrelation of functions and the structure of the elements of the system of interaction between the management bodies of the Unified State System of Prevention and Liquidation of Emergency Situations and organizations of the private sector of the economy in the management of reserves of material resources are revealed. A structural-functional model of interaction between the management bodies of the Unified State System of Prevention and Liquidation of Emergency Situations and organizations of the private sector of the economy in the management of reserves of material resources is proposed. The place in the general structure of the system material support systems for liquidation of emergency situations of the following models and algorithms has been determined: Decision support model for assessing the sufficiency of produced material resources to eliminate emergencies on the territory of a constituent entity of the Russian Federation; Decision support model algorithm for placing reserves of material resources in the presence of a shortage of commodity stocks; Decision support model algorithm for the formation of reserves of material resources based on the management of insurance stocks of organizations; Decision support model for the delivery of material resources to the emergency area. Conclusions. The use of structural and functional analysis methods for automating the process of managing reserves of material resources can be applied in the development of computer programs and databases for calculating the calculated parameters of the resource security of the emergency response process both in moderation mode and in real time. Keywords: modeling; SADT; IDEF0; IDEF3; reserves of material resources; business entity; liquidation of an emergency.

A low-cost AR application to control arm prosthesis

This paper presents an augmented reality application to assist with myoelectric prostheses control for people with limb amputations. For that, we use the low-cost Myo armband coupled with low-level signal processing methods specifically built to control filters’ levels and processing chain. In particular, we use deep learning techniques to process the signals and to accurately identify seven different hand gestures. From that, we have built an augmented reality projection of a hand based on AprilTag markers that displays the gesture identified by the deep learning techniques. With the aim to properly train the gesture recognition system, we have built our own dataset with nine subjects. This dataset was combined with one publicly available to work with the data of 24 subjects in total. Finally, three different deep learning architectures have been comparatively studied, achieving high accuracy values (being 95.56% the best one). This validates our hypothesis that it is possible to have an adaptive platform able to fast learn personalized hand/arm gestures while projecting a virtual hand in real-time. This can reduce the adaptation time to myoelectric prostheses and improve the acceptance levels.