Summaries: the best scientific articles for IT, logistics and technology summarized

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The focus of this article is on Human Machine Interface (HMI) as main communication tool between any vehicle and the human driver. HMI includes all kinds of displays, visual and auditory, all vehicle controls that provide the communication and feedback between vehicle and driver. HMI is from even greater importance in fully automatic cars, providing mutual understanding and trust. In automated vehicles, HMI has to inform and support the human about its capabilities.

In this article, goals of HMI will be established, based on “ten challenges”, of Klein et al. (2004), which are based on the four principles of proper human-agent activity, The four principles are an agreement that actors will work together, actors must be mutually predictable in their actions, actions must be mutually directable and a common ground has to be maintained. A proposal for fully automated vehicle HMI-system will be designed

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The ability to predict the future is in some cases seen as a form of intelligence. Our brains are adjusted so that they can quickly compare the different outcomes of our actions. How do they do this?

Cully et al. show that robots can learn to recover quickly from physical damage. The underlying idea is that they must adopt a new strategy to be able to continue. This looks like instinct, but what the robots do is compare their previously learned strategies with each other so that the best strategy is chosen.

Three things are needed to make an accurate prediction about your behavior: experience, understanding how the world works and being able to judge how your own actions contrast with those of others.

Previous studies state that the ability to plan depends on the ability to program a representation of the world in a robot. If this

The subject that is most researched of the human brain is vision. However, all available data shows that we still know very little about visual processes. The purpose of this paper is to represent the biological aspects of vision.

Anatomy

The anatomy of the visual system is structured as follows. There are different lobes in the brain and all parts in the brain are connected to each other in a complex way. The visual cortex occupies 40% of the brain. The visual system can be divided into two main streams. First the visual information comes from the eye to the V1 area via the thalamus. From there it can go to the occipital

This article provides a graphical perspective on the arithmetic challenges of object recognition. It also looks at which neuronal population is responsible for the representation of objects. Our daily activities are accompanied by quick and accurate recognition of visual stimuli; this way we can recognize thousands of objects within seconds. However, which brain mechanisms are involved in this process is still unknown.

Object recognition is defined as being able to accurately distinguish objects or categories from all kinds of possible stimuli. This is done via the retina through an identification-prescribing transformation. Object recognition is difficult for a variety of reasons. The main reason is that every object can produce an infinite number of different images on the retina, while this image is still recognized

The branch of computer science concerned with symbolic, (non)algorithmic methods for problem solving is called artificial intelligence (AI). An algorithm is a procedure that guarantees to find a fitting solution for a problem or that there is no solution. For many years, computers have been doing numerical calculations for data processing. However, knowledge about a certain subject is rarely numerical, but rather symbolical. Therefore, its problem-solving methods are rarely mathematical. It relies on heuristics, that are not guaranteed to work, but often find solutions quicker than trial-and-error. MYCIN is an

Neurophysiological measurements of delayed or selective visual responses are used to determine how long visual processes take. With the help of these measurements, certain parts of the brain and their functions were discovered. These studies are mainly about brain processes. The current article is about face recognition.

Problems with ERPs

Face recognition is measured with ERPs. However, there are several problems when measuring with ERPs. A problem is, for example, that faces are recognized through highly specialized neuronal pathways. No studies are known that have carried out the precise neuron measurements. A second problem is that with a measured response, the face recognition process may not yet be complete.

The purpose of this article is to provide an overview of how perceptrons classify patterns, and to highlight the importance of squashing functions such as activation functions and the learning opportunities of perceptrons.

Basic principles of perceptrons

A perceptron is a neural network in which neurons from different layers are connected to each other. A basic perceptron is shown in Figure 1 of the article. The network consists of two input neurons (x and y) and one output neuron (U). In general, perceptrons can have multiple input neurons and multiple output neurons. The output of neuron x is given by its output activation

Object recognition takes place in the ventral visual system in the cortex. This system runs from the visual V1 area to IT. From there, there are connections with the PFC that ensure that perception and memory are connected. The further down the path, the more specific the neurons are and the greater their receptive fields. Plasticity and learning are probably present in all phases of object recognition.

It is not known what feedback will be given from the phases. The hypothesis is that the basic processes of information go feedforward and are supported by short time limits that are required for specific responses. However, this hypothesis does not exclude feedback loops. The feedforward architecture is a reasonable starting point for the theory of the visual cortex aimed at explaining