The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.

The object of the studies conducted in the years 2000–2002 on a field of 3 years’ monoculture of soybean was rhizosphere soil of soybean cultivated after tansy phacelia, winter wheat, white mustard, rye, agrimony and soybean as previous crops. The purpose of the studies was to determine the effect of cultivating the above listed previous crop plants on the formation of microorganism communities in the rhizosphere soil of soybean. The lowest total number of fungal colonies was found in the rhizosphere soil of soybean cultivated after rye and winter wheat (21.09 × 10³ and 22.58 × 10³ c. f. u., respectively), while the highest number was found in soil after soybean (36.95 × 10³ c. f. u.). The highest total number of bacteria was found in 1 g of dr yweight of the rhizosphere soil of so ybean cultivated after agrimony, and the lowest after soybean (5.80 × 10⁶ and 4.09 × 10⁶ c. f. u., respectively). The largest proportion of pathogenic fungi was characteristic of the rhizosphere soil of soybean cultivated after soybean, and the smallest – of the rhizosphere soil of soybean after agrimony as a previous crop. The dominating species among pathogenic fungi in all experimental objects was Fusarium oxysporum. The rhizosphere soil of soybean cultivated after soybean was the poorest in saprophytic fungi (35.2% of all isolations). On the other hand, the highest number of saprophytes, including antagonistic ones, was found in the rhizosphere soil of soybean after agrimony and winter wheat.

Visible Light Communication (VLC) is a technique for high-speed, low-cost wireless data transmission based on LED luminaries. Wireless LAN environments are a major application of VLC. In these environments, VLC is used in place of traditional systems such as Wi-Fi. Because of the physical characteristics of visible light, VLC is considered to be superior to traditional radio-based communication in terms of security. However, as in all wireless systems, the security of VLC with respect to eavesdropping, signal jamming and modification must be analyzed. This paper focuses on the aspect of jamming in VLC networks. In environments where multiple VLC transmitters are used, there is the possibility that one or more transmitters will be hostile (or “rogue”). This leads to communication disruption, and in some cases, the hijacking of the legitimate data stream. In this paper we present the theoretical system model that is used in simulations to evaluate various rogue transmission scenarios in a typical indoor environment. The typical approach used so far in jamming analysis assumes that all disruptive transmissions may be modeled as Gaussian noise, but this assumption may be too simplistic. We analyze and compare two models of VLC jamming: the simplified Gaussian and the exact model, where the full characteristics of the interfering signal are taken into account. Our aim is to determine which methodology is adequate for studying signal jamming in VLC systems.