Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

Nonlinear effects in the optical transmission systems (OTSs) are considered as the major performance limiting factor to provide high transmission rates over ultra-long distances. As the demands for system capacity, transmission range and the number of users is increasing exponentially with the development of mobile broadband, new challenges are being faced by the backbone optical networks. Mainly, the refractive index related non-linearities (RIrNLs) need to be characterized to design an optimal OTS for error-free transmission with provision of wavelength division multiplexing (WDM) to support for multiple channels. This paper provides an estimation technique of RIrNLs for long-haul transmission and their treatment for different channel spacing and the number of channels in a WDM system operating frequency domain multiple in multiple out (FD-MIMO) equalizer based digital signal processing (DSP) receiver and microstrip Chebyshev low pass filter. The main focus of this work is to utilize the existing structure of OTS for RIrNLs treatment with a low cost solution. Thus, by varying the parameters of the third order dispersion parameters, group velocity dispersion parameters, phase modulation dispersion and nonlinear refractive index, the impact of RIrNLs is investigated in detail to enhance the transmission range and capacity of the current OTS. The proposed system is analyzed in terms of range of input power, fiber length and received power for OTS figure of merits including bit error rate (BER) and optical signal-to-noise ratio (OSNR). Using duo-binary modulation, the BER achieved in this work is <${10}^{-5}$ till 500 km range, for maximum number of 32 channels, with 100 Gbps aggregate data rate, which shows the feasibility and effectiveness of our proposed model.

Currently, scientific research shows an increasing use of artificial intelligence technology to model various types of phenomena. One of such issues is the modeling of the output signals from special electric surge generator. The authors in earlier work presented the model of the output voltage signal from such a generator. This model used the technology of artificial neural networks. This article presents the model of the output current signal from the surge generator. This model also uses an artificial neural network. Both of them, with suboptimal structure, the above models have 1 hidden layer and 4 neurons. The simulation tests of these models showed their high accuracy. Having the model of the output voltage and current from the surge generator available, it is possible to simulate the influence of such signals on various elements of electrical circuits. This can be used, for example, for testing of projected surge protection systems.

The railway transport system is a key factor supporting industrialization in all aspects of human activity. However, in order not to lose its importance, it must meet the challenge of modern civilization. The safety, reliability, and efficiency of railway transport, to a large degree, depend on using highly integrated electronics, which are very sensitive to various disturbances generated in the electric traction system and train or coming from the environment. One of the sources of electromagnetic disturbances are high-voltage (HV) power lines running close to the railway infrastructure. The purpose was to assess the electromagnetic impact of overhead HV transmission lines on buried signaling cables of the railway traffic control system crossbreeding with them. The levels of voltage induced in the cable under steady state and the earth fault in the HV line at various soil resistivity were estimated. A software tool based on a hybrid numerical method that combines circuit theory and electromagnetic field theory was used for computations. It was found that very high voltages may be induced in the signaling cables during earth faults in the HV lines, which may lead to serious interference or damage to the equipment. The results provide useful knowledge for implementing modern railway traffic control systems and protection measures.

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.