An analytical framework for event mining in video data

With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.     

A new image encryption scheme with Feistel like structure using chaotic S-box and Rubik cube based P-box

Multimedia Tools and Applications - In this paper, a novel chaos-based dynamic encryption scheme with a permutation-substitution structure is presented. The S-boxes and P-boxes of the scheme are...     

Modeling and robust discrete LQ repetitive control of electrically driven robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.     

Surface characterization and biological properties of regular dentin,demineralized dentin,and deproteinized dentin

Bone autografts are often used for reconstruction of bone defects; however, due to the limitations of autografts, researchers have been in search of bone substitutes. Dentin is of particular interest for this purpose due to high similarity to bone. This in vitro study sought to assess the surface characteristics and biological properties of dentin samples prepared with different treatments. This study was conducted on regular (RD), demineralized (DemD), and deproteinized (DepD) dentin samples. X-ray diffraction and Fourier transform infrared spectroscopy were used for surface characterization. Samples were immersed in simulated body fluid, and their bioactivity was evaluated under a scanning electron microscope. The methyl thiazol tetrazolium assay, scanning electron microscope analysis and quantitative real-time polymerase chain reaction were performed, respectively to assess viability/proliferation, adhesion/morphology and osteoblast differentiation of cultured human dental pulp stem cells on dentin powders. Of the three dentin samples, DepD showed the highest and RD showed the lowest rate of formation and deposition of hydroxyapatite crystals. Although, the difference in superficial apatite was not significant among samples, functional groups on the surface, however, were more distinct on DepD. At four weeks, hydroxyapatite deposits were noted as needle-shaped accumulations on DemD sample and numerous hexagonal HA deposit masses were seen, covering the surface of DepD. The methyl thiazol tetrazolium, scanning electron microscope, and quantitative real-time polymerase chain reaction analyses during the 10-day cell culture on dentin powders showed the highest cell adhesion and viability and rapid differentiation in DepD. Based on the parameters evaluated in this in vitro study, DepD showed high rate of formation/deposition of hydroxyapatite crystals and adhesion/viability/osteogenic differentiation of human dental pulp stem cells, which may support its osteoinductive/osteoconductive potential for bone regeneration.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Nano levels detection of beryllium by a novel beryllium PVC-based membrane sensor based on 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy

A novel poly(vinyl chloride)-based 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy (PBC) with sodium tetraphenyl borate (NaTPB) as an anion excluder, benzyl acetate (BA), acetophenon (AP) and o-nitrophenyloctyl ether (NPOE) as plasticizing solvent mediators was prepared and investigated as a beryllium selective sensor. The best performance was observed with the membrane having the PVC–NaTPB–NPOE–PBC composition 30%:3%:62%:5%, which worked well over a very wide concentration range (1.0×10⁻⁷ M to 1.0×10⁻¹ M). The sensor exhibits a Nernstian slope of 29.9 mV per decade of Be²⁺ activity. The detection limit of the sensor is 7.0×10⁻⁸ M (630 ppt). The proposed electrode shows excellent discriminating ability toward Be²⁺ ion with regard to alkali, alkaline earth, transition and heavy metal ions. It was successfully applied to the determination of beryllium in a mineral sample.     

A wrapper‐based feature selection for improving performance of intrusion detection systems

Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.     

High Gain Modified Dual-Band and Dual-Circularly Polarized Array Antenna

Wireless Personal Communications - A novel design of double-layer dual-band circularly polarized array antennas (DDCPAAs) is presented in this paper. First, a DDCP single antenna is introduced as...     

Inter-cell interference in multi-tier heterogeneous cellular networks: modeling and constraints

Telecommunication Systems - Interference is the main source of capacity limitation in wireless networks. In some medium access technologies in cellular networks, such as OFDMA, the allocation of...