Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enable network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service's characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20 %

Audit Quality: Providing a Model and Investigating The Gap Between the Current Situation and The Desired Level

In this study by using the Fuzzy Delphi research methodology and getting the expert opinions, it was tried to identify indicators for improving audit quality approved by experts in order to design a suitable model for the Economy of IRAN by utilizing a confirmatory factor analysis model. Also in this research, the gap between current and desired situation of audit quality indicators in IRAN is investigated. Finally, the dimensions of audit quality are ranked in terms of importance. For this purpose, following the International Auditing and Assurance Standards Board, 60 indicators were identified. These indicators were classified in five dimensions: a. Input factors with 21 indicators; B. Process factors with 10 indicators; C. Output factors with 9 indicators; D. Key interactions with 10 indicators; and E. Contextual factors by 10 indicators. Data were analyzed by utilizing R, Amos and Super Decisions software. The findings indicate that 54 indicators have been adopted, which provide a model for improving the Audit Quality. Also the results of comparing the current and desired situation of audit quality improvement indicators shows a significant difference between the current situation of the audit quality and the desired environment in Iran. Finally, the results of ranking the dimensions affecting the improvement of audit quality Shows that process factors are in the first place of importance from the point of view of experts, input factors are in the second place, main interactions and contextual factors are both in the third place and output factors are in the fourth place

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention

Phylogenetic and Genetic Analysis of D-loop and Cyt-b Region of mtDNA Sequence in Iranian Sistani, Sarabi and Brown Swiss Cows

Cattle have an important role in primary human civilization, so molecular studies for more accurate recognition of their origin are effective to identify unknown historical aspects. Cattle can be divided in to 2 main groups including Bos Tuarus and Bos Indicus. Both types of cattle can be found in Iran; therefore study of their origin has particular importance. The aim of this study was to investigate the nucleotide sequences of Cytochrome-b (Cyt-b) and HVR1&2 loci of D-loop gene region in mitochondrial DNA of Sistani, Sarabi and Brown Swiss breeds of cattle. Twenty blood samples of each breed, from non-relative individuals were obtained from blood bank of animal science department of Faculty of Agriculture, Ferdowsi University of Mashhad. The DNA content of sample was extracted based on the guanidinium thiocianate-silicagel method. Polymerase Chain Reaction with specific designed primers was performed to amplify Cyt-b and HVR 1&2 loci with 751 and 701 bp lengths, respectively. Sequencing of amplified Cyt-b and HVR 1&2 loci were done based on Sanger method by automatic sequencer machine (ABI 3130). Nucleotide diversity in Brown Swiss, Sarabi and Sistani breeds were estimated 0.0037, 0.0024 and 0.0029, respectively. Sequences of Cyt-b and HVR 1&2 were register in National Center for Biotechnology Institute due to nucleotide differences. Results of phylogenetic test using UPGMA for both loci showed that Sarabi and Sistani breeds are belonging to first group and Brown Swiss breed to other group

Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations

In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this b

Using 3D-Printed Mesh-Like Brain Cortex with Deep Structures for Planning Intracranial EEG Electrode Placement

Surgical evaluation of medically refractory epilepsy frequently necessitates implantation of multiple intracranial electrodes for the identification of the seizure focus. Knowledge of the individual brain’s surface anatomy and deep structures is crucial for planning the electrode implantation. We present a novel method of 3D printing a brain that allows for the simulation of placement of all types of intracranial electrodes. We used a DICOM dataset of a T1-weighted 3D-FSPGR brain MRI from one subject. The segmentation tools of Materialise Mimics 21.0 were used to remove the osseous anatomy from brain parenchyma. Materialise 3-matic 13.0 was then utilized in order to transform the cortex of the segmented brain parenchyma into a mesh-like surface. Using 3-matic tools, the model was modified to incorporate deep brain structures and create an opening in the medial aspect. The final model was then 3D printed as a cerebral hemisphere with nylon material using selective laser sintering technology. The final model was light and durable and reflected accurate details of the surface anatomy and some deep structures. Additionally, standard surgical depth electrodes could be passed through the model to reach deep structures without damaging the model. This novel 3D-printed brain model provides a unique combination of visualizing both the surface anatomy and deep structures through the mesh-like surface while allowing repeated needle insertions. This relatively low-cost technique can be implemented for interdisciplinary preprocedural planning in patients requiring intracranial EEG monitoring and for any intervention that requires needle insertion into a solid organ with unique anatomy and internal targets