Ali Mohammed Mansoor

A novel cell-selection optimization handover for long-term evolution (LTE) macrocellusing fuzzy TOPSIS

Optimal cell-selection scheme that allows a roaming UE to reconnect to the most suitable cell while maintaining its quality of service (QoS) requirements.Very low ping-pong handover ratio and handover failure ratio.Higher cell throughput gain.Considering uplink and downlink conditions make a reliable radio link connection.A comprehensive investigation of proposed scheme at varying UE speeds is demonstrating its robustness. To satisfy the demand for higher data rate while maintaining the quality of service, a dense long-term evolution (LTE) cells environment is required. This imposes a big challenge to the network when it comes to performing handover (HO). Cell selection has an important influence on network performance, to achieve seamless handover. Although a successful handover is accomplished, it might be to a wrong cell when the selected cell is not an optimal one in terms of signal quality and bandwidth. This may cause significant interference with other cells, handover failure (HOF), or handover ping-pong (HOPP), consequently degrading the cell throughput. To address this issue, we propose a multiple-criteria decision-making method. In this method, we use an integrated fuzzy technique for order preference by using similarity to ideal solution (TOPSIS) on S-criterion, availability of resource blocks (RBs), and uplink signal-to-interference-plus-noise ratio. The conventional cell selection in LTE is based on S-criterion, which is inadequate since it only relies on downlink signal quality. A novel method called fuzzy multiple-criteria cell selection (FMCCS) is proposed in this paper. FMCCS considers RBs utilization and user equipment uplink condition in addition to S-criterion. System analysis demonstrates that FMCCS managed to reduce handover ping-pong and handover failure significantly. This improvement stems from the highly reliable cell-selection technique that leads to increased throughput of the cell with a successful handover. The simulation results show that FMCCS outperforms the conventional and cell selection scheme (CSS) methods.

A Joint Evaluation of Energy-Efficient Downlink Scheduling and Partial CQI Feedback for LTE Video Transmission

A green cellular technology is proposed to optimize the energy and spectrum resources. Such optimization will require perfect channel state information at the transmitting base station. However, reporting the channel status of the entire bandwidth requires huge undesirable feedback overhead. Therefore, the aim of this paper is to optimize the energy and bandwidth resources while maintaining quality-of-service at the downlink when a partial feedback is considered. In this paper, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB to optimize the energy and spectrum resources. On the user equipment side, a partial channel feedback scheme based on an adaptive feedback threshold is developed. A primary concern of this feedback scheme is to reduce the uplink signaling overhead without a substantial loss in downlink performances. Finally, the downlink packet scheduling and the partial feedback are jointly evaluated to further enhance the system performance. Based on a system-level simulation results, the proposed energy-efficient scheduling with partial feedback has achieved an improvement in EE of up to 79% compared to the PPM scheduler. Besides, it minimizes the degradation caused by the partial channel quality indicator feedback. Thus, the proposed two-sided algorithm gives the best tradeoff between uplink and downlink performances.

SLAMM: Visual monocular SLAM with continuous mapping using multiple maps

This paper presents the concept of Simultaneous Localization and Multi-Mapping (SLAMM). It is a system that ensures continuous mapping and information preservation despite failures in tracking due to corrupted frames or sensor’s malfunction; making it suitable for real-world applications. It works with single or multiple robots. In a single robot scenario the algorithm generates a new map at the time of tracking failure, and later it merges maps at the event of loop closure. Similarly, maps generated from multiple robots are merged without prior knowledge of their relative poses; which makes this algorithm flexible. The system works in real time at frame-rate speed. The proposed approach was tested on the KITTI and TUM RGB-D public datasets and it showed superior results compared to the state-of-the-arts in calibrated visual monocular keyframe-based SLAM. The mean tracking time is around 22 milliseconds. The initialization is twice as fast as it is in ORB-SLAM, and the retrieved map can reach up to 90 percent more in terms of information preservation depending on tracking loss and loop closure events. For the benefit of the community, the source code along with a framework to be run with Bebop drone are made available at https://github.com/hdaoud/ORBSLAMM.

An Energy-Efficient User-Centric Approach High-Capacity 5G Heterogeneous Cellular Networks

Today’s cellular networks (3G/4G) do not scale well in heterogeneous networks (HetNets) of multiple technologies that employ network-centric (NC) model. This destabilization is due to the need for coordination and management of multiple layers of the HetNets that the NC models cannot provide. User-centric (UC) approach is one of the key enablers of 5G wireless cellular networks for rapid recovering from network failures and ensuring certain communication capability for the users. In this paper, we present resource-aware energy-saving technique based on the UC model for LTE-A HetNets.We formulate an optimization problem for UC as a mixed linear integer programming (MILP) that minimizes the total power consumption (Energy Efficiency) while respecting the data rate per user and propose a low complexity iterative algorithm to user terminal (UE)-eNodeB association. In UC model, UE possessing terminal intelligence can establish the transmission and reception with different cells within the LTEA HetNet assuming the existence of coordination between the different cells in the network. The performance is evaluated in terms of energy saving in the uplink and downlink and the added capacity to the network (data rate). The evaluation is carried out by comparing a UC model against a NC model with the same simulation setup. The results show significant percentage of energy saving at eNodeBs and UEs in a UC model. Also, system capacity is enhanced in the UC model in both the uplink and downlink due to utilizing best channel gain for transmission and reception.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELMs training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

A Feedback-based Admission Control Unit for QoS provision of video Transmission over WLANs

The IEEE 802.11e standard intends to enhance the Quality of Service (QoS) by introducing Hybrid Coordination Function Controlled Channel Access (HCCA). In HCCA, the QoS-enabled Station (QSTA) is assigned a Transmission Opportunity (TXOP) based on TS Specification (TSPEC) assigned during traffic setup time. Allocating fixed TXOP is only efficient for scheduling Constant Bit Rate (CBR) applications. However, Variable Bit Rate (VBR) traffics are not adequately supported via this approach, as its usage results in non-deterministic traffic profile. More specifically, this leads to degradation in the performance of the multimedia transmission and reduction in the number of admitted traffics. In this work, we propose an efficient admission control unit, called Feedback-based Admission Control Unit (FACU). The proposed scheme aims at maximizing the utilization of the surplus bandwidth which has never been tested in previous schemes. The FACU exploits piggybacked information containing size of subsequent video frames to increase the number of admitted flows. The proposed method is analytically evaluated using different video sequences. The results show that the FACU maximizes the number of admitted video streams comparable to other proposed techniques without jeopardizing the assigned QoS constraints.

A Brief Survey on 5G Wireless Mobile Network

The new upcoming technology of the fifth generation wireless mobile network is advertised as lightning speed internet, everywhere, for everything, for everyone in the nearest future. There are a lot of efforts and research carrying on many aspects, e.g. millimetre wave (mmW) radio transmission, massive multiple input and multiple output (Massive-MIMO) new antenna technology, the promising technique of SDN architecture, Internet of Thing (IoT) and many more. In this brief survey, we highlight some of the most recent developments towards the 5G mobile network.

Performance Evaluations Power Consumption, and Heterogeneousity of WSNs in Medical Field

With the rapid development of technology, and the prevalence of the aging population, researchers are focusing on how these technology can aid medical care, especially, the care of older people. Thus, the technologist strife to develop sensors and other peripherals to address the demands in our life, and to achieve patients’ satisfaction. On the other hand, clinical staff like doctors and nurses should be able to handle technology in as simple a way as possible. Nowadays we able to communicate with our peripheral environment by using different sensors and gateways. The aim of this paper is to report and survey the main applications of wireless sensors networks, which are power efficient and heterogeneous in the medical field. We attempt to show the relationship and collaboration between healthcare, engineering and the computer science fields, we will illustrate the new technologies, how they are evaluated, and what are the simulators and hardwires they use. The advantage for the development of sensors and communications and using heterogeneous at medical field make the monitoring easier, faster and efficiency.

Automatic Rotation Recovery Algorithm for Accurate Digital Image and Video Watermarks Extraction

Research in digital watermarking has evolved rapidly in the current decade. This evolution brought various different methods and algorithms for watermarking digital images and videos. Introduced methods in the field varies from weak to robust according to how tolerant the method is implemented to keep the existence of the watermark in the presence of attacks. Rotation attacks applied to the watermarked media is one of the serious attacks which many, if not most, algorithms cannot survive. In this paper, a new automatic rotation recovery algorithm is proposed. This algorithm can be plugged to any image or video watermarking algorithm extraction component. The main job for this method is to detect the geometrical distortion happens to the watermarked image/images sequence; recover the distorted scene to its original state in a blind and automatic way and then send it to be used by the extraction procedure. The work is limited to have a recovery process to zero padded rotations for now, cropped images after rotation is left as future work. The proposed algorithm is tested on top of extraction component. Both recovery accuracy and the extracted watermarks accuracy showed high performance level.

A hybrid classification algorithm approach for breast cancer diagnosis

Early diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic Algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique for kNN by selecting best features as well as optimization of the k value, while the kNN is used for classification purpose. The planned algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases using different datasets in which the first is Wisconsin Breast Cancer Database (WBCD) and the second one is Wisconsin Diagnosis Breast Cancer (WDBC) which has changes in the number of attributes and number of instances. The proposed algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved 99% accuracy.