Aznul Qalid Md Sabri

Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

PD Control Scheme with Region Formulation for Slow Time-Varying Tracking Control of an Unmanned Aerial Vehicle

This paper aims to investigate whether simple proportional and derivative (PD) controller works for slow time-varying tracking control an under- actuated vehicle. The control scheme that consists of PD controller and also region formulation has been proposed for a quadrotor aerial vehicle due to the advantages of simplicity and ease of implementation. A Lyapunov-like function is presented for stability analysis of the proposed control law. Numerical simulations are presented to demonstrate the performance of the proposed tracking control of the under-actuated vehicle.

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.

Image pattern recognition in big data: taxonomy and open challenges: survey

Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research.

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.

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.