Other affiliations: Cairo University
Bio: F.A. Elmisery is an academic researcher from Beni-Suef University. The author has contributed to research in topics: Speaker recognition & Vector quantization. The author has an hindex of 4, co-authored 10 publications receiving 70 citations. Previous affiliations of F.A. Elmisery include Cairo University.
••01 Jan 2003
TL;DR: A pattern matching algorithm based on HMM is implemented using Field Programmable Gate Array (FPGA) for isolated Arabic word recognition and achieved a recognition accuracy comparable with the powerful classical recognition system.
Abstract: In this work we propose a speech recognition system for Arabic speech based on a hardware/software co-design implementation approach. Speech recognition is a computationally demanding task, specially the pattern matching stage. The Hidden Markov Model (HMM) is considered the most powerful modeling and matching technique in the different speech recognition tasks. Implementing the pattern matching algorithm, which is time consuming, using dedicated hardware will speed up the recognition process. In this paper, a pattern matching algorithm based on HMM is implemented using Field Programmable Gate Array (FPGA). The forward algorithm, core of matching algorithm in HMM, is analyzed and modified to be more suitable for FPGA implementation. Implementation results showed that the recognition accuracy of the modified algorithm is very close to the classical algorithm with the gain of achieving higher speed and less occupied area in the FPGA. The proposed approach is used for isolated Arabic word recognition and achieved a recognition accuracy comparable with the powerful classical recognition system.
TL;DR: A remote robot control system is implemented that utilizes Kinect based gesture recognition as human-robot interface and the accuracy of control by human's hand motion was tested.
Abstract: In the presented work, a remote robot control system is implemented utilizes Kinect based gesture recognition as human-robot interface. The movement of the human arm in 3 d space is captured, processed and replicated by the robotic arm. The joint angles are transmitted to the Arduino microcontroller. Arduino receives the joint angles and controls the robot arm. In investigation the accuracy of control by human's hand motion was tested.
28 Nov 2012-International Journal of Advanced Research in Computer Science and Electronics Engineering
TL;DR: This paper introduces an alternative way to implement CRC hardware on FPGA to speed up the CRC calculation while maintaining a very low area, and will be suitable candidate for manycommunication protocols such as 100 Gbps Ethernet.
Abstract: This paper introduces an alternative way to implement CRC hardware on FPGA to speed up the CRC calculation while maintaining a verylow area. The traditional implementations with high data throughput have very large area. In Ourdesign weused the CRC Reduced Table Lookup Algorithm (RTLA) for achieving very low area, while using pipelined architecture for having high data throughput.In our implementation we have reached a data throughput of more than 100 Gbps when the data input width is 200 bits or more, and relatively fixed maximum frequency which make doubling the data width approximately doubles the data throughput. The proposed design will be suitable candidate for manycommunication protocols such as 100 Gbps Ethernet.
13 Dec 2005
TL;DR: A vector quantization is implemented in an FPGA and is reached almost 100% identification rate in 18.8 μs using only 22% of the slices inside the spartan 3 chip.
Abstract: Speaker identification is a challenging pattern classification task It is used enormously in many applications such as security systems, information retrieved services, etc portable identification systems are expected to be widely used in future in many purposes, such as mobile applications Implementing the identification technique using a dedicated hardware could be very useful to achieve smart units In this context, the Field Programmable Gate Array (FPGA) offer an efficient technology to realize a pattern classification strategy A speaker identification system can be implemented using many classification approaches, one of these, the vector quantization technique (VQ), which is considered one of the most powerful classification techniques In this paper a vector quantization is implemented in an FPGA We have reached almost 100% identification rate in 188 μs using only 22% of the slices inside the spartan 3 chip
••01 Dec 2018
TL;DR: A prototype robotic system for robot - aided radiologists in their work, especially radiologists whom using ultrasound devices, including master device, slave robot and a computer control system that lets the operator remotely position the ultrasound probe on the patient.
Abstract: the interest in robots and their applications has increased recently, and one of the most important applications is using robots in medical applications. This paper shows a prototype robotic system for robot - aided radiologists in their work, especially radiologists whom using ultrasound devices. The system includes master device, slave robot and a computer control system that lets the operator remotely position the ultrasound probe on the patient, Master device is available with the operator; Slave robot is located in the patient’s location with an ultrasound machine.The proposed system composed of a new design of master device and slave robot; both designs were designed based on the shape, size and movements that radiologists used to do during an ultrasound examination, the master device is used to control the movement of ultrasound probe, the ultrasound probe is fixed inside the slave robot and its contacted to the patient body through a hole in the base at end of design. Movement accuracy between master device and slave robot was achieved. Ultrasound examination was tested by homemade gelatin model.
TL;DR: This paper discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems.
Abstract: Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.
••01 Dec 2015
TL;DR: Identification of important design features like feeding techniques, advantages and limitations of control methods of meal assistance robots and different inputs signals are comprehensively discussed.
Abstract: Need of assistive robots for performing activities of daily living is increasing with the reduction of labor force in the welfare and nursing care. Self-feeding or eating is one of the primary activities of a human in his/her daily life. Devices such as assistive robots for self-feeding have been developed as a solution for this problem. The purpose of this paper is to review existing meal assistance robots. In the paper, identification of important design features like feeding techniques, advantages and limitations of control methods of meal assistance robots and different inputs signals are comprehensively discussed. Challenges for developing meal assistance robots and potential future directions are also discussed at the end.
TL;DR: A speech recognition system that allows arm‐disabled students to control computers by voice as a helping tool in the educational process and achieves higher recognition rates than other relevant approaches.
Abstract: Over the previous decades, a need has emerged to empower human-machine communication systems, which are essential to not only perform actions, but also obtain information especially in education applications. Moreover, any communication system has to introduce an efficient and easy way for interaction with a minimum possible error rate. The keyboard, mouse, trackball, touch-screen, and joystick are all examples of tools which were built to provide mechanical human-to-machine interaction. However, a system with the ability to use oral speech, which is the natural form of communication between humans instead of mechanical communication systems, can be more practical for normal students and even a necessity for arm-disabled students who cannot use their arms to handle traditional education tools like pens and notebooks. In this paper, we present a speech recognition system that allows arm-disabled students to control computers by voice as a helping tool in the educational process. When a student speaks through a microphone, the speech is divided into isolated words which are compared with a predefined database of huge number of spoken words to find a match. After that, each recognized word is translated into its related tasks which will be performed by the computer like opening a teaching application or renaming a file. The speech recognition process discussed in this paper involves two separate approaches; the first approach is based on double thresholds voice activity detection and improved Mel-frequency cepstral coefficients (MFCC), while the second approach is based on discrete wavelet transform along with modified MFCC algorithm. Utilizing the best values for all parameters in just mentioned techniques, our proposed system achieved a recognition rate of 98.7% using the first approach, and 98.86% using the second approach of which is better in ratio than the first one but slower in processing which is a critical point for a real time system. Both proposed approaches were compared with other relevant approaches and their recognition rates were noticeably higher.
TL;DR: A novel protocol for extracting a set of kinematic features from movements recorded with Microsoft Kinect that captures spatial and temporal features, such as height, velocity, submovements/strokes, and holds that are important in the production as well as the comprehension of manual acts.
Abstract: Action, gesture, and sign represent unique aspects of human communication that use form and movement to convey meaning. Researchers typically use manual coding of video data to characterize naturalistic, meaningful movements at various levels of description, but the availability of markerless motion-tracking technology allows for quantification of the kinematic features of gestures or any meaningful human movement. We present a novel protocol for extracting a set of kinematic features from movements recorded with Microsoft Kinect. Our protocol captures spatial and temporal features, such as height, velocity, submovements/strokes, and holds. This approach is based on studies of communicative actions and gestures and attempts to capture features that are consistently implicated as important kinematic aspects of communication. We provide open-source code for the protocol, a description of how the features are calculated, a validation of these features as quantified by our protocol versus manual coders, and a discussion of how the protocol can be applied. The protocol effectively quantifies kinematic features that are important in the production (e.g., characterizing different contexts) as well as the comprehension (e.g., used by addressees to understand intent and semantics) of manual acts. The protocol can also be integrated with qualitative analysis, allowing fast and objective demarcation of movement units, providing accurate coding even of complex movements. This can be useful to clinicians, as well as to researchers studying multimodal communication or human-robot interactions. By making this protocol available, we hope to provide a tool that can be applied to understanding meaningful movement characteristics in human communication.