Voice Over LTE (VoLTE)

How does Voice over LTE (VoLTE) improve call quality compared to traditional voice calls?

Voice over LTE (VoLTE) improves call quality compared to traditional voice calls by utilizing the LTE network for transmitting voice data. This results in higher audio quality, reduced background noise, and faster call setup times. VoLTE also supports HD voice, which enhances the clarity and richness of the audio during calls, providing a more immersive communication experience for users.

Applications of Digital Audio Signal Processing in Telecommunications

How does Voice over LTE (VoLTE) improve call quality compared to traditional voice calls?

What are the specific requirements for a device to support VoLTE technology?

To support VoLTE technology, a device must meet specific requirements such as having an LTE-enabled chipset, VoLTE-compatible software, and support for the necessary codecs for voice transmission over LTE networks. Additionally, the device needs to be provisioned by the carrier to enable VoLTE services and must have adequate network coverage to ensure seamless VoLTE call connectivity.

Two Post Doctoral Researchers and One PhD Student in Advanced Medical Image Analysis

Project Description We are glad to announce the launch of a new research project based on the collaboration between the Mathematics and Data Science (MADS) research group at Vrije Universiteit Brussel (VUB) and the Centre for Reproductive Medicine at UZ Brussel (Brussels IVF). This project aims at helping the field of assisted reproductive technology (ART) by developing innovative AI-driven frameworks for the analysis of high-dimensional oocyte/embryo images. By integrating advanced deep learning and mathematical modeling, we seek to investigate, understand and potentially improve decision-making in ART procedures. The ultimate objective of this interdisciplinary research is to push the boundaries of current reproductive treatment, potentially offering new insights and tools for clinicians. Open Positions We are opening the following research positions in Digital Mathematics (DIMA), a research group chaired by Prof. Ann Dooms from MADS, VUB. 1. Post-doctoral Researchers (2 vacancies) Focus Area: Advanced deep learning and machine intelligence for medical image analysis. Duration: Full-time position for 2 years (with possibility for extending to 30 month). Starting from 1stSeptember 2024. Key Responsibilities: Conceptualize, develop and implement deep learning and mathematical modeling algorithms for analyzing high-dimensional medical images. Collaborate with embryologists and clinicians to integrate biological motivations into AI models. Publish research findings in high-impact journals and present at conferences. Requirements: PhD in Applied Mathematics, Computer Science, Electrical/Electronic/Information Engineering, or related fields. Strong background in deep learning, machine learning, computer vision and image processing. Proven track record of publications in top-tier conferences and journals. Excellent programming skills in Python/MATLAB and rich experiences with deep learning frameworks (e.g., PyTorch). English as official working language.  2. Doctoral Candidate (1 position) Focus Area: Mathematical modeling and machine learning for image analysis. Duration: Full-time for 3 years (with possibility for extending to 4 years). Starting from 1st August 2024.  Key Responsibilities: Develop mathematical models to assist/enhance AI-driven (e.g., deep learning based) image analysis. Work closely with embryologists and post-doctoral researchers to integrate these models into the overall framework. Data collection, preprocessing, and annotation. Contribute to writing research papers and project reports. Obtain a PhD diploma following the regulations of VUB. Requirements: Master's degree in (Applied) Mathematics, Computer Science, Electronic and Information Engineering, or related fields. Strong analytical and problem-solving skills, being able to conduct independent research and development with strong self-motivation. Experiences with mathematical modeling, machine learning and computer vision. Proficiency in programming languages such as Python or MATLAB. English as official working language. How to Apply If you are a highly motivated individual with a passion for advancing medical technology through AI and mathematical modeling, we encourage you to apply. Please send your CV and a cover letter detailing your research experience and interests to Prof. Ann Dooms ([email protected]) and Prof. Tan Lu ([email protected]).  All applications must be sent before 1st July 2024.

Posted by on 2024-05-20

Two Post Doctoral Researchers and One PhD Student in Advanced Medical Image Analysis

Project Description We are glad to announce the launch of a new research project based on the collaboration between the Mathematics and Data Science (MADS) research group at Vrije Universiteit Brussel (VUB) and the Centre for Reproductive Medicine at UZ Brussel (Brussels IVF). This project aims at helping the field of assisted reproductive technology (ART) by developing innovative AI-driven frameworks for the analysis of high-dimensional oocyte/embryo images. By integrating advanced deep learning and mathematical modeling, we seek to investigate, understand and potentially improve decision-making in ART procedures. The ultimate objective of this interdisciplinary research is to push the boundaries of current reproductive treatment, potentially offering new insights and tools for clinicians. Open Positions We are opening the following research positions in Digital Mathematics (DIMA), a research group chaired by Prof. Ann Dooms from MADS, VUB. 1. Post-doctoral Researchers (2 vacancies) Focus Area: Advanced deep learning and machine intelligence for medical image analysis. Duration: Full-time position for 2 years (with possibility for extending to 30 month). Starting from 1stSeptember 2024. Key Responsibilities: Conceptualize, develop and implement deep learning and mathematical modeling algorithms for analyzing high-dimensional medical images. Collaborate with embryologists and clinicians to integrate biological motivations into AI models. Publish research findings in high-impact journals and present at conferences. Requirements: PhD in Applied Mathematics, Computer Science, Electrical/Electronic/Information Engineering, or related fields. Strong background in deep learning, machine learning, computer vision and image processing. Proven track record of publications in top-tier conferences and journals. Excellent programming skills in Python/MATLAB and rich experiences with deep learning frameworks (e.g., PyTorch). English as official working language.  2. Doctoral Candidate (1 position) Focus Area: Mathematical modeling and machine learning for image analysis. Duration: Full-time for 3 years (with possibility for extending to 4 years). Starting from 1st August 2024.  Key Responsibilities: Develop mathematical models to assist/enhance AI-driven (e.g., deep learning based) image analysis. Work closely with embryologists and post-doctoral researchers to integrate these models into the overall framework. Data collection, preprocessing, and annotation. Contribute to writing research papers and project reports. Obtain a PhD diploma following the regulations of VUB. Requirements: Master's degree in (Applied) Mathematics, Computer Science, Electronic and Information Engineering, or related fields. Strong analytical and problem-solving skills, being able to conduct independent research and development with strong self-motivation. Experiences with mathematical modeling, machine learning and computer vision. Proficiency in programming languages such as Python or MATLAB. English as official working language. How to Apply If you are a highly motivated individual with a passion for advancing medical technology through AI and mathematical modeling, we encourage you to apply. Please send your CV and a cover letter detailing your research experience and interests to Prof. Ann Dooms ([email protected]) and Prof. Tan Lu ([email protected]).  All applications must be sent before 1st July 2024.

Posted by on 2024-05-20

Distinguished Lecture: Prof. Dr. Justin Dauwels (TU Delft)

Date: 15 June 2024 Chapter: UAE Joint w/ComSoc Chapter Chapter Chair: Diana Wasfi Dawoud Title: TBA

Posted by on 2024-05-15

Distinguished Lecture: Dr. Tran Quoc Long (VNU University of Engineering and Technology, Vietnam)

Date: 16 May 2024 Chapter: Vietnam Chapter Chapter Chair: Nguyen Linh-Trung Title: How healthcare systems in Vietnam work?

Posted by on 2024-05-15

How does VoLTE impact battery life on smartphones?

VoLTE can impact battery life on smartphones due to the increased data usage and processing power required for transmitting voice calls over LTE networks. However, advancements in battery optimization techniques and network efficiency have helped mitigate this impact. Additionally, smartphones with VoLTE support often come with enhanced power management features to optimize battery usage during VoLTE calls.

How does VoLTE impact battery life on smartphones?

Can VoLTE be used for video calls as well as voice calls?

VoLTE can be used for video calls as well as voice calls, providing users with the flexibility to switch between different communication modes seamlessly. Video calls over VoLTE benefit from the same high-quality audio and faster call setup times as voice calls, enhancing the overall communication experience for users. This integration of voice and video services over LTE networks offers a more comprehensive communication solution for users.

Cross-Talk Suppression

What security measures are in place to protect VoLTE calls from eavesdropping or hacking?

VoLTE calls are protected from eavesdropping or hacking through various security measures implemented by carriers and device manufacturers. These measures include encryption of voice data, secure authentication protocols, and network-level security mechanisms to prevent unauthorized access to VoLTE calls. Additionally, regular security updates and patches are provided to ensure the integrity and confidentiality of VoLTE communications.

What security measures are in place to protect VoLTE calls from eavesdropping or hacking?
How does VoLTE handle handovers between LTE and non-LTE networks during a call?

VoLTE handles handovers between LTE and non-LTE networks during a call by utilizing technologies such as Single Radio Voice Call Continuity (SRVCC) and Enhanced Single Radio Voice Call Continuity (eSRVCC). These technologies enable seamless handovers between LTE and legacy networks, ensuring uninterrupted voice calls even when transitioning between different network technologies. This ensures a consistent and reliable communication experience for users.

What are the potential benefits of using VoLTE for emergency calls compared to traditional voice calls?

Using VoLTE for emergency calls offers several potential benefits compared to traditional voice calls, including faster call setup times, improved audio quality, and enhanced location accuracy for emergency services. VoLTE enables emergency calls to be prioritized on the network, ensuring that critical communications are given precedence during emergencies. Additionally, the enhanced data capabilities of VoLTE allow for the transmission of additional information, such as medical data or location details, to emergency responders, facilitating quicker and more effective emergency responses.

What are the potential benefits of using VoLTE for emergency calls compared to traditional voice calls?

Digital audio signal processing plays a crucial role in facilitating multilingual communication systems by enabling the real-time translation of spoken language into different languages. Through the use of algorithms, such as speech recognition, language identification, and machine translation, digital audio signal processing can accurately convert spoken words into text and then translate that text into the desired language. This process involves various techniques, including noise reduction, voice activity detection, and speaker diarization, to enhance the quality and accuracy of the translation. By leveraging advanced technologies like neural networks, deep learning, and natural language processing, multilingual communication systems can effectively bridge language barriers and enable seamless communication between individuals speaking different languages.

In high-fidelity communication systems, audio clipping mitigation is achieved through various techniques such as dynamic range compression, peak limiting, and digital signal processing. These methods help prevent distortion and clipping by adjusting the audio levels in real-time to ensure that the signal remains within the acceptable range. Additionally, advanced algorithms like adaptive filtering and noise reduction can be employed to further enhance the audio quality and reduce the likelihood of clipping. By implementing a combination of these strategies, high-fidelity communication systems can deliver clear, undistorted audio signals that meet the highest standards of quality and fidelity.

Advancements in audio signal processing for 5G networks include the integration of machine learning algorithms, such as deep learning and neural networks, to enhance the quality of audio transmission and reception. These algorithms can help in noise reduction, echo cancellation, and beamforming, resulting in improved audio clarity and intelligibility. Additionally, the use of adaptive bitrate streaming and dynamic range compression techniques can optimize audio delivery over 5G networks, ensuring a seamless and uninterrupted listening experience for users. Furthermore, the implementation of real-time audio processing capabilities, such as audio recognition and synthesis, can enable innovative applications like voice-controlled devices and virtual assistants to operate efficiently on 5G networks. Overall, these advancements in audio signal processing are crucial for maximizing the potential of 5G technology in delivering high-quality audio services to users.

The most effective echo cancellation algorithms for mobile networks typically include adaptive filtering, nonlinear processing, and double-talk detection techniques. These algorithms are designed to minimize the echo caused by the acoustic coupling between the loudspeaker and microphone in mobile devices. Some popular echo cancellation algorithms used in mobile networks are the Least Mean Squares (LMS) algorithm, the Normalized Least Mean Squares (NLMS) algorithm, and the Recursive Least Squares (RLS) algorithm. These algorithms work by estimating the impulse response of the acoustic path and subtracting it from the received signal to eliminate the echo. Additionally, advanced algorithms such as the Frequency-Domain Adaptive Filter (FDAF) and the Subband Adaptive Filter (SAF) have been developed to further improve echo cancellation performance in mobile networks.

Efficient speech coders for mobile communication require careful consideration of various key factors to ensure optimal performance. Some of these factors include the choice of encoding algorithms, bit rate optimization, noise reduction techniques, bandwidth utilization, and error resilience mechanisms. Additionally, the design of speech coders must take into account the specific requirements of mobile devices such as low power consumption, processing capabilities, and network constraints. By incorporating advanced signal processing algorithms, adaptive coding techniques, and efficient compression methods, speech coders can effectively minimize data transmission overhead while maintaining high audio quality. Overall, a holistic approach to designing speech coders for mobile communication is essential to meet the demands of modern wireless networks and provide users with a seamless communication experience.