Intelligent Signal Processing Techniques Research Articles

Air–ground integrated artificial intelligence of things with cognition-enhanced interference management

Integrated air–ground network enhances AIoT performance by improving spectral efficiency, achieving high-speed, stable network connectivity, and enabling sensing, learning, and decision-making. However, unmanned aerial vehicles (UAVs) can lead to local spectrum congestion and competition. To address this issue, intelligent signal processing techniques are employed to enhance AIoT system performance and stability through intelligent multi-channel sensing and communication. A novel communication framework inspired by brain cognition for UAV communication in heterogeneous environments is introduced. This framework iteratively determines the importance of signals, effectively eliminating unimportant signals with interference characteristics, and reducing their transmission power. Simulation results demonstrate the superiority of this method in terms of communication performance.

Guest Editorial Special Issue on 5G and Beyond—Mobile Technologies and Applications for IoT

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances, including Internet of Things (IoT). As the IoT integrates many heterogeneous networks, such as wireless sensor networks, wireless local area networks, mobile communication networks (3G/4G/LTE/5G), wireless mesh networks, and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches, and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This Special Issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IoT.

Guest Editorial 5G and Beyond Mobile Technologies and Applications for Industrial IoT (IIoT)

Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, a benefit offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances including Industrial Internet of Things (IIoT). As the IIoT integrates many heterogeneous networks, such as Wireless Sensor Networks (WSNs), Wireless Local Area Networks (WLANs), Mobile Communication Networks (3G/4G/LTE/5G), Wireless Mesh Networks (WMNs) and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million healthcare workers by 2035. Hence, it is important to develop wearable healthcare systems to perform self-health monitoring. In general, wearable healthcare systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches and intelligent signal processing techniques play important roles in the developments of the wearable healthcare systems. Therefore, this special issue provides a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IIoT.

Designing a Compact Wireless Network based Device-free Passive Localisation System for Indoor Environments

Determining the location of individuals within indoor locations can be useful in various scenarios including security, gaming and ambient assisted living for the elderly. Healthcare services globally are seeking to allow people to stay in their familiar home environments longer due to the multitude of benefits associated with living in non-clinical environments and technologies to determine an individual's movements are key to ensuring that home emergencies are detected through lack of movement can be responded to promptly. This paper proposes a device-free localisation (DFL) system which would enable the individual to proceed with normal daily activities without the concern of having to wear a traceable device. The principle behind this is that the human body absorbs/reflects the radio signal being transmitted from a transmitter to one or more receiving stations. The proposed system design procedure facilitates the use of a minimum number of wireless nodes with the help of a principle component analysis (PCA) based intelligent signal processing technique. Results demonstrate that human detection and tracking are possible to within 1m resolution with a minimal hardware infrastructure.

Using acoustic sensors to discriminate between nasal and mouth breathing

The recommendation to change breathing patterns from the mouth to the nose can have a significantly positive impact upon the general well being of the individual. We classify nasal and mouth breathing by using an acoustic sensor and intelligent signal processing techniques. The overall purpose is to investigate the possibility of identifying the differences in patterns between nasal and mouth breathing in order to integrate this information into a decision support system which will form the basis of a patient monitoring and motivational feedback system to recommend the change from mouth to nasal breathing.

Detection and classification of human movement (DC-MOVE)

Characteristic movements of human body parts ranging from eye twitches to limbs jerky movements have been used for decades by physicians as clinical indicators of certain neurological disorders. Through a multidisciplinary research approach, our team, composed of medical experts, signal-processing specialists, wireless sensing experts, and computer scientists, aims at developing a sophisticated framework for automatic characterization of certain clinical conditions via identification of a proposed unique sequence (a signature pattern) of limb movements in relation to other body parts. We argue that a set of movement data collected from human subjects via strategically located movement sensors fused with other supporting data, such as gyroscopic movements and relative locations of sensors, can be processed by advanced intelligent signal processing techniques. Using medical expert systems fed with knowledge provided by the contributing medical experts this can be used to characterize and classify typical and atypical human movements. The collected data is then processed using machine learning algorithms which is trained to automatically detect and characterize a set of movement disorders and classify them into specific clinical diagnosis such as specific types of seizures. In particular, our work ambitiously aims at developing a prototype proof-of-concept seizure remote monitoring and detection system. This would demonstrate the applicability of our developed methodology in real-life scenarios, using commercial of-the-shelf wireless sensing platforms coupled to our intelligent expert-based signal-processing platform. We believe that the outcomes of this applied research will pave the roads for new methods in clinical diagnosis of various neurological diseases and monitoring progress and outcome of treatment that will, in turn, reduce human suffering and medical costs. Further, when coupled with our wireless technology and positioning methods, DC-MOVE can initiate or trigger an alerting response that could be life-saving.

Cooperative communication with partial channel-state information in multiuser MIMO systems

Cooperative communication applying the multi-cell cooperative signal processing for the mobile stations (MSs) at the coordinated base stations (BSs) is a very promising candidate for intercell interference management in multiuser multiple-input multiple-output (MIMO) cellular systems. Paying attention to the practical constraints in reality, the present paper reconsiders cooperative communication from a view of partial channel-state information (CSI), i.e., significant CSI and imperfect CSI. Significant CSI is the CSI of the channels which play a significant role in the system performance. We propose a novel MS-oriented dynamic significant channel selection distinguishing the significant useful channels from the significant interference channels. With the significant CSI corresponding to the selected significant channels, an efficient decentralized cooperative signal processing scheme is proposed to make a good compromise between system performance and implementation complexity. Meanwhile, the impact of imperfectness of the CSI on the system performance is assessed. The question about how much CSI should be considered as a function of the extent of imperfectness of the CSI in order to achieve optimum system performance is answered based on numerical results. Additionally, numerical results show that the proposed intelligent signal processing technique can significantly benefit from the proposed smart BS-antenna layout.

Intelligent sensors using computationally efficient Chebyshev neural networks

Intelligent signal processing techniques are required for auto-calibration of sensors, and to take care of nonlinearity compensation and mitigation of the undesirable effects of environmental parameters on sensor output. This is required for accurate and reliable readout of the measurand, especially when the sensor is operating in harsh operating conditions. A novel computationally efficient Chebyshev neural network (CNN) model that effectively compensates for such non-idealities, linearises and calibrates automatically is proposed. By taking an example of a capacitive pressure sensor, through extensive simulation studies it is shown that performance of the CNN-based sensor model is similar to that of a multilayer perceptron-based model, but the former has much lower computational requirement. The CNN model is capable of producing pressure readout with a full-scale error of only ±1.0% over a wide operating range of −50 to 200°C.