Department of Electronics and Telecommunications Engineering

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Nonlinear anisotropic diffusion methods for image denoising problems: Challenges and future research opportunities
(Elsevier, 2023-03-01) Maiseli, Baraka
Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona–Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.
Propagation Measurements for IQRF Network in an Urban Environment
(MDPI, 2022-09-16) Bouzidi, Mohammed; Mohamed, Marshed; Dalveren, Yaser; Moldsvor, Arild; Cheikh, Faouzi Alaya; Derawi, Mohammad
Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch–Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.
Free-space optical communication: From space to ground and ocean
(IEEE, 2021-11-11) Abrahamsen, Fredrik Ege; Ai, Yun; Wold, Knut; Mohamed, Marshed
In-Body Sensor Communication: Trends and Challenges
(IEEE, 2021-07-07) Mohamed, Marshed; Maiseli, Baraka; Ai, Yun; Mkocha, Khadija; Al-Saman, Ahmed
Wireless body area networks (WBANs) consist of interconnected devices that monitor the human body functions and the surrounding environment. Of these sensors, implants encounter multiple challenges due to their invasive nature. In addition, the transmission channel of the implants involves living tissues that pose practical challenges in channel modeling. Despite several promising applications of implants in the healthcare industry, there have been insufficient comprehensive reviews that extensively describe trends and challenges of this technology. This work reviews in-body WBANs and presents critical challenges that hinder advancement and application of the technology. We also discuss possible solutions that may be useful to realize in-body WBANs practically.
Wideband Channel Characterization for 6G Networks in Industrial Environments
(MDPI, 2021-03-12) Al-Saman, Ahmed; Mohamed, Marshed; Cheffena, Michael; Moldsvor, Arild
Wireless data traffic has increased significantly due to the rapid growth of smart terminals and evolving real-time technologies. With the dramatic growth of data traffic, the existing cellular networks including Fifth-Generation (5G) networks cannot fully meet the increasingly rising data rate requirements. The Sixth-Generation (6G) mobile network is expected to achieve the high data rate requirements of new transmission technologies and spectrum. This paper presents the radio channel measurements to study the channel characteristics of 6G networks in the 107–109 GHz band in three different industrial environments. The path loss, K-factor, and time dispersion parameters are investigated. Two popular path loss models for indoor environments, the close-in free space reference distance (CI) and floating intercept (FI), are used to examine the path loss. The mean excess delay (MED) and root mean squared delay spread (RMSDS) are used to investigate the time dispersion of the channel. The path loss results show that the CI and FI models fit the measured data well in all industrial settings with a path loss exponent (PLE) of 1.6–2. The results of the K-factor show that the high value in industrial environments at the sub-6 GHz band still holds well in our measured environments at a high frequency band above 100 GHz. For the time dispersion parameters, it is found that most of the received signal energy falls in the early delay bins. This work represents a first step to establish the feasibility of using 6G networks operating above 100 GHz for industrial applications.
Hybrid automatic repeat request‐based intelligent reflecting surface‐assisted communication system
(Institution of Engineering and Technology, 2021-01-20) Ai, Yun; Mohamed, Marshed; Kong, Long; Al‐Saman, Ahmed; Cheffena, Michael
The intelligent reflecting surface (IRS) is an emerging technique to extend wireless coverage. In this letter, the performance of the hybrid automatic repeat request (hybrid ARQ) for an IRS‐assisted system is analysed. More specifically, the outage performance of the IRS‐aided system using hybrid ARQ protocol with chase combining is studied. The asymptotic analysis also shows that the outage performance is better and improves linearly by increasing the number of reflectors of the IRS‐aided system. The results also verify the potential of combining the ARQ scheme in the link layer of the IRS‐aided system and demonstrate that a very small change of path loss condition can impact the performance largely.
Radio Propagation Measurements in the Indoor Stairwell Environment at 3.5 and 28 GHz for 5G Wireless Networks
(Hindawi, 2020-12-28) Al-Saman, Ahmed; Mohamed, Marshed; Cheffena, Michael
To cover the high demand for wireless data services for different applications in the wireless networks, different frequency bands below 6 GHz and in millimeter-wave (mm-Wave) above 24 GHz are proposed for the fifth generation (5G) of communication. The communication network is supposed to handle, among others, indoor traffic in normal situations as well as during emergencies. The stairway is one of those areas which has less network traffic during normal conditions but increases significantly during emergencies. This paper presents the radio propagation in an indoor stairway environment based on wideband measurements in the line of sight (LOS) at two candidate frequencies for 5G wireless networks, namely, 3.5 GHz and 28 GHz. The path loss, root mean square (RMS) delay spread, K-factor results, and analysis are provided. The close-in free-space reference distance (CI), floating intercept (FI), and the close-in free-space reference distance with frequency weighting (CIF) path loss models are provided. The channel parameters such as the number of clusters, the ray and cluster arrival rates, and the ray and cluster decay factors are also obtained for both frequencies. The findings of the path loss show that the CI, FI, and CIF models fit the measured data well in both frequencies with the path loss exponent identical to the free-space path loss. Based on clustering results, it is found that the cluster decay rates are identical at both bands. The results from this and previous measurements indicate that at least one access point is required for every two sections of the stairway to support good coverage along the stairwell area in 5G wireless networks. Moreover, for 5G systems utilizing mm-Wave frequency bands, one access point for each stair section might be necessary for increased reliability of the 5G network in stairwell environments.
Buffer Delay Improvement in Gait-Cycle-Driven Transmission Power Control Scheme for WBAN
(IEEE, 2020-10-08) Mohamed, Marshed; Cheffena, Michael; Ai, Yun; Al-Saman, Ahmed
Due to the dynamic nature of the wireless body area network (WBAN) channels, there is a need for dynamic transmission power control (TPC) to increase their energy efficiency. The existing gait-cycle-driven TPC (G-TPC) successfully achieves this objective, however, it introduces maximum buffer delay equal to the period of the gait cycle. In this study, we investigate the relationship between the potential power saved and the maximum buffer delay in the G-TPC approach. A new approach is proposed based on the transmission window (instead of currently used transmission point) to reduce the maximum buffer delay by studying the received signal strength indicator (RSSI) gait patterns collected from 20 subjects. The results indicated that with a slight modification of the protocol, the same power saving can be achieved for 1.2% of the time with less than half of the maximum buffer delay. The study also indicated that, with tolerant power saving requirements, at least half of the gait channels can reduce the maximum buffer delay by more than 38%.
Performance of Full-Duplex Wireless Back-Haul Link under Rain Effects Using E-Band 73 GHz and 83 GHz in Tropical Area
(MDPI, 2020-09-03) Al-Saman, Ahmed; Mohamed, Marshed; Cheffena, Michael; Azmi, Marwan Hadri; Rahman, Tharek
This paper presents rain attenuation effects on the performance of the full-duplex link in a tropical region based on one-year measurement data at 73.5- and 83.5-GHz E-band for distances of 1.8 km (longer links) and 300 m (shorter links). The measured rain attenuations were analyzed for four links, and the throughput degradation due to rain was investigated. The findings from this work showed that the rain attenuation for both frequencies (73.5 and 83.5 GHz) of E-band links are the same. The rain rates above 108 and 193 mm/h caused an outage for the longer and shorter links, respectively. The 73.5 and 83.5 GHz bands can support the full-duplex wireless back-haul link under rainy conditions with outage probability of 2.9×10−4 and 6×10−5 for the longer and shorter links, respectively. This work also finds that the heavy rain with rain rates above 80 mm/h for long link and 110 mm/h for short link causes about 94% and 0.90% degradation of maximum throughput. The application of these findings would help improve the architecture and service of full-duplex wireless E-band links that are established at other sites and in other tropical areas.
Indoor Channel Estimation Using Single-Snapshot Wideband Measurement
(IEEE, 2020-03-15) Ai, Yun; Cheffena, Michael; Mohamed, Marshed; Al-Saman, Ahmed
The successful design of communication systems generally requires knowledge of various channel characteristic parameters. This paper utilizes the reverberation time extracted from single-snapshot wideband measurement to estimate different indoor propagation parameters based on the room electromagnetics theory. The indoor room environment is conceived as a lossy cavity that is characterized by the diffuse scattering components resulting from the surrounding walls and objects and possibly a line-of-sight (LoS) component. The main advantages of the room electromagnetics based approach are simplicity and good accuracy. The approach needs only one wideband measurement in order to extract the reverberation time in addition to some dimensional information on the investigated room to predict various important channel parameter of great importance. The measurements show good agreement with the theoretical predicted results.
Learning a deep predictive coding network for a semi-supervised 3D-hand pose estimation
(IEEE, 2020-03-27) Bulugu, Isack; Banzi, Jamal; Huang, Shiliang; Ye, Zhongfu
In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model ( LDTM ) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.
Learning Hand Latent Features For Unsupervised 3D Hand Pose Estimation
(2019-05-06) Banzi, Jamal; Bulugu, Isack
Recent hand pose estimation methods require large numbers of annotated training data to extract the dynamic information from a hand representation. Nevertheless, precise and dense annotation on the real data is difficult to come by and the amount of information passed to the training algorithm is significantly higher. This paper presents an approach to developing a hand pose estimation system which can accurately regress a 3D pose in an unsupervised manner. The whole process is performed in three stages. Firstly, the hand is modelled by a novel latent tree dependency model (LTDM) which transforms internal joints location to an explicit representation. Secondly, we perform predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. A mapping is then performed between an image depth and a generated representation. Thirdly, the hand joints are regressed using convolutional neural networks to finally estimate the latent pose given some depth map. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. To demonstrate the performance of the proposed system, a complete experiment is conducted on three challenging public datasets, ICVL, MSRA, and NYU. The empirical results show the significant performance of our method which is comparable or better than state-of-the-art approaches.
Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation
(2019-08-15) Banzi, Jamal; Bulugu, Isack; Ye, Zhongfu
The discriminative approaches for hand pose estimation from depth images usually require dense annotated data to train a supervised network. Additionally, generative methods depend on temporal information in generating candidate poses which can be trapped due to local minima during the optimization process. Different from these methods, we propose a hybrid two-stage deep predictive neural network approach that performs predictive coding of image sequences of hand poses in order to capture latent features underlying a given image. Firstly, we train a deep convolutional neural network (CNN) for direct regression of hand joints position. Secondly, we add an unsupervised error term as a part of the recurrent architecture connected with predictive coding portion. An error regression term (ERT) ensures minimal residual errors of the estimated values while the predictive coding portion allows training of the network without the supervision of image sequences, so no dense annotation of data is required. We conduct a complete experiment using two challenging public datasets, ICVL and NYU. Using the ICVL datasets, our approach improved accuracy over the current state of the art methods with an average error joint of 7.5mm. We also achieve 12.2mm average error joint on NYU dataset which is the smallest error to be achieved on all state-of-art approaches.
A novel Hand Pose Estimation using Dicriminative Deep Model and Transductive Learning Approach for Occlusion Handling and Reduced Descrepancy
(IEEE, 2017-05-11) Banzi, Jamal; Bulugu, Isack; Ye, Zhongfu
Discriminative based model have demonstrated an epic distinction in hand pose estimation. However there are key challenges to be solved on how to intergrate the self-similar parts of fingers which often occlude each other and how to reduce descrepancy among synthetic and realistic data for an accurate estimation. To handle occlusion which lead to inaccurate estimation, this paper presents a probabilistic model for finger position detection framework. In this framework the visibility correlation among fingers aid in predicting the occluded part between fingers thereby estimating hand pose accurately. Unlike convectional occlusion handling approach which assumes occluded parts of fingers as independent detection target, this paper presents a discriminative deep model which learns the visibility relationship among the occluded parts of fingers at multiple layers. In addition, we propose the semi-supervised Transductive Regression(STR) forest for classification and regression to minimise discrepancy among realistic and synthetic pose data. Experimental results demonstrate promising performance with respect to occlusion handling, and discrepancy reduction with higher degree of accuracy over state-of-the-art approaches.
Higher-Order Local Autocorrelation Feature Extraction Methodology for Hand Gestures Recognition
(IEEE, 2017-12-25) Bulugu, Isack; Ye, Zhongfu; Banzi, Jamal; Bulugu, Isack
A novel feature extraction method for hand gesture recognition from sequences of image frames is described and tested. The proposed method employs higher order local autocorrelation (HLAC) features for feature extraction. The features are extracted using different masks from Grey-scale images for characterising hands image texture with respect to the possible position, and the product of the pixels marked in white. Then features with the most useful information are selected based on mutual information quotient (MIQ). Multiple linear discriminant analysis (LDA) classifier is adopted to classify different hand gestures. Experiments on the NUS dataset illustrate that the HLAC is efficient for hand gesture recognition compared with other feature extraction methods.
Scale Invariant Static Hand-Postures Detection using Extended Higher-order Local Autocorrelation Features
(Foundation of Computer Science (FCS), NY, USA, 2016-02-17) Bulugu, Isack; Ye, Zhongfu
This paper presents scale invariant static hand postures detection methods using extended HLAC features extractedfrom Log-Polar images. Scale changes of a handposture in an image are represented as shift in Log-Polar image. Robustness of the method is achieved through extracting spectral features from theeach row of the Log-Polar image. Linear Discriminant Analysis was used to combine features with simple classification methods in order to realize scale invariant hand postures detection and classification.The method was successful tested by performing experiment using NSU hand posture dataset images which consists 10 classes of postures, 24 samples of images per class, which are captured by the position and size of the hand within the image frame. The results showed that the detection rate using Extended-HLAC can averaged reach 94.63% higher than using HLAC features on a Intel Core i5-4590 CPU running at 3.3 GHz.
Rain Attenuation Measurements and Analysis at 73 GHz E-Band Link in Tropical Region
(IEEE, 2020-03-25) Al-saman, Ahmed; Mohamed, Marshed; Ai, Yun; Cheffena, Michael; Azmi, Marwan Hadri; Rahman, Tharek
This letter presents the rainfall intensity and rain attenuation analysis in tropical region based on a one-year measurement using the 73.5 GHz E-band link of 1.8 km with three rain gauges installed along the path. The measured rain rate and rain attenuation were analysed and bench-marked with previous measurements and prediction models. The findings from this work showed that Malaysia agrees with the ITU-R rain prediction model of Zone P by 99.99% of time. The maximum measured rain attenuation exceeding 0.03% of the year is around 40.1 dB at the maximum rain rate of 108 mm/h.
Statistical Analysis of Rain at Millimeter Waves in Tropical Area
(IEEE, 202-03-09) Al-saman, Ahmed; Cheffena, Michael; Mohamed, Marshed; Azmi, Marwan Hadri; Ai, Yun
The high frequencies of millimeter wave (mm-wave) bands have been recognized for the fifth generation (5G) and beyond wireless communication networks. However, the radio propagation channel at high frequencies can be largely influenced by rain attenuation, especially in tropical regions with high rainfall intensity. In this paper, we present the results of rainfall intensity and rain attenuation in tropical regions based on one-year measurement campaign. The measurements were conducted from September 2018 until September 2019 at 21.8 GHz (K-band) and 73.5 GHz (E-band) in Malaysia. The rainfall intensity was collected using three rain gauges installed along a 1.8 km link. The rain attenuation is computed from the difference between the measured minimum received signal level (RSL) during clear sky and rain conditions. The measured rain rate and rain attenuation distributions are then analysed and benchmarked with several previous measurements and well-known prediction models such as the ITU-R P. 530-17. The rainfall rate results showed that the best agreement between the measured rainfall rate in Malaysia and the ITU-R PN.837-1 prediction value for Zone P is up to 0.01% of time (99.99% of time agrees well and only disagrees for 0.01% of time). For the E-band, the maximum measured rain attenuation exceeding 0.03% of the year is around 40.1 and 20 dB for 1.8 and 0.3 km links, respectively, at the maximum rain rate of 108 mm/h. For the K-band, the maximum rain attenuation exceeding 0.01% of the year is around 31 dB for the 1.8 km link. Finally, the rain rates exceeding 108 and 180 mm/h at 73.5 and 21.8 GHz, respectively, along the 1.8 km path caused an outage on our measurement setup. The rain rate of 193 mm/h and above caused an outage for the 0.3 km E-band link. The experimental data as well as the presented data analysis can be utilized for efficient planning and deployments of mm-wave wireless communication systems in tropical regions.
Path-loss Compensation in Through-the-wall Radar Imaging
(IEEE, 2016) Alahmed, Ahmed; Alafif, Omar; Abdalla, Abdi T; Muqaibel, Ali
In through-the-wall radar imaging (TWRI), pathloss can result in misleading targets. The fact that distant targets experience more path-Ioss than near targets is an essential feature of propagating signals. This paper proposes a path-Ioss compensator, which reverts the unavoidable loss in power by using different path-Ioss models. This will be effectively done by developing and incorporating the path-Ioss compensator matrix. The signal model is generalized so that it includes the front wall reflections. Many research papers in the field do not account for the differences in the propagation losses of the paths due to its complexity and mutable behavior, wh ich can bring great difficulties to establish distance path-Ioss model. Simulation and experimental results are presented to show how the proposed approach can efficiently compensate for far targets and thus enhancing the signal-to-c1utter ratio and reducing the normalized mean square error
Through-the-Wall Radar Imaging Exploiting Pythagorean Coprime-Based Synthetic Apertures with Sparse Reconstruction
(2017) Muqaibel, Ali; Abdalla, Abdi T; Alkhodary, Mohammad; Alawsh, Saleh
Through-the-wall radar imaging (TWRI) is receiving a considerable attention recently due to its diverse applications. One of the impinging challenges is the multipath propagation from the surrounding environment and even targets themselves. Multipath propagation produces ghost targets which populate the scene and not only create confusion with genuine targets but deteriorate the performance of compressive sensing (CS) algorithms. Unlike genuine targets, ghost locations are aspect dependent. Successful exploitation of this feature is dictated by the subarray selection modality. Up to this far, random multiple subarrays selection is the practice in exploiting aspect dependence. This paper suggests new subarray configurations based on Pythagorean triple which is made of pairwise coprime numbers that can enhance ghost suppression process and improve image resolution. The sensing matrices of the proposed subarrays are developed and analyzed. The paper investigates the effectiveness of generating the images from all the elements in the array as opposed to generating the images by processing designed subarrays individually and then combining the results. This comparison is done in view of multipath ghost suppression exploiting aspect dependence feature. Results based on synthesized data and electromagnetic propagation simulator show the effectiveness of the proposed arrays.

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