• D-shaped photonic crystal fiber based surface plasmon resonance sensor D-Şeklinde fotonik kristal fiber tabanli yüzey plazmon rezonans sensörü

      Yaşli, Ahmet; Ademgil, Huseyin; Haxha, Shyqyri; Lefke Avrupa Üniversitesi; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2018-07-09)
      In this study, we have proposed photonic crystal fiber based surface plasmon resonance sensor, where hexagonally arranged circular air holes surrounded the core of photonic crystal fiber. Gold has been placed on top surface as a metallic layer, while all the structure covered with analyte channel. Full Vectorial Finite Elimination Method has been used for analysing effective refractive index and confinement losses for proposed sensor. When refractive index of analyte channel changes, spectral interrogation and amplitude methods have been used for calculating the sensitivities and resolutions of offered sensor.
    • The dark web: cyber-security intelligence gathering opportunities, risks and rewards

      Epiphaniou, Gregory; French, Tim; Maple, Carsten; University of Bedfordshire (University of Zagreb, 2014-12-31)
      We offer a partial articulation of the threats and opportunities posed by the so-called Dark Web (DW). We go on to propose a novel DW attack detection and prediction model. Signalling aspects are considered wherein the DW is seen to comprise a low cost signaling environment. This holds inherent dangers as well as rewards for investigators as well as those with criminal intent. Suspected DW perpetrators typically act entirely in their own self-interest (e.g. illicit financial gain, terrorism, propagation of extremist views, extreme forms of racism, pornography, and politics; so-called 'radicalisation'). DWinvestigators therefore need to be suitably risk aware such that the construction of a credible legally admissible, robust evidence trail does not expose investigators to undue operational or legal risk.
    • Data mining techniques in health informatics: a case study from breast cancer research

      Lu, Jing; Hales, Alan; Rew, David; Keech, Malcolm; Fröhlingsdorf, Christian; Mills-Mullett, Alex; Wette, Christian; Southampton Solent University; University Hospital Southampton; University of Bedfordshire (Springer Verlag, 2015-08-11)
      This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.
    • Data mining, management and visualization in large scientific corpuses

      Wei, Hui; Wu, Shaopeng; Zhao, Youbing; Deng, Zhikun; Ersotelos, Nikolaos; Parvinzamir, Farzad; Liu, Baoquan; Liu, Enjie; Dong, Feng; University of Bedfordshire (Springer Verlag, 2016-12-31)
      Organizing scientific papers helps efficiently derive meaningful insights of the published scientific resources, enables researchers grasp rapid technological change and hence assists new scientific discovery. In this paper, we experiment text mining and data management of scientific publications for collecting and presenting useful information to support research. For efficient data management and fast information retrieval, four data storages are employed: a semantic repository, an index and search repository, a document repository and a graph repository, taking full advantage of their features and strength. The results show that the combination of these four repositories can effectively store and index the publication data with reliability and efficiency and hence supply meaningful information to support scientific research.
    • Deep learning for biometric face recognition: experimental study on benchmark data sets

      Selitskaya, Natalya; Sielicki, S.; Jakaite, Livija; Schetinin, Vitaly; Evans, F.; Conrad, Marc; Sant, Paul (Springer International Publishing, 2020-06-09)
      There are still problems in applications of Machine Learning for face recognition. Such factors as lighting conditions, head rotations, emotions, and view angles affect the recognition accuracy. A large number of recognition subjects requires complex class boundaries. Deep Neural Networks have provided efficient solutions, although their implementations require massive computations for evaluation and minimisation of error functions. Gradient algorithms provide iterative minimisation of the error function. A maximal performance is achieved if parameters of gradient algorithms and neural network structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented in the form of a tutorial.
    • Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

      Jakaite, Livija; Schetinin, Vitaly; Hladůvka, Jiří; Minaev, Sergey; Ambia, Aziz; Krzanowski, Wojtek; ; University of Bedfordshire; TU Wien; Stavropol State Medical University; et al. (Nature, 2021-01-27)
      Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.
    • Deep neural-network prediction for study of informational efficiency

      Sulaiman, Rejwan Bin; Schetinin, Vitaly; University of Bedfordshire (Springer, 2021-08-03)
      In this paper, we attempt to verify a hypothesis of informational efficiency of financial markets, known as “random walk” introduced by Fama. Such hypotheses could be considered in relation to financial crises. In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive modelling based on Machine Learning (ML). We compare conventional ML techniques and the proposed “deep” neural-network structures grown by Group Method of Data Handling (GMDH). In our experiments a GMDH-type neural-network model has outperformed the conventional ML techniques, which is important for achieving the reliable results of predictive modelling and testing the hypothesis. GMDH-type modelling does not require the knowledge of network structure, as a desired network of near-optimal connectivity is learnt from the data. The experimental results compared in terms of prediction error show that the GMDH-type prediction model has a significantly smaller error than the conventional autoregressive and neural-network models.
    • Design an asymmetrical three-beam laser interference lithography for fabricating micro- and nano-structures

      Dong, Litong; Zhang, Ziang; Wang, Zuobin; Li, Dayou; Liu, Mengnan; Changchun University of Science and Technology; University of Bedfordshire; Changchun Observatory (Japan Laser Processing Society, 2020-09-01)
      Multi-beam laser interference lithography (LIL) has become one of the most important techniques and shown significant advantages in the fabrication of micro- and nano-structures. Controlling inten-sity ratio of optical distributions is a key issue in LIL for fabricating micro- and nano-structures. This paper presents an asymmetrical three-beam LIL system which effectively improves the intensity ratio of optical distributions. Comparing with the symmetrical three-beam interference, the asymmetrical three-beam LIL achieved the high intensity ratio of optical distribution when producing the similar interference pattern. In addition, this system also avoids modulation patterns in multi-beam LIL sys-tems and reduces the difficulty of actual LIL processing. A fast Fourier Transform (FFT) analysis used to study the pattern distributions of the asymmetrical three-beam interference from frequency spectra which shows that the pattern with a high-intensity array can be obtained by adjusting the parameter settings of incident laser beams. The asymmetrical three-beam LIL system was verified through fab-ricating patterns. The experimental results are in good agreement with the theoretical analyses.
    • Design and study of a small implantable antenna design for blood glucose monitoring

      Ahmed, Ayesha; Kalsoom, Tahera; Ur-Rehman, Masood; Ramzan, Naeem; Karim, Sajjad; Abbasi, Qammer Hussain; University of Bedfordshire; University of West of Scotland; Foundation University Islamabad; University of Glasgow (Applied Computational Electromagnetics Society Inc, 2018-10-12)
      In this paper, a miniaturized implantable antenna with the dimensions of 8×8×1 mm3 has been studied for continuous monitoring of Blood Glucose Levels (BGL). The antenna performance is analyzed numerically for both the free space and implanted operation. The results show that the works excellently in both the scenarios. The antenna has the lowest resonant frequency of 3.58 GHz in free space with a gain 1.18 GHz while it operates at 2.58 GHz with a gain of 4.18 dBi. Good performance, small size and resilience to the human body effects make the antenna to have a good potential use in future implantable glucose monitoring devices.
    • Design of a compact multiband circularly polarized antenna for global navigation satellite systems and 5G/B5G applications

      Falade, Oluyemi Peter; Ur-Rehman, Masood; Yang, Xiaodong; Safdar, Ghazanfar Ali; Parini, Clive G.; Chen, Xiaodong (Wiley, 2020-02-17)
      Design of a multiband circularly polarized antenna is proposed in this article. The antenna has a simple and compact form factor by employing single‐feed stacked patch structure. It exhibits good performance at the global navigation satellite system (GNSS) frequency bands of L1, L2, and L5 and cellular communications frequency band of 2.3 GHz. The antenna has a 3‐dB axial ratio bandwidth of 1.1%, 1.0%, 4.1%, and 1.5% at the four operating bands of L1 (1.575 GHz), L2 (1.227 GHz), L5 (1.176 GHz), and 2.3 GHz. The antenna also achieves a gain of more than 2.2 dBiC and efficiency of more than 70% at the four frequencies. A detailed parametric study is carried out to investigate the importance of different structural elements on the antenna performance. Results are verified through close agreement of simulations and experimental measurements of the fabricated prototype. Good impedance matching, axial ratio bandwidth, and radiation characteristics at the four operating bands along with small profile and mechanically stable structure make this antenna a good candidate for current and future GNSS devices, mobile terminals, and small satellites for 5G/Beyond 5G (5G/B5G) applications.
    • Design of a finger ring antenna for wireless sensor networks

      Farooq, Waqas; Ur-Rehman, Masood; Abbasi, Qammer Hussain; Yang, Xiaodong; Qaraqe, Khalid (Institute of Electrical and Electronics Engineers Inc., 2016-06-02)
      Body-centric communications have become very active area of research due to ever-growing demand of portability. Advanced applications such as; health monitoring, tele-medicine, identification systems, performance monitoring of athletes, defence systems and personal entertainment are adding to its popularity. In this paper, a novel wearable antenna radiating at 5 GHz for the body-centric wireless sensor networks has been presented. The antenna consists of a conventional microstrip patch mounted on a gold base and could be worn in a finger like a ring. CST Microwave Studio is used for modelling, simulation and optimisation of the antenna. The simulated results show that the proposed antenna has a -10 dB bandwidth of 90.3 MHz with peak gain of 6.9 dBi. Good performance in terms of bandwidth, directivity, gain, return loss and radiation characteristics, along with a miniaturised form factor makes it a very well suited candidate for the body-worn wireless sensor applications.
    • Design of a slotted-patch microstrip antenna for mobile terminals

      Ur-Rehman, Masood; Haxha, Shyqyri; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-10-26)
      This paper presents a study carried out to design a multi-band microstrip patch antenna. The antenna make use of straight and circular slots to support the operation at 4G/LTE/CDMA (2.1 GHz), and WLAN (3.6/4.9/5 GHz) [1] frequency bands while a novel 'Ohm-shaped' slot for better impedance matching. It offers compact size, good impedance matching with 2:1 VSWR and radiation pattern performance. Simple design and good gain and efficiency characteristics make this antenna a promising candidate for mobile terminal applications.
    • Design of a smart system for rapid bacterial test

      Patil, Rajshree; Levin, Saurabh; Rajkumar, Samuel; Ajmal, Tahmina; Institute of Chemical Technology (ICT), Mumbai; Foundation for Environmental Monitoring, Bangalore; University of Bedfordshire (MDPI, 2019-12-19)
      In this article, we present our initial findings to support the design of an advanced field test to detect bacterial contamination in water samples. The system combines the use of image processing and neural networks to detect an early presence of bacterial activity. We present here a proof of concept with some tests results. Our initial findings are very promising and indicate detection of viable bacterial cells within a period of 2 h. To the authors' knowledge this is the first attempt to quantify viable bacterial cells in a water sample using cell splitting. We also present a detailed design of the complete system that uses the time lapse images from a microscope to complete the design of a neural network based smart system.
    • Design of an LCP-based antenna array for 5G/B5G wearable applications

      Saeed, Muhammed Asfar; Ur-Rehman, Masood; University of Bedfordshire; University of Glasgow (Institute of Electrical and Electronics Engineers Inc., 2019-10-24)
      Interest in wearable applications providing early medical diagnostics and reliable communication to the remote observation station is ever increasing. A flexible microstrip patch antenna array designed on a liquid crystal polymer (LCP) substrate is presented in this paper. The designed antenna array consists of five radiating patches fed by a combination of series transmission lines. The simulated results show that the antenna resonates at 52 GHz with-10 dB impedance bandwidth of 1.34 GHz. It offers a gain of 3.5 dBi and 3-dB angular width of 72.3°at the operating frequency along with low weight, low cost and ease of fabrication. Good impedance and radiation characteristics with small size and low profile make this antenna ideally suited for low latency wearable applications in 5G/Beyond 5G networks.
    • Design of band-notched ultra wideband antenna for indoor and wearable wireless communications

      Ur-Rehman, Masood; Abbasi, Qammer Hussain; Akram, Muhammad; Parini, Clive G.; University of Bedfordshire; Texas A & M University at Qatar; University of Engineering and Technology, Pakistan; Queen Mary University of London (Institution of Engineering and Technology, 2014-10-16)
      Design of a tapered-slot ultra wideband (UWB) band-notched wearable antenna is presented in this study. The antenna operation covers the whole UWB frequency spectrum of 7.5 GHz ranging from 3.1 to 10.6 GHz, while rejecting the wireless local area network operation at 5.25 GHz band. The performance of the antenna is analysed through simulations and validated through measurements. The antenna makes use of ultra-thin liquid crystal polymer (LCP) substrate. The presented return loss and radiation pattern results show that the antenna offers excellent performance in the UWB frequency band in free space. Use of the LCP substrate makes the antenna to efficiently mitigate the bending effects. Moreover, the antenna performs well in on-body configurations and its working is little affected in adversely hot and humid weather conditions. Furthermore, it offers good on-body communication link and pulse fidelity. These features make the proposed antenna design a well-suited choice for hand-held and wearable UWB applications.
    • Design optimization of resource allocation in OFDMA-based cognitive radio-enabled Internet of Vehicles (IoVs)

      Eze, Joy C.; Zhang, Sijing; Liu, Enjie; Eze, Elias Chinedum; ; University of Bedfordshire (MDPI, 2020-11-09)
      Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity.
    • Design optimization of resource allocation in OFDMA-based cognitive radio-enabled internet of vehicles (IoVs)∗

      Eze, Joy C.; Eze, Elias Chinedum; Zhang, Sijing; Liu, Enjie; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2019-11-11)
      The problem of joint optimal subcarrier and transmit power allocation with quality of service (QoS) guarantee for enhanced packet transmission over a cognitive radio-enabled IoVs network system is considered in this paper. A novel Symmetric Nash bargaining solution (SNBS) based wireless radio resource scheduling scheme in orthogonal frequency division multiple access (OFDMA) cognitive radio (CR)-enabled IoVs network systems is proposed. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced and by applying time-sharing variable transformation, closed-form analytical solutions were obtained. To avoid the iterative processes associated with searching the optimal solution numerically through iterative programming methods, this study developed a low-complexity algorithm. Theoretical analysis and simulation results demonstrate that under similar conditions, the proposed solutions outperform reference scheduler schemes.
    • Detecting advance fee fraud using NLP bag of word model

      Hamisu, Muhammad; Mansour, Ali; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2021-05-25)
      Advance Fee Fraud (AFF) is a form of Internet fraud prevalent within the Cybercrimes domain in literature. Evidence shows that huge financial assets are stolen from the global economy as a result of AFF. Consequently, this paper presents a fraudulent email classifier (FEC) that detects and classifies an email as fraudulent or non-fraudulent using Natural Language Process (NLP) model referred to as Bag-of-Words (BoW). The classifier is designed and trained to detect and classify AFF that originate from known sources using Nigeria as a Case study. Dataset is obtained and used for the training while testing the classifier logs. Experimentally, the classifier was trained using various machine learning algorithms with BoW generated as predictors. By selecting the best algorithms, the classifier was tested and found to perform satisfactorily.
    • Detection of essential tremor at the S-band.

      Yang, Xiaodong; Shah, Syed Aziz; Ren, Aifeng; Fan, Dou; Zhao, Nan; Cao, Dongjian; Hu, Fangming; Ur-Rehman, Masood; Wang, Weigang; von Deneen, Karen M.; et al. (Institute of Electrical and Electronics Engineers (IEEE): OAJ / IEEE, 2018-01-24)
      Essential tremor (ET) is a neurological disorder characterized by rhythmic, involuntary shaking of a part or parts of the body. The most common tremor is seen in the hands/arms and fingers. This paper presents an evaluation of ETs monitoring based on finger-to-nose test measurement as captured by small wireless devices working in shortwave or [Formula: see text]-band frequency range. The acquired signals in terms of amplitude and phase information are used to detect a tremor in the hands. Linearly transforming raw phase data acquired in the [Formula: see text]-band were carried out for calibrating the phase information and to improve accuracy. The data samples are used for classification using support vector machine algorithm. This model is used to differentiate the tremor and nontremor data efficiently based on secondary features that characterize ET. The accuracy of our measurements maintains linearity, and more than 90% accuracy rate is achieved between the feature set and data samples.
    • Determination of optimal curing conditions for imaging single lung cancer cells by atomic force acoustic microscope

      Wang, Xuan; Zhao, Yujing; Zhang, Wenxiao; Wang, Ying; Tian, Liguo; Wang, Xinyue; Song, Zhengxun; Wang, Zuobin; Li, Dayou; Changchun University of Science and Technology; et al. (Institute of Electrical and Electronics Engineers Inc., 2018-11-29)
      This paper presents a method for the determination of optimal curing conditions for the imaging of single lung cancer cells by atomic force acoustic microscope (AFAM). The cellular morphology, height and surface roughness of the cells treated with different concentrations of paraformaldehyde and methanol and durations were observed using an AFAM. The experimental results showed that the A549 cells solidified with 4% paraformaldehyde for the period from 30min to 60min were close to the profiles of living cells. The cells solidified with 4% paraformaldehyde for 10min were beneficial to obtain subsurface structures.