• Nano-ferrite near-field microwave imaging for in-body applications

      Abbasi, Qammer Hussain; Ren, Aifeng; Qing, Maojie; Zhao, Nan; Wang, Mingming; Gao, Ge; Yang, Xiaodong; Zhang, Zhiya; Hu, Fangming; Ur-Rehman, Masood; et al. (Institute of Electrical and Electronics Engineers, 2018-06-04)
      In recent years, nanotechnology has become indispensable in our lives, especially in the medical field. The key to nanotechnology is the perfect combination of molecular imaging and nanoscale probes. In this paper, we used iron oxide nanoparticles as a nanoprobe because it is widely used in clinical MRI and other molecular imaging techniques. We built our own experimental environment and used absorbing materials during the whole experiment to avoid electromagnetic interference with the surroundings. Moreover, we repeated the experiment many times to exclude the influence of contingency. Hence, the experimental data we obtained were relatively precise and persuasive. Finally, the results demonstrated that the iron oxide nanoparticles were appropriate for use as contrast agents in biological imaging.
    • Nanoantenna arrays combining enhancement and beam control for fluorescence-based sensing applications

      Dorh, N.; Sarua, A; Ajmal, Tahmina; Okache, Julius; Rega, C.; Müller, G.; Cryan, M.; University of Bristol; University of Bedfordshire; ABB Ltd; et al. (OSA - The Optical Society, 2017-12-31)
      This paper presents measured fluorescence enhancement results for ~250 × 250 element aluminum nanoantenna arrays fabricated using electron beam lithography. The arrays have been designed to use diffractive coupling to enhance and control the direction of fluorescent emission. Highly directional emission is obtained at the designed angles with beam widths simulated to be in the range of 4–6°. Angle-resolved spectroscopy measurements of dye-coated nanoantenna arrays were in good agreement with finite difference time domain modeling. Critically, these results were obtained for near UV wavelengths (~360 nm), which is relevant to a number of biosensing applications.
    • Navigating health literacy using interactive data visualisation

      Liu, Enjie; Zhao, Youbing; Wei, Hui; Roumeliotis, Stefanos; Kaldoudi, Eleni; University of Bedfordshire; University of Thrace (Institute of Electrical and Electronics Engineers Inc., 2016-12-19)
      It is commonly concluded that health literacy focuses on individual skills to obtain, process and understand health information and services necessary to make appropriate health decisions. To achieve this, an individual first needs to obtain an adequate level of health literacy. However, nowadays, the information that individuals encounter with regards to their health, the amount, credibility and quality of the data make it difficult for one to make judgments on their health and disease progression, let alone make informed decisions on behaviour change. In this paper, we will report our work in providing patients with efficient ways to explore and understand the relevant health literacy. We focus on two data types: 1) harvested medical evidence from PubMed on cardiorenal disease and its comorbidities, 2) data collected from patients including from PHR and wearable sensors. Our work provides ways for patients to visualise this data meaningfully. Our work aims to improve the health literacy for the general public and increase the population's understanding of the medical field, thus helping users to make informed decision with regards to their care.
    • Nephroblastoma analysis in MRI images

      Kaba, Djibril; McFarlane, Nigel J.B.; Dong, Feng; Graf, Norbert; Ye, Xujiong; University of Bedfordshire; Saarland University Hospital; University of Lincoln (International Society for Stereology, 2019-12-31)
      The annotation of the tumour from medical scans is a crucial step in nephroblastoma treatment. Therefore, an accurate and reliable segmentation method is needed to facilitate the evaluation and the treatments of the tumour. The proposed method serves this purpose by performing the segmentation of nephroblastoma in MRI scans. The segmentation is performed by adapting and a 2D free hand drawing tool to select a region of interest in the scan slices. Results from 24 patients show a mean root-mean-square error of 0.0481 ± 0.0309, an average Dice coefficient of 0.9060 ± 0.0549 and an average accuracy of 99.59% ± 0.0039. Thus the proposed method demonstrated an effective agreement with manual annotations.
    • Network coding for reliable safety message communication in vehicular Ad-Hoc networks: a review

      Muhammad, Shehu Jabaka; Zhang, Sijing; Dyo, Vladimir; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-10-26)
      Advancements in hardware, software, and computing technologies make the design and application of series and diverse kinds of networks deployment in several environments possible. An instance of such networks greeted with the enormous interest in research and industries is the vehicular ad-hoc networks (VANETs). Currently, information dissemination in the practical communication networks is achieved by routing. However, network coding can be thought of as the hopeful generalisation of routing that has further potential to network changing situations. Despite the existence of numerous studies on the applicability of network coding to broadcasting communications for mobile ad-hoc networks, there are few broadcasting protocols designed for VANETs that applied network coding. This paper reviews some of the applications of network coding for reliable safety message communication in VANETs, classifying them based on the transmission orientation and divulging the gain realized when the method is used. A summary table presenting the comparative study of the protocols is provided.
    • A new algorithm for private cloud

      Feng, Xiaohua (IJETAE, 2014-10-08)
      An operative encryption algorithm is the goal that cyber security researchers have been seeking. In this paper, we briefly report a newly developed encryption algorithm. An implementation has been carried out, the testing result shown this algorithm is effective. We focus on the processing speed and discuss the comparison with others. It has been recognized as an feasible algorithm, which is able to applied to practical circumstances.
    • A new approach for selecting informative features for text classification

      Ghasem, Zinnar; Frommholz, Ingo; Maple, Carsten; University of Bedfordshire; University of Warwick (CEUR-WS, 2015-12-31)
      Selecting useful and informative features to classify text is not only important to decrease the size of the feature space, but as well for the overall performance and precision of machine learning. In this study we propose a new feature selection method called Informative Feature Selector (IFS). Different machine learning algorithms and datasets have been utilised to examine the effectiveness of IFS, and it is compared to well-established methods, namely Information Gain, Odd Ratio, Chi Square, Mutual Information and Class Discriminative Measure. Our experiments show that IFS is able to outperform aforementioned methods and to produce effective and efficient results.
    • A new digital forensics model of smart city automated vehicles

      Feng, Xiaohua; Dawam, Edward Swarlat; Amin, Saad; University of Bedfordshire; Coventry University (2017-06-23)
      In the modern world, cyber societies are full of complications. The Internet has brought so many convenient services to our society but Internet is also a mine field. Mass surveillance from smart phone to PC, from automated car to smart television, any online device seems could be turn to privacy breach toolkit. In order to follow the GDPR (General Data Protection Regulation), protect privacy data, including PII (Personally Identifiable Information), against Cyberstalking and many other cybercrime challenges, a novel Digital Forensics Model served for Smart City Automated Vehicles has been developed working on investigating AAV (Autonomous Automated Vehicle) cases. The proposed development is reported to Big Data 2017. Here, we report the update for discussions
    • New intuitive metrics for diversity performance evaluation of multi-element antenna systems

      Papamichael, V.C.; Karadimas, Petros (Electromagnetics Academy, 2015-07-31)
      Diversity performance of multi-element antenna (MEA) systems is evaluated using several metrics. The most common are the diversity antenna gain (DAG), the effective diversity gain (EDG) and the actual diversity gain (ADG). These metrics calculate the performance by comparing the MEA system with the reference antenna at a fixed outage probability level, i.e., usually at 1% outage level. As fixed outage levels are just an indication of probability of occurrence of specific signal levels, they provide limited insight into realistic deep-fade cases. Thus, we introduce three novel metrics, namely, the fading mitigation gain (FMG), the reliability percentage (RP) and the generalized diversity antenna gain (GDAG) for characterizing diversity performance when deep fades take place in one or more of the diversity branches. Based on the aforementioned metrics, intuitive knowledge on the diversity performance of MEA systems is provided.
    • New random block cipher algorithm

      Albermany, Salah A.; Hamade, Fatima Radi; Safdar, Ghazanfar Ali; University of Kufa; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2017-07-03)
      This paper proposed a new secret key random block cipher as a candidate for Reaction Automata Direct Graph (RADG method). A new secret key consists of a 64-bit block size and a 128-bit key length. A new algorithm based in design on Generalized Unbalanced Feistel Network and uses a new S-boxes generated from a DES S-boxes, a bijective F function, bitwise operations and a carefully key schedule design. The new algorithm uses in protected wireless network and comparing with the old system (without encryption key), the proposed designs are designed for security purposes as characterized highly efficient encrypting large data. As for the analysis for the proposed block cipher design proves terms of privacy, data integrity, authentication and non-repudiation.
    • A non-enzymatic glucose sensor via uniform copper nanosphere fabricated by two-step method

      Yu, Miaomiao; Weng, Zhankun; Hu, Jing; Zhu, Xiaona; Song, Hangze; Wang, Shenzhi; Cao, Siyuan; Song, Zhengxun; Xu, Hongmei; Li, Jinhua; et al. (Elsevier Ltd, 2021-08-10)
      Herein, we explored an effective way to obtain uniform copper nanoparticles by irradiating Cu2O microparticles in ethanol with a 1064 nm laser. The morphology, structure and chemical composition of as-prepared copper nanoparticles were characterized by scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and X-ray photoelectron spectroscopy. It is interesting that the diameter of obtained spherical copper nanoparticles can be finely tuned by changing the irradiation time. Moreover, we also found that the particle size of copper nanoparticles can be reduced to ~63 nm when the irradiation time is 30 min. Inspired by the fast-developing non-enzymatic glucose sensors, the electrochemical activity of the copper nanoparticles toward glucose in alkaline media was further investigated. Notably, the electrochemical results reveal that the prepared copper nanoparticles possess a good prospect in non-enzymatic glucose sensor.
    • Nonreciprocity compensation combined with turbo codes for secret key generation in vehicular ad hoc social IoT networks

      Epiphaniou, Gregory; Karadimas, Petros; Ben Ismail, Dhouha Kbair; al-Khateeb, Haider; Dehghantanha, Ali; Choo, Kim-Kwang Raymond (Institute of Electrical and Electronics Engineers Inc., 2017-10-18)
      The physical attributes of the dynamic vehicle-to-vehicle propagation channel can be utilized for the generation of highly random and symmetric cryptographic keys. However, in a physical-layer key agreement scheme, nonreciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements. This has to be addressed prior to the symmetric key generation which is inherently important in Social Internet of Things networks, including in adversarial settings (e.g., battlefields). In this paper, we parametrically incorporate temporal variability attributes, such as 3-D scattering and scatterers’ mobility. Accordingly, this is the first work to incorporate such features into the key generation process by combining nonreciprocity compensation with turbo codes (TCs). Preliminary results indicate a significant improvement when using TCs in bit mismatch rate and key generation rate in comparison to sample indexing techniques.
    • A novel approach to knowledge discovery and representation in biological databases

      Lu, Jing; Wang, Cuiqing; Keech, Malcolm (Inderscience, 2017-09-25)
      Extraction of motifs from biological sequences is among the frontier research issues in bioinformatics, with sequential patterns mining becoming one of the most important computational techniques in this area. A number of applications motivate the search for more structured patterns and concurrent protein motif mining is considered here. This paper builds on the concept of structural relation patterns and applies the concurrent sequential patterns (ConSP) mining approach to biological databases. Specifically, an original method is presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Data modelling is pursued to represent the more interesting concurrent patterns visually. Experiments with real-world protein datasets from the UniProt and NCBI databases highlight the applicability of the ConSP methodology in protein data mining and modelling. The results show the potential for knowledge discovery in the field of protein structure identification. A pilot experiment extends the methodology to DNA sequences to indicate a future direction.
    • A novel classified ledger framework for data flow protection in AIoT networks

      Han, Daoqi; Wu, Songqi; Hu, Zhuoer; Gao, Hui; Liu, Enjie; Lu, Yueming; Beijing University of Posts and Telecommunications; University of Bedfordshire (Hindawi, 2021-02-19)
      The edge computing node plays an important role in the evolution of the artificial intelligence-empowered Internet of things (AIoTs) that converge sensing, communication, and computing to enhance wireless ubiquitous connectivity, data acquisition, and analysis capabilities. With full connectivity, the issue of data security in the new cloud-edge-terminal network hierarchy of AIoTs comes to the fore, for which blockchain technology is considered as a potential solution. Nevertheless, existing schemes cannot be applied to the resource-constrained and heterogeneous IoTs. In this paper, we consider the blockchain design for the AIoTs and propose a novel classified ledger framework based on lightweight blockchain (CLF-LB) that separates and stores data rights at the source and enables a thorough data flow protection in the open and heterogeneous network environment of AIoT. In particular, CLF-LB divides the network into five functional layers for optimal adaptation to AIoTs applications, wherein an intelligent collaboration mechanism is also proposed to enhance the across-layer operation. Unlike traditional full-function blockchain models, our framework includes novel technical modules, such as block regenesis, iterative reinforcement of proof-of-work, and efficient chain uploading via the system-on-chip system, which are carefully designed to fit the cloud-edge-terminal hierarchy in AIoTs networks. Comprehensive experimental results are provided to validate the advantages of the proposed CLF-LB, showing its potentials to address the secrecy issues of data storage and sharing in AIoTs networks.
    • A novel dual ultrawideband CPW-fed printed antenna for Internet of Things (IoT) applications

      Awais, Qasim; Chattha, Hassan Tariq; Jamil, Mohsin; Jin, Yang; Tahir, Farooq Ahmad; Ur-Rehman, Masood; Chongqing University; Islamic University of Madinah; National University of Sciences and Technology (NUST), Islamabad; University of Bedfordshire (Hindawi, 2018-03-28)
      This paper presents a dual-band coplanar waveguide (CPW) fed printed antenna with rectangular shape design blocks having ultrawideband characteristics, proposed and implemented on an FR4 substrate. The size of the proposed antenna is just 25 mm × 35 mm. A novel rounded corners technique is used to enhance not only the impedance bandwidth but also the gain of the antenna. The proposed antenna design covers two ultrawide bands which include 1.1–2.7 GHz and 3.15–3.65 GHz, thus covering 2.4 GHz Bluetooth/Wi-Fi band and most of the bands of 3G, 4G, and a future expected 5G band, that is, 3.4–3.6 GHz. Being a very low-profile antenna makes it very suitable for the future 5G Internet of Things (IoT) portable applications. A step-by-step design process is carried out to obtain an optimized design for good impedance matching in the two bands. The current densities and the reflection coefficients at different stages of the design process are plotted and discussed to get a good insight into the final proposed antenna design. This antenna exhibits stable radiation patterns on both planes, having low cross polarization and low back lobes with a maximum gain of 8.9 dB. The measurements are found to be in good accordance with the simulated results.
    • A novel framework to provide reliable and timely communication for industrial control applications using wireless token passing approach

      Karimireddy, Thanmayee; Zhang, Sijing; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2017-05-04)
      This paper investigates the strict reliable and timely requirements of the hard real traffic based control packets in the industrial control applications. As a result, a framework is proposed with two novel approaches intended to meet the strict deadline requirements and reliability requirements of the industrial control packets. The first novel approach, timed control panel incorporates timed token protocol to provide guaranteed channel access time to each station present in the wireless ring structure. The second novel approach, adaptive sub-MAC Hybrid Automatic Repeat Request (HARQ) reduces the packet error rate and the packet loss rate by reducing the size of MAC frame by half. The mathematical analysis and simulation results show that the proposed framework ensures in providing the reliable and timely communications for Industrial control applications.
    • Novel malware detection methods by using LCS and LCSS

      Mira, Fahad; Brown, Antony; Huang, Wei; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-10-24)
      The field of computer security faces numerous vulnerabilities which cause network resources to become unavailable and violate systems confidentiality and integrity. Malicious software (Malware) has become one of the most serious security threats on the Internet. Malware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based on dynamic analysis of malware is proposed whereby Longest Common Subsequence (LCSS) and Longest Common Substring (LCS) algorithms are adopted to accurately detect malware. The empirical results show that the proposed approach performs favorably compared to other related work that use API call sequences.
    • A numerical study of the effects of oxy-fuel combustion under homogeneous charge compression ignition regime

      Mobasheri, Raouf; Aitouche, Abdel; Peng, Zhijun; Li, Xiang; Centre de Recherche en Informatique Signal et Automatique de Lille; Junia; University of Bedfordshire (SAGE Publications Ltd, 2021-02-16)
      The European Union (EU) has recently adopted new directives to reduce the level of pollutant emissions from non-road mobile machinery engines. The main scope of project RIVER for which this study is relating is to develop possible solutions to achieve nitrogen-free combustion and zero-carbon emissions in diesel engines. RIVER aims to apply oxy-fuel combustion with Carbon Capture and Storage (CCS) technology to eliminate NOx emissions and to capture and store carbon emissions. As part of this project, a computational fluid dynamic (CFD) analysis has been performed to investigate the effects of oxy-fuel combustion on combustion characteristics and engine operating conditions in a diesel engine under Homogenous Charge Compression Ignition (HCCI) mode. A reduced chemical n-heptane-n-butanol-PAH mechanism which consists of 76 species and 349 reactions has been applied for oxy-fuel HCCI combustion modeling. Different diluent strategies based on the volume fraction of oxygen and a diluent gas has been considered over a wide range of air-fuel equivalence ratios. Variation in the diluent ratio has been achieved by adding different percentages of carbon dioxide for a range from 77 to 83 vol.% in the intake charge. Results show that indicated thermal efficiency (ITE) has reduced from 32.7% to 20.9% as the CO2 concentration has increased from 77% to 83% at low engine loads while it doesn’t bring any remarkable change at high engine loads. It has also found that this technology has brought CO and PM emissions to a very ultra-low level (near zero) while NOx emissions have been completely eliminated.
    • On textual analysis and machine learning for cyberstalking detection

      Frommholz, Ingo; al-Khateeb, Haider; Potthast, Martin; Ghasem, Zinnar; Shukla, Mitul; Short, Emma; University of Bedfordshire; Bauhaus-Universität Weimar (Springer, 2016-06-01)
      Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.
    • On the impact of mobility on battery-less RF energy harvesting system performance

      Munir, Bilal; Dyo, Vladimir (MDPI, 2018-10-23)
      The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.