arXiv subjects

Muhammad Usman

Publications and source records attributed to Muhammad Usman.

At least 181 records · Page 10Linked to original sources

arXiv2020

Lightweight Encryption for the Low Powered IoT Devices

The internet of things refers to the network of devices connected to the internet and can communicate with each other. The term things is to refer non-conventional devices that are usually not connected to the internet. The network of such devices or things is growing at an enormous rate. The security and privacy of the data flowing through these things is a major concern. The devices are low powered and the conventional encryption algorithms are not suitable to be employed on these devices. In this correspondence a survey of the contemporary lightweight encryption algorithms suitable for use in the IoT environment has been presented.

cs.CR↗

arXiv2020

Long-Range Surface-Assisted Molecule-Molecule Hybridization

Metalated phthalocyanines (Pc's) are robust and versatile molecular complexes, whose properties can be tuned by changing their functional groups and central metal atom. The electronic structure of magnesium Pc (MgPc) - structurally and electronically similar to chlorophyll - adsorbed on the Ag(100) surface is investigated by low-temperature scanning tunneling microscopy (STM) and spectroscopy (STS), non-contact atomic force microscopy (ncAFM) and density functional theory (DFT). Single, isolated MgPc's exhibit a flat, four-fold rotationally symmetric morphology, with doubly degenerate, partially populated (due to surface-to-molecule electron transfer) lowest unoccupied molecular orbitals (LUMOs). In contrast, MgPc's with neighbouring molecules in proximity undergo a lift of LUMOs degeneracy, with a near-Fermi local density of states with reduced two-fold rotational symmetry, indicative of a long-range attractive intermolecular interaction. The latter is assigned to a surface-mediated two-step electronic hybridization process. First, LUMOs interact with Ag(100) conduction electrons, forming hybrid molecule-surface orbitals with enhanced spatial extension. Then, these delocalized molecule-surface states further hybridize with those of neighbouring molecules. This work highlights how the electronic structure of molecular adsorbates - including orbital degeneracies and symmetries - can be significantly altered via surface-mediated intermolecular hybridization, over extended distances (beyond 3 nm), having important implications for prospects of molecule-based solid-state technologies.

cond-mat.mtrl-sci↗

arXiv2020

KaFHCa: Key-establishment via Frequency Hopping Collisions

The massive deployment of IoT devices being utilized by home automation, industrial and military scenarios demands for high security and privacy standards to be achieved through innovative solutions. This paper proposes KaFHCa, a crypto-less protocol that generates shared secret keys by combining random frequency hopping collisions and source indistinguishability independently of the radio channel status. While other solutions tie the secret bit rate generation to the current radio channel conditions, thus becoming unpractical in static environments, KaFHCa guarantees almost the same secret bitrate independently of the channel conditions. KaFHCa generates shared secrets through random collisions of the transmitter and the receiver in the radio spectrum, and leverages on the fading phenomena to achieve source indistinguishability, thus preventing unauthorized eavesdroppers from inferring the key. The proposed solution is (almost) independent of the adversary position, works under the conservative assumption of channel fading (σ = 8dB), and is capable of generating a secret key of 128 bits with less than 564 transmissions.

cs.CR↗

arXiv2020

A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)

This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A key novelty of MCML is quantification of the performance of and semantic differences among trained machine learning (ML) models, specifically decision trees, with respect to entire (bounded) input spaces, and not just for given training and test datasets (as is the common practice). MCML reduces the quantification problems to the classic complexity theory problem of model counting, and employs state-of-the-art model counters. The results show that relatively simple ML models can achieve surprisingly high performance (accuracy and F1-score) when evaluated in the common setting of using training and test datasets - even when the training dataset is much smaller than the test dataset - indicating the seeming simplicity of learning relational properties. However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.

cs.LG↗

arXiv2020

Security and Privacy in IoT Using Machine Learning and Blockchain: Threats & Countermeasures

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

cs.CR↗

arXiv2020

Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection

Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliability and high accuracy, and due to the presence of background noise in the heartbeat sound. In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution. Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models deliver the abnormal heartbeat classification score with significant improvement. Our proposed approach using RNNs can be potentially be used for real-time abnormal heartbeat detection in the Internet of Medical Things for remote monitoring applications.

cs.CV↗

arXiv2020

Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages

Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data is available. In this study, we investigate the problem of cross-lingual emotion recognition for Urdu language and contribute URDU---the first ever spontaneous Urdu-language speech emotion database. Evaluations are performed using three different Western languages against Urdu and experimental results on different possible scenarios suggest various interesting aspects for designing more adaptive emotion recognition system for such limited languages. In results, selecting training instances of multiple languages can deliver comparable results to baseline and augmentation a fraction of testing language data while training can help to boost accuracy for speech emotion recognition. URDU data is publicly available for further research.

cs.CL↗

arXiv2020

Tunable band-gap and isotropic light absorption from bismuth-containing GaAs core$-$shell and multi$-$shell nanowires

Semiconductor core$-$shell nanowires based on the GaAs substrate are building blocks of many photonic, photovoltaic and electronic devices, thanks to the associated direct band-gap and the highly tunable optoelectronic properties. The selection of a suitable material system is crucial for custom designed nanowires tailored for optimised device performance. The bismuth containing GaAs materials are an imminent class of semiconductors which not only enable an exquisite control over the alloy strain and electronic structure but also offer the possibility to suppress internal loss mechanisms in photonic devices. Whilst the experimental efforts to incorporate GaBiAs alloys in the nanowire active region are still in primitive stage, the theoretical understanding of the optoelectronic properties of such nanowires is only rudimentary. This work elucidates and quantifies the role of nanowire physical attributes such as its geometry parameters and bismuth incorporation in designing light absorption wavelength and polarisation response. Based on multi-million atom tight-binding simulations of the GaBiAs/GaAs core$-$shell and GaAs/GaBiAs/GaAs multi$-$shell nanowires, our results predict a large tuning of the absorption wavelength, ranging from 0.9 $μ$m to 1.6 $μ$m, which can be controlled by engineering either Bi composition or nanowire diameter. The analysis of the strain profiles indicates a tensile character leading to significant light-hole mixing in the valence band states. This offers a possibility to achieve polarisation-insensitive light interaction, which is desirable for several photonic devices involving amplification and modulation of light. Furthermore, at low Bi compositions, the carrier confinement is quasi type-II, which further broadens the suitability of these nanowires for myriad applications demanding large carrier separations...

cond-mat.mtrl-sci↗

arXiv2020

Polarization Independent Ground State Optical Transitions in Closely Stacked InAs/GaAs Columnar Quantum Dots

This work presents an analysis of the electronic and optical properties of InAs/GaAs columnar quantum dots (QDs) by performing multi-million-atom tight-binding simulations. The plots of the polarisation-dependent ground state optical transition strengths predict that a nearly zero degree of polarisation can be achieved at 1550 nm emission/absorption wavelength by engineering the number of QD layers in a columnar QD. These results are promising for the design of optical devices requiring polarisation insensitive optical response such as semiconductor optical amplifiers.

cond-mat.mes-hall↗

arXiv2020

Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding

Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.

cs.CV↗

arXiv2020

Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniques rely on radiologist 3-D volume of interest (VOI) input or use the constant region of interest (ROI) and only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to investigate the nodule presence outside the given VOI and also include the redundant structures into VOI, which may lead to inaccurate nodule segmentation. In this work, a novel semi-automated approach for 3-D segmentation of nodule in volumetric computerized tomography (CT) lung scans has been proposed. The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy. The adaptive ROI algorithm enables the solution to dynamically select the ROI for the surrounding slices to investigate the presence of nodule using deep residual U-Net architecture. The first stage provides the initial estimation of nodule which is further utilized to extract the VOI. At the second stage, the extracted VOI is further investigated along the coronal and sagittal axis with two different networks and finally, all the estimated masks are fed into the consensus module to produce the final volumetric segmentation of nodule. The proposed approach has been rigorously evaluated on the LIDC dataset, which is the largest publicly available dataset. The result suggests that the approach is significantly robust and accurate as compared to the previous state of the art techniques.

eess.IV↗

arXiv2019

Low Cost 3D Printing for Rapid Prototyping and its Application

In the recent years of industrial revolution, 3D printing has shown to grow as an expanding field of new applications. The low cost solutions and short time to market makes it a favorable candidate to be utilized in the dynamic fields of engineering. Additive printing has the vast range of applications in many fields. This study presents the wide range of applications of the 3D printers along with the comparison of the additive printing with the traditional manufacturing methods have been shown. A tutorial is presented explaining the steps involved in the prototype printing using Rhinoceros 3D and Simplify 3D software including the detailed specifications of the end products that were printed using the Delta 3D printer.

cs.RO↗

arXiv2019

Heart Segmentation From MRI Scans Using Convolutional Neural Network

Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder decoder type fully convolutional neural network models

eess.IV↗

arXiv2019

Towards low-loss telecom-wavelength photonic devices by designing GaBi$_{x}$As$_{1-x}$/GaAs core$-$shell nanowires

Nanowires are versatile nanostructures, which allow an exquisite control over bandgap energies and charge carrier dynamics making them highly attractive as building blocks for a broad range of photonic devices. For optimal solutions concerning device performance and cost, a crucial element is the selection of a suitable material system which could enable a large wavelength tunability, strong light interaction and simple integration with the mainstream silicon technologies. The emerging GaBiAs alloys offer such promising features and may lead to a new era of technologies. Here, we apply million-atom atomistic simulations to design GaBiAs/GaAs core-shell nanowires suitable for low-loss telecom-wavelength photonic devices. The effects of internal strain, Bi Composition (x), random alloy configuration, and core-to-shell diameter ratio ($\rm ρ_D$) are analysed and delineated by systematically varying these attributes and studying their impact on the absorption wavelength and charge carrier confinement. The complex interplay between x and $\rm ρ_D$ results in two distinct pathways to accomplish 1.55 um optical transitions: either fabricate nanowires with $\rm ρ_D \geq$ 0.8 and $x \sim$15\%, or increase $x$ to $\sim$30\% with $\rm ρ_D \leq$ 0.4. Upon further analysis of the electron hole wave functions, inhomogeneous broadening and optical transition strengths, the nanowires with $\rm ρ_D \leq$ 0.4 are unveiled to render favourable properties for the design of photonic devices. Another important outcome of our study is to demonstrate the possibility of modulating the strain character from a compressive to a tensile regime by simply engineering the thickness of the core region. The availability of such a straightforward knob for strain manipulation would be highly desirable for devices involving polarisation-sensitive light interactions.

cond-mat.mes-hall↗

arXiv2019

AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network

Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the heterogeneous sequence characteristics, AFPs are found to demonstrate a high degree of diversity, which makes their prediction a challenging task. Herein, we propose a machine learning framework to deal with this vigorous and diverse prediction problem using the manifolding learning through composition of k-spaced amino acid pairs. We propose to use the deep neural network with skipped connection and ReLU non-linearity to learn the non-linear mapping of protein sequence descriptor and class label. The proposed antifreeze protein prediction method called AFP-CKSAAP has shown to outperform the contemporary methods, achieving excellent prediction scores on standard dataset. The main evaluater for the performance of the proposed method in this study is Youden's index whose high value is dependent on both sensitivity and specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on the independent dataset, which is better than previous methods.

q-bio.QM↗

arXiv2019

Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

Due to the dynamic nature, chaotic time series are difficult predict. In conventional signal processing approaches signals are treated either in time or in space domain only. Spatio-temporal analysis of signal provides more advantages over conventional uni-dimensional approaches by harnessing the information from both the temporal and spatial domains. Herein, we propose an spatio-temporal extension of RBF neural networks for the prediction of chaotic time series. The proposed algorithm utilizes the concept of time-space orthogonality and separately deals with the temporal dynamics and spatial non-linearity(complexity) of the chaotic series. The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF. The spatio-temporal RBF is shown to out perform the standard RBFNN by achieving significantly reduced estimation error.

stat.ML↗

arXiv2019

Quantum Calculus-based Volterra LMS for Nonlinear Channel Estimation

A novel adaptive filtering method called $q$-Volterra least mean square ($q$-VLMS) is presented in this paper. The $q$-VLMS is a nonlinear extension of conventional LMS and it is based on Jackson's derivative also known as $q$-calculus. In Volterra LMS, due to large variance of input signal the convergence speed is very low. With proper manipulation we successfully improved the convergence performance of the Volterra LMS. The proposed algorithm is analyzed for the step-size bounds and results of analysis are verified through computer simulations for nonlinear channel estimation problem.

math.OC↗

arXiv2019

Atomistic tight binding study of quantum confined Stark effect in GaBi$_x$As$_{1-x}$/GaAs quantum wells

Recently, there has been tremendous research interest in novel bismide semiconductor materials (such as GaBi$_x$As$_{1-x}$) for wavelength-engineered, low-loss optoelectronic devices. We report a first study of the quantum confined Stark effect (QCSE) computed for GaBi$_x$As$_{1-x}$/GaAs quantum well (QW) structures based on large-scale atomistic tight-binding calculations. A comprehensive investigation of the QCSE as a function of the applied electric field orientations and the QW Bi fractions reveals unconventional character of the Stark shift at low Bi compositions ($x$=3.125\%). This atypical QCSE is attributed to a strong confinement of the ground-state hole wave functions due to the presence of Bi clusters. At technologically-relevant large Bi fractions ($\geq$ 10\%), the impact of Bi clustering on the electronic structure is found to be weak, leading to a quadratic Stark shift of the ground-state transition wavelength, similar to the previously observed Stark shift in other conventional III-V materials. Our results provide useful insights for the understanding of the electric field dependence of the electronic and optical properties of GaBi$_x$As$_{1-x}$/GaAs QWs, and will be important for the design of devices in the optoelectronics and spintronics areas of research.

cond-mat.mtrl-sci↗