Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of the Impact of Uncertainty on the Internet of Things: A Smart Home Case Study

The Internet of Things (IoT) is the enabling technology for a range of smart domains and application areas. IoT consists of sensors, actuators, gateways and cloud infrastructures which together support the processing of information to enable decision makers to take appropriate actions in a given scenario. IoT infrastructures may, however, be associated with unreliable sensors and network infrastructures (e.g., due to incompleteness, inconsistency, inaccuracy and inaccessibility) which will lead to uncertainty in the decision-making process and as a result take inappropriate actions which may pose a threat to the safety and security of its users. The work presented in this paper focuses on analysing and evaluating the impact of different types of data imperfections and unreliable sensor data on the process of classification within an IoT instance applied in the domain of smart homes (SH). Four classifiers, namely Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (kNN) and Naïve Bayes (NB) were chosen for evaluation purpose. Classification results based on an openly available SH dataset were analysed and compared to evaluate and illustrate the performance and robustness of classifiers. The results indicated that it is difficult to declare a single classifier with high performance and robustness. Nevertheless, the RF classifier achieved the highest accuracy of 95.51%, 83.27% and 85.08% for attribute noise, missing and failure cases, respectively while NB achieved a highest accuracy of 79.5% in the case of class noise.
Process Complexity and Cost Considerations of Multi-Layer Die Stacks

The increased requirements of data processing and reduced data latency has driven the demand for multi-layer 3D stacks for high performance systems and memory applications. In this paper, different approaches for multi-layer stacking are considered, including wafer-to-wafer (W2W) and die-to-die (D2D) stacking. The complexity of each approach is evaluated in terms of manufacturing cost and its impact on the stacked-system yield.
Compressed Higher Order Modes Slot Loaded Trapezoidal Antenna for Electromagnetic Imaging

A wideband trapezoidal antenna operating at compressed high-order modes is proposed for microwave imaging applications. The antenna is investigated using characteristic mode analysis to show that by loading the slots and using the curved trapezoidal shape, the high-order modes shift to the lower frequency enhance the bandwidth. Multi-square-slotted-ring unit cells are integrated as an artificial magnetic conductor that is located below the antenna to act as a reflector. The proposed antenna operates from 0.85 GHz to 3.7 GHz with an average gain and front to back ratio of 10.5 dBi and 19.5 dB, respectively.
Learning Automata Based Q-Learning RACH Access Scheme for Cellular M2M Communications

This paper introduces a novel Learning Automata (LA) scheme to improve the performance of a Q-learning based RACH access (QL-RACH) scheme, when applied for combined human and Machine-to-Machine (M2M) communication in a cellular network. The scheme employs an LA technique to eliminate the excessive punishment suffered by the M2M users by controlling the use of a penalty factor in the QL-RACH scheme. Simulation results show that the proposed LA-QL-RACH scheme improves the RACH throughput by 82% and reduces the access delay performance by 79%, thereby eliminating the repetitive collisions experienced in the dedicated M2M slots.
Low Cost, Electrically small, Quasi-Isotropic Antenna Based on Split Ring Resonator

An electrically small, quasi-isotropic antenna is proposed and fabricated. The quasi-isotropic electrically small antenna (ESA) is composed of two driven striplines and a split ring resonator (SSR) with two extended strips printed on the same side of the substrate. The SRR could generate both equivalent electric and magnetic dipoles with almost the same amplitude of the realized gains. The radiation patterns between the equivalent electric and magnetic dipoles are orthogonal to each other. The measurement results agrees well with the simulation values, which revealed that the ESA antenna has a quasi-isotropic radiation pattern possessing the maximum gain difference around 3 dB covering the whole space, an electrically small size ($ka=0.68$), and an overall efficiency 77%.
Machine Learning for Security at the IoT Edge - A Feasibility Study

Benefits of edge computing include reduced latency and bandwidth savings, privacy-by-default and by-design in compliance with new privacy regulations that encourage sharing only the minimal amount of data. This creates a need for processing data locally rather than sending everything to a cloud environment and performing machine learning there. However, most IoT edge devices are resource-constrained in comparison and it is not evident whether current machine learning methods are directly employable on IoT edge devices. In this paper, we analyze the state-of-the-art machine learning (ML) algorithms for solving security problems (e.g. intrusion detection) at the edge. Starting from the characteristics and limitations of edge devices in IoT networks, we assess a selected set of commonly used ML algorithms based on four metrics: computation complexity, memory footprint, storage requirement and accuracy. We also compare the suitability of ML algorithms to different cybersecurity problems and discuss the possibility of utilizing these methods for use cases.
Malware Family Classification using Active Learning by Learning

In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples.
Object Handling of Cognitive Robots Using Deep Leaning Based Object Recognition

In this paper, a methodology of object handling using deep learning based object recognition for cognitive robot is proposed. A cognitive robot using a sentential cognitive system expresses all experienced events as a sentential form and store in a memory to be retrieved for responding to order of human. For the case of an event dealing with objects, a deep learning is used for detecting labels and bounding boxes from the captured data from a RGB-D camera. The segmented 3D information of an object extracted from bounding box and depth data is stored in an object descriptor of the cognitive system for being used for object related conversation. The experimental results show the applicability of the proposed approach to more advanced human robot interaction.
Efficient Privacy-Preserving Scheme for Location Based Services in VANET System

A Vehicular Ad-hoc Network (VANET) is a type of Mobile Ad-hoc Network (MANET) that is used to provide communications between nearby vehicles, and between vehicles and fixed infrastructure on the roadside. VANET is not only used for road safety and driving comfort but also for infotainment. Communication messages in VANET can be used to locate and track vehicles. Tracking can be beneficial for vehicle navigation using Location Based Services (LBS). However, it can lead to threats on location privacy of vehicle users; since it can profile them and track their physical location. Therefore, to successfully deploy LBS, user’s privacy is one of major challenges that must be addressed. In this paper, we propose Privacy-Preserving Fully Homomorphic Encryption over Advanced Encryption Standard (P2FHE-AES) scheme for LBS query. This scheme is required for location privacy protection to encourage drivers to use this service without any risk of being pursued. It is implemented using Network Simulator (NS-2), Simulation of Urban Mobility (SUMO), and Cloud simulation (CloudSim). Analysis and evaluation results demonstrate that P2FHE-AES scheme can preserve the privacy of the drivers’ future routes in an efficient and secure way. The results prove the feasibility and efficiency of P2FHE-AES scheme in terms of query’s response time, query accuracy, throughput and query overhead.
Deep Learning for Channel-Agnostic Brain Decoding across Multiple Subjects

Accurate decoding of perceptual information from brain signals is crucial in real-world BCI applications. While existing decoding methods work well in static, single-subject cases, more versatile, multi-subject decoding methods should be developed for achieving scalable and transferable BCI systems. In practice, it is not straightforward to record brain signals using the same recording equipment from a large number of subjects. If a pretrained decoder is not robust to subject or channel shifts, it cannot be applied to data from novel subjects and even from trained subjects when the recording equipment changes. In this work, we study brain decoding across multiple subjects with a different number of recording channels and channel location shifts. We consider channel-agnostic brain decoding as a multi-instance learning problem, where each input is seen as a set of instances. We propose a novel decoder architecture based on three building blocks: a channel-wise transform, an across-channel transform, and multi-channel pooling. We conduct a thorough experiment on our multi-subject electrocorticography (ECoG) classification dataset to verify the effectiveness of our proposed methods against other baseline architectures. Our results show that, even without any explicit spatial information about channels, our proposed architecture with channel permutation invariance and channel interactions work well in channel-agnostic multi-subject brain decoding.
A Performance Evaluation of Sensor Nodes in the Home Automation System based on Arduino

In this paper, a home automation system model was constructed with purpose to investigate of the correlation between the performance of wireless communication, power consumption of the constrained IoT devices and security. The series of experiments were conducted using sensor nodes connected to Arduino microcontroller and an RF 433 MHz wireless communication module. Measurements of the execution time and power consumption of the Arduino during data transfer with different security levels, as well as the analysis of the experimental results, were performed. The results show that the lifetime of the IoT device is determined by the communication speed, sleep mode management and depends on encryption. The obtained results can be used to minimize the power consumption of the device and improve communication efficiency. The results show that applied security reduces the productivity and lifetime of the sensor node not significantly.
Multimodal Simulation of Large Area Silicon Photomultipliers for Time Resolution Optimization

The time response of a Silicon Photomultiplier (SiPM) depends on some of the intrinsic parameters of the sensor. Combining multiple and small SiPM instead of one with larger area will reduce the detector capacitance at electronic level, which can be translated into a lower signal jitter. This will improve the Single Photon Time Resolution (SPTR) of the sensor and the electronics and thus the Coincidence Time Resolution (CTR) of a PET system. This effect is studied using a combination of a physics simulation environment (GATE) and a commercial electrical simulator. GATE is an advanced opensource software developed by the international collaboration OpenGATE, dedicated to numerical simulations in medical imaging and radiotherapy. Accurate modelling of photon interactions with crystal surfaces is essential in optical simulations, but the existing UNIFIED model in GATE is often inaccurate, especially for rough surfaces. A new approach has been developed for GATE, named Davis Model. This method calculates the reflectance properties from the crystal topography previously measured with a surface scan device (AFM, CONFOCAL…). In combination with GATE, an electrical simulator is being used on this work to simulate the response of the SiPM and the front-end readout electronics. The aim of this work is to provide a framework that will enable a global optimization of the PET system that consider the scintillator, the sensor (sensor size, pixel pitch, dead area, capacitance) and the readout electronics (input impedance, noise, bandwidth, summation).
Power-Recycling Microwave Mixer With Wide Linear Instantaneous Bandwidth

A self-embedded power-recycling technique in microwave mixers is proposed and presented for radio frequency (RF) and microwave multiple-input multiple-output (MIMO) systems. The novelty of the proposed power-recycling technique stems from a distinct concept of harnessing direct current (dc) signal rectified by mixing elements in the course of a frequency translation. The large-signal model of a nonlinear mixing element coupled with electromagnetic simulations is used to understand and examine the contribution of the dc signal in the overall transceiver energy efficiency. A single-balanced (SB) diode-switching mixer integrated with a diplexer bank is prototyped using off-the-shelf components to validate the power-recycling technique, which can be extended for an IC implementation. The measurement results show that about 30% of the driving signal power is directly present in the form of a dc signal at the power-recycling port under the maximum conversion efficiency condition. Furthermore, wide linear instantaneous bandwidth performance is shown with a resistive termination of sum–product signal in a downconverter.
A Comparative Study of User Data Security and Privacy in Native and Cross Platform Android Mobile Banking Applications

This research reports on security and privacy of user data transmitted in Android mobile banking applications. The basis of this approach was to experiment and urge a choice of development framework which ensures user data privacy in banking mobile applications of banks operating in Ghana. The results obtained directed attention to the fact that mobile banking applications developed using cross platform development frameworks (particularly Cordova) make applications less vulnerable by making use of most of the permissions requested ensuring user data privacy as compared to applications development using native development framework.
Energy Optimization of Streaming Applications in IoT on NoC Based Heterogeneous MPSoCs using Re-Timing and DVFS

The Multiprocessor System-on-Chip (MPSoC) computing architectures are widely adopted in modern embedded systems for real-time applications due to their high performance, reliability, and Quality-of-Service (QoS). Green computing or energy-efficient task scheduling is a critical technological challenging facet in an energy constrained embedded systems because higher energy consumption limits the lifetime of the computing platform and causes an increased carbon footprint. In this paper, we investigate energy-aware task scheduling on Dynamic Voltage and Frequency Scaling (DVFS) enabled Network-on-Chip (NoC) based Heterogeneous MPSoCs (HMPSoCs). We transform the intra-data dependencies into inter-data dependencies of the tasks with precedence constraints represented by Directed Acyclic Graph (DAG). We further implement Energy-efficient Task Scheduling Heuristic (ETSH) algorithm embedded with a list scheduler to perform energy-aware task scheduling while considering the energy performance profiles of the processors and task deadlines. The observed results on 5 real-world and 5 synthetic Task Graphs (TGs) adopted from Embedded Systems Synthesis (E3S) benchmarks suit demonstrate that ETSH outperforms state-of-the-art technique. Concisely, it achieves 20% and 38% average energy-efficiency with and without using coarse-grained software pipelining respectively.
Handling Fingerprint Sparsity for Wi-Fi Based Indoor Localization in Complex Environments

In Wi-Fi based indoor localization, the fingerprint approach is one of the most popular solutions to achieving satisfactory positioning performance based on the requirements of many commercialized applications. However, the performance of such an approach heavily relies on the density of reference points collected offline. Previous efforts have tried to use regression based approaches to alleviate the influence caused by the sparsity of fingerprints and enhance the localization accuracy. Nevertheless, we observe that regression based methods are not applicable in complex environments. We first verify this observation based on experiments and give insight into the problem. Then a general framework is proposed to handle fingerprint sparsity in complex environments (HFSCE). In HFSCE, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to reduce the impact of complex indoor environments on regression. The purpose is to distinguish uncorrelated Received Signal Strength (RSS) data and cluster similar RSS data. After the clustering of RSS data, regression approaches are applied to estimate the continuous space and hence the localization model is built with finer pattern of the environment. Note that the proposed HFSCE framework can accommodate any regression model. In the online phase, we design an algorithm selection scheme, which chooses the most suitable localization approach and regression model for current RSS input. Series of experiments show our proposed approach has significant improvement in localization accuracy when compared with the state-of-the-art approaches.
A SimSET-STIR hybrid Monte Carlo model for the Philips Vereos Digital PET

The aim of this study was to develop an accurate simulation and reconstruction model for the Philips Vereos digital whole-body PET/CT scanner. The model was developed using the SimSET Monte Carlo (MC) simulation code, storing all detected photons in SimSET list-mode (LM) format (history files). LM processing, binning and image reconstruction were performed using STIR recently added LM capabilities. In order to accurately process the listmode files generated by SimSET, a new interface between SimSET and STIR was developed. The performance of our MC model was evaluated using the NEMA NU2-2012 standard. Spatial resolution was in good agreement with published results(±12%). Count-rate performance was accurate overall, with NEC rates accurate for both low (32.5% vs. 31.7% at ≈ 5 KBq/mL), and high counts (146.2 kcps vs 153.4 at ≈ 55 KBq/mL), and consistent prompts for all the activity concentrations. LM-MLEM reconstruction including scatter and attenuation correction was performed to validate the SimSET-STIR developed interface.
Wavelet and KICA based ECG Beat Classification for Cardiac Health Care

Analysis of disturbances in Electrocardiogram (ECG) signal is a challenging task for diagnosis of cardiac diseases accurately. ECG being a non stationary signal, its amplitude and time duration keeps on changing. Computer designed techniques or algorithms are effective diagnostic tools for cardiac health management. In this proposed method, five ECG beats from ‘MIT/BIH’ arrhythmia database have been taken The approach adopted is to decompose ECG signal using Discrete Wavelet Transform (DWT) and then to apply Kernel Independent Component Analysis (KICA) for reducing the vector dimensions and non linear feature extraction to get desired dataset. Three classifiers K nearest neighbor (KNN), feed forward neural network (FNN) and back propagation neural network (BPNN) are independently deployed to achieve classification performance. The highest accuracy of 99.8% is achieved by using KICA with KNN.
Variable Step Size-Least Mean Squared Algorithm for ECG Signal Denoising Considering Baseline Wander Noise and Cyber Attacks

The advancement in the field of information and communication technology explores new paths for healthcare and E-health systems. Generally, the electrocardiographic (ECG) signals are contaminated with different impairments which impose difficulty to analysis and diagnosis the heart diseases. In contrast to the traditional artifacts removal techniques such as wavelet transform, and neural networks, this paper proposes a variable step size-least mean squared (VSS-LMS) algorithm for mitigating noise and cyber attacks in ECG signals. The proposed algorithm is obtained considering tuning parameters and errors, where the step size is adaptively updated based on the signal dynamics. The performances of the proposed approach is demonstrated by using MIT-BIH ECG signals database. It shows that the proposed method can properly remove noises from ECG signals.
An Improved Positioning Method in a Smart Building for Visually Impaired Users

Pedestrians have a variety of tools that can assist them in travelling, including maps, kiosks, and signage. However, these facilities are inaccessible to visually impaired users. Moreover, voice aided feature with Global Positioning System (GPS) cannot be adopted in indoor applications due to signal strength attenuation and multipath effects. Internet of Things (IoT) has become a backbone for such navigation applications that can assist in locating a user within IoT equipped smart buildings. Despite the growing use of Wi-Fi and beacon technologies, smartphones are uniquely positioned to be a critical part of a localization solution. The popularity of smartphone is increased by the diverse array of microelectromechanical (MEMS) inertial sensors. This paper discusses the adaptive distance estimation algorithm for visually impaired people. It represents the use of a smartphone in a situation where external proximity sensors fail to share location information to supplement an indoor navigation system in dark areas. An improved fusion algorithm is presented that adapts the walking style of a user detecting right turns and headings. The proposed fusion algorithm depends on inertial sensors to detect the relative position of the moving user with the absolute initial position using ibeacon. Tests were carried out to determine the accuracy of steps travelled, orientation and heading information for a user holding a smartphone. Our approach estimates heading and orientation from 3-axis inertial sensors (gyroscope, accelerometer and magnetometer) have shown accuracy more than 95%. The positioning root-mean-square error (RMSE) calculation results have demonstrated that the hybrid fusion algorithm can achieve a real-time positioning and reduce the error of indoor positioning.