Electronics Research Institute

Introducing IoT systems to healthcare applications has made it possible to remotely monitor patients' information and provide proper diagnostics whenever needed. However, providing high-security features that guarantee the correctness and confidentiality of patients' data is a significant challenge. Any alteration to the data could affect the patients' treatment, leading to human casualties in emergency conditions. Due to the high dimensionality and prominent dynamicity of the data involved in such systems, machine learning has the promise to provide an effective solution when it comes to intrusion detection. However, most of the available healthcare intrusion detection systems either use network flow metrics or patients' biometric data to build their datasets. This paper aims to show that combining both network and biometric metrics as features performs better than using only one of the two types of features. We have built a real-time Enhanced Healthcare Monitoring System (EHMS) testbed that monitors the patients' biometrics and collects network flow metrics. The monitored data is sent to a remote server for further diagnostic and treatment decisions. Man-in-the-middle cyber-attacks have been used, and a dataset of more than 16 thousand records of normal and attack healthcare data has been created. The system then applies different machine learning methods for training and testing the dataset against these attacks. Results prove that the performance has improved by 7% to 25% in some cases, and this shows the robustness of the proposed system in providing proper intrusion detection.

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of this paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize the time series as a benchmark to compare between several neural-network forecasting methods.We compare between four different methods to preprocess the inputs and outputs, including a novel method proposed here based on the discrete Fourier series. We also compare between three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform longer horizon forecast, and that includes ways to partition the problem into the several subproblems of forecasting K steps ahead.

After implementing 5G technology, academia and industry started researching 6th generation wireless network technology (6G). 6G is expected to be implemented around the year 2030. It will offer a significant experience for everyone by enabling hyper-connectivity between people and everything. In addition, it is expected to extend mobile communication possibilities where earlier generations could not have developed. Several potential technologies are predicted to serve as the foundation of 6G networks. These include upcoming and current technologies such as post-quantum cryptography, artificial intelligence (AI), machine learning (ML), enhanced edge computing, molecular communication, THz, visible light communication (VLC), and distributed ledger (DL) technologies such as blockchain. From a security and privacy perspective, these developments need a reconsideration of prior security traditional methods. New novel authentication, encryption, access control, communication, and malicious activity detection must satisfy the higher significant requirements of future networks. In addition, new security approaches are necessary to ensure trustworthiness and privacy. This paper provides insights into the critical problems and difficulties related to the security, privacy, and trust issues of 6G networks. Moreover, the standard technologies and security challenges per each technology are clarified. This paper introduces the 6G security architecture and improvements over the 5G architecture. We also introduce the security issues and challenges of the 6G physical layer. In addition, the AI/ML layers and the proposed security solution in each layer are studied. The paper summarizes the security evolution in legacy mobile networks and concludes with their security problems and the most essential 6G application services and their security requirements. Finally, this paper provides a complete discussion of 6G networks' trustworthiness and solutions.

Abstract Because of the rapid growth of mobile technology, social media has become an essential platform for people to express their views and opinions. Understanding public opinion can help businesses and political institutions make strategic decisions. Considering this, sentiment analysis is critical for understanding the polarity of public opinion. Most social media analysis studies divide sentiment into three categories: positive, negative, and neutral. The proposed model is a machine-learning application of a classification problem trained on three datasets. Recently, the BERT model has demonstrated effectiveness in sentiment analysis. However, the accuracy of sentiment analysis still needs to be improved. We propose four deep learning models based on a combination of BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The study is based on pre-trained word embedding vectors that aid in the model fine-tuning process. The proposed methods are trying to enhance accuracy and check the effect of hybridizing layers of BIGRU and BILSTM on both Bert models (DistilBERT, RoBERTa) for no emoji (text sentiment classifier) and also with emoji cases. The proposed methods were compared to two pre-trained BERT models and seven other models built for the same task using classical machine learning. The proposed architectures with BiGRU layers have the best results.

Fifth-generation (5G) mobile communication technology is now widely available in several countries, with millions of 5G customers. Therefore, it's time for academia and business to focus on the next generation. This paper will overview the sixth-generation (6G) mobile network, including motivations, use case scenarios, requirements, supported research projects, and technologies. We discuss the Beyond 5G (B5G) evolution and advanced 5G features to predict the critical 6G requirements and highlight the 6G capabilities. We also introduce the 6G scenarios, requirements, and technological components compared to 5G. Moreover, the current status of 6G research is discussed, and a rough roadmap for specification and regulation is explored. Then we describe a few prospective applications, their benefits, concepts, and research directions. We explore the business direction for 6G by introducing the most recently 6G projects in the vertical markets. We also propose a network architectural vision and the evolution of hardware-software designs to satisfy the higher requirements of 6G applications. This paper also presents a comprehensive survey of existing 6G trends, technologies, applications, industrial markets, and network structures for the most promising 6G applications.

False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.

This paper gives an overview of the wireless sensor network, studies its application in precision farming, and its importance for improving the agriculture in Egypt. An example for using wireless sensor network in cultivating the potato crop in Egypt is given, and it is shown that the cost of the system with respect to the yearly benefit from exporting potato crop after recovering the loss from its export preventing (this loss is estimated to be 2 billion pounds which is the value of the potato export to Russia annually), after the expected consequence of increasing the yield size and quality, after the expected savings in the resources used in cultivation such as the fertilizer and irrigation water, and after recovering the monetary loss results from the harms caused by excessive use of pesticides, is acceptable, and it can be said that this cost can be recovered in one year. It is concluded that the APTEEN protocol is the most suitable routing strategy to precision farming and its network lifetime can reach 6.5 month which is a period more than the maximum value of the potato crop lifetime that estimated to be 120 day, but it is greater than the yearly cultivation period of potato in Egypt which reaches 6 month.

Blind people need some aid to feel safe while moving. Smart stick comes as a proposed solution to improve the mobility of both blind and visually impaired people. Stick solution use different technologies like ultrasonic, infrared and laser but they still have drawbacks. In this paper we propose, light weight, cheap, user friendly, fast response and low power consumption, smart stick based on infrared technology. A pair of infrared sensors can detect stair-cases and other obstacles presence in the user path, within a range of two meters. The experimental results achieve good accuracy and the stick is able to detect all of obstacles.

Abstract: Defining the project estimated cost, duration and maintenance effort early in the development life cycle is a valuable goal to be achieved for software projects. Many model structures evolved in the literature. These model structures consider modeling software effort as a function of the developed line of code (DLOC). Building such a function helps project managers to accurately allocate the available resources for the project. In this study, we present two new model structures to estimate the effort required for the development of software projects using Genetic Algorithms (GAs). A modified version of the famous COCOMO model provided to explore the effect of the software development adopted methodology in effort computation. The performance of the developed models were tested on NASA software project dataset [1].The developed models were able to provide a good estimation capabilities. Key words: COCOMO model, NASA software, genetic algorithms, genetic programming technique

This paper proposes a hybrid <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm H}^{\infty}$</tex></formula> -based wavelet-neural-network (WNN) position tracking controller as a new robust motion-control system for permanent-magnet synchronous motor (PMSM) servo drives. The combinations of both WNN and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm H}^{\infty}$</tex></formula> controllers would insure the robustness and overcome the uncertainties of the servo drive. The new controller combines the merits of the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm H}^{\infty}$</tex> </formula> control with robust performance and the WNN control (WNNC) which combines the capability of NNs for online learning ability and the capability of wavelet decomposition for identification ability. The online trained WNNC is utilized to predict the uncertain system dynamics to relax the requirement of uncertainty bound in the design of the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm H}^{\infty}$</tex></formula> controller. The WNNC generates an adaptive control signal to attain robust performance regardless of parameter uncertainties (PU) and load disturbances. Systematic methodology for both controllers' design is provided. A computer simulation is developed to demonstrate the effectiveness of the proposed WNN-based <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm H}^{\infty}$</tex> </formula> controller. An experimental system is established to validate the effectiveness of the drive system. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulated and experimental results confirm that the new motion controller grants robust performance and precise dynamic response regardless of load disturbances and PMSM PU.

A dual-band wideband composite patch antenna constructed from a modified circular primary patch and secondary parasitic patch element is presented in this work. A microstrip feed line is employed to inset-feed the primary patch. The secondary patch is fed indirectly through edge coupling with the main patch. The composite antenna is printed on Rogers Ro3003™ with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon _{\mathrm {r}}=3$ </tex-math></inline-formula> and thickness 0.25 mm. The antenna design stages are presented in detail. A MIMO antenna system is constructed from two elements of the proposed composite patch with two different configurations. A prototype is fabricated for the single element and the two-port MIMO antenna configurations. Numerical and experimental results are presented showing good performance regarding impedance matching at the operating bands 28 and 38 GHz, bandwidth, radiation patterns, and gain. The impedance matching bandwidths are 1.23 GHz at 28 GHz and about 1.06 GHz at 38 GHz. The minimum value of the reflection coefficient for 28 GHz 28 GHz band is −34.5 and is −27.3 dB for 38 GHz. The gain of the radiation pattern has a peak of 6.6 dBi at 28 GHz and 5.86 dBi at 38 GHz. For both bands, the radiation patterns are balloon-like omnidirectional. The antenna total dimensions are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.5\times 8.8\times0.25$ </tex-math></inline-formula> excluding the transmission line. The MIMO system with two-ports for both proposed configurations has appropriate values for ECC and the DG which are calculated through electromagnetic simulations.

In this paper, a novel compact single feed quad-band planar inverted F-antenna (PIFA) is presented. Two techniques to reduce the physical size are illustrated. First, we insert U-shaped slits within the antenna-radiating surface. This forces the current to flow around the obstacles hence, reducing the resonant frequency. This technique reduces the size by about 30% from the original PIFA. Second, a capacitive plate is loaded between the radiating surface and the ground plane. The size is reduced more to reach 55% of the original. The relation between the capacitance load value and the antenna size reduction ratio is studied. Using different U-shaped slits at different appropriate positions, multiple (dual, tri, and quad) band capabilities are realized. The four center frequencies are chosen to lie within the GSM band, DCS band, Bluetooth (IEEE802.11a) ISM band, and WLAN (IEEE802.11b) band, respectively. Foam dielectric substrate is used for rigid structure and easy shielding purposes. The reduced size and thin substrate thickness (h<0.15/spl lambda//sub 0/) allow the design to be compatible with wireless and portable communication systems. Experimental measurements verify the design and simulation criteria.

The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.

This letter proposes a novel wideband rectifier circuit for RF energy harvesting. The proposed circuit can collect signals efficiently over broad bandwidth spanning from 0.87 to 2.7 GHz which includes UHF ISM 900 MHz, GSM 900 and 1800 MHz, wireless communication, PCS, and ISM 2.4 GHz. In order to obtain sufficiently large rectifier bandwidth, a matching circuit based on high-pass type L-section for lower band impedance matching and inductive L-section for higher band impedance matching is proposed. The rectifier circuit is constructed using voltage doubler configuration with Schottky diode SMS7630-005LF. The circuit is optimized and refabricated to compensate the undesired parasitic and obtain the required rectifier performance. Two prototypes were simulated, fabricated, and characterized. The rectifier has a measured conversion efficiency exceeding 30% from 870 MHz to 2.5 GHz at 0 dBm input power and a load terminal of 2 kΩ and a dc output voltage equal to 1 V. The circuit sensitivity may reach up to -20 dBm with dc output voltage 40 mV and 8% conversion efficiency. The maximum measured efficiency is 63% from 1.1 to 1.35 GHz.

In this paper, a new formulation for optimizing the design of a photovoltaic (PV)-wind hybrid energy home system, incorporating a storage battery, is developed. This formulation is carried out with the purpose of arriving at a selection of the system economical components that can reliably satisfy the load demand. Genetic algorithm (GA) optimization technique is utilized to satisfy two purposes. The first is to minimize the formulated objective function, which is the total cost of the proposed hybrid system. Whereas, the second is to ensure that the load is served according to certain reliability criteria, by maintaining the loss of power supply probability (LPSP) of the system lower than a certain predetermined value. Two computer programs are designed, using MATLAB code in a two M-files, to simulate the proposed hybrid system and to formulate the optimization problem by computing the coefficients of the objective function and the constraints. Also, these two programs are utilized together with the GA tool under MATLAB software to yield the optimum PV, wind, and battery ratings. The results verified that PV-wind hybrid systems feature lower system cost compared to the cases where either PV-alone or wind-alone systems are used.

AIM: To address the implementation of the National Newborn Screening Program (NBS) in Saudi Arabia and stratify the incidence of the screened disorders. METHODS: A retrospective study conducted between 1 August 2005 and 31 December 2012, total of 775 000 newborns were screened from 139 hospitals distributed among all regions of Saudi Arabia. The NBS Program screens for 16 disorders from a selective list of inborn errors of metabolism (IEM) and endocrine disorders. Heel prick dry blood spot samples were obtained from all newborns for biochemical and immunoassay testing. Recall screening testing was performed for Initial positive results and confirmed by specific biochemical assays. RESULTS: A total of 743 cases were identified giving an overall incidence of 1:1043. Frequently detected disorders nationwide were congenital hypothyroidism and congenital adrenal hyperplasia with an incidence of 1:7175 and 1:7908 correspondingly. The highest incidence among the IEM was propionic acidaemia with an incidence rate of 1:14 000. CONCLUSION: The article highlights the experience of the NBS Program in Saudi Arabia and providing data on specific regional incidences of all the screened disorders included in the programme; and showed that the incidence of these disorders is one of the highest reported so far world-wide.

Characterization of data network monitoring registers allows for reductions in the number of data, which is essential when the information flow is high, and implementation of processes with short response times, such as interchange of control information between devices and anomaly detection is required. The present investigation applied wavelet transforms, so as to characterize the statistic monitoring register of a software-defined network. Its main contribution lies in the obtention of a record that, although reduced, retains detailed, essential information for the correct application of anomaly detectors.

This paper introduces a proposed approach to estimate the optimal parameters of the photovoltaic (PV) modules using in-field outdoor measurements and manufacturers' datasheet as well as employing the nonlinear least-squares fitting algorithm. The main goal is to determine the optimal parameter values of the implemented model which are: series resistance, reverse saturation current, photocurrent, ideality factor and shunt resistance in case of the five parameters model. A Microsoft Excel spreadsheet is developed in order to perform modeling and analysis of the parameters analytical initial values using manufacturer datasheet specifications regarding to the changing in solar irradiance and ambient temperature. Then, the sum of the squared residuals between in-field measured and simulated data are calculated and minimized using Excel solver in order to obtain the optimal values of the parameters simultaneously, to describe the best fit for the outdoor measured data. The proposed approach is used to find the optimal parameters of the PV module TRINA TSM-295 using an array tester. The convergence confidences of the estimated parameters are presented and assessed in an easy way. This approach allows all parameters to be optimized, simultaneously. The results are verified and compared with other research studies for different PV cell technologies. The obtained results are useful for the tested PV module manufacturer and assess the performance of the products in different weather conditions.

This paper presents a study on a stand-alone photovoltaic (PV) system to provide the required electricity for a single residential household in Sinai Peninsula of Egypt.The complete design of the suggested system is carried out, such that the site radiation data and the electrical load data of a typical household in the considered site are taken into account during the design steps.Also, the life cycle cost (LCC) analysis is conducted to assess the economic viability of the system.The results of the study encouraged the use of the PV systems to electrify the remote sites of Egypt.

This paper presents the development of a new complete wearable system for detecting breast tumors based on fully textile antenna-based sensors. The proposed sensor is compact and fully made of textiles so that it fits conformably and comfortably on the breasts with dimensions of 24 × 45 × 0.17 mm3 on a cotton substrate. The proposed antenna sensor is fed with a coplanar waveguide feed for easy integration with other systems. It realizes impedance bandwidth from 1.6 GHz up to 10 GHz at |S11| ≤ −6 dB (VSWR ≤ 3) and from 1.8 to 2.4 GHz and from 4 up to 10 GHz at |S11| ≤ −10 dB (VSWR ≤ 2). The proposed sensor acquires a low specific absorption rate (SAR) of 0.55 W/kg and 0.25 W/kg at 1g and 10 g, respectively, at 25 dBm power level over the operating band. Furthermore, the proposed system utilizes machine-learning algorithms (MLA) to differentiate between malignant tumor and benign breast tissues. Simulation examples have been recorded to verify and validate machine-learning algorithms in detecting tumors at different sizes of 10 mm and 20 mm, respectively. The classification accuracy reached 100% on the tested dataset when considering |S21| parameter features. The proposed system is vision as a “Smart Bra” that is capable of providing an easy interface for women who require continuous breast monitoring in the comfort of their homes.