Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient ToA Estimation Technique Based on Phase Correction for 5G mMTC system

In 5G mMTC system, massive user equipment (UE) access network simultaneously by contending a common RACH resource. It is thus of significant importance for network to estimate the time-of-arrival (ToA) of each UE efficiently based on preamble allocated for Physical Random Access Channel(PRACH) so that UE can achieve uplink synchronization with network reliably. In this paper, we focus on improving the accuracy and performance of ToA estimation. Considering the phase ambiguity problem due to varied propagation delay defined as ToA during detection of preamble signals generated by UE, we propose a ToA estimation technique to compensate phase transition with a set of pre-determined phase corrector vectors(PCV) associated with all possible ToA values. Furthermore, we optimize PCVs by build a reduced and essential PCV candidate as a smaller search space for more efficient phase recover and ToA estimation. Simulation results show that the performance of proposed method and its variants can fulfill the requirements of 3GPP standard with lower computational complexity.
Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Money laundering has been a global issue for decades, which is one of the major threat for economy and society. Government, regulatory and financial institutions are combating it together in their respective capacity, however still billions of dollars in fines by authorities make the headlines in the news. High-speed internet services have enabled financial institutions to deliver better customer experience through multi-channel engagements, which has led to exponential growth in transactions and new avenues for laundering the money for fraudsters. Literature shows the usage of statistical methods, data mining and Machine Learning (ML) techniques for money laundering detection, but limited research on Deep Learning (DL) techniques, primarily due to lack of model interpretability and explainability of the decisions made. Several studies are conducted on application of ML for Anti-Money Laundering (AML), and Explainable Artificial Intelligence (XAI) techniques in general, but lacks the study on usage of DL techniques together with XAI. This paper aims to review the current state-of-the-art literature on DL together with XAI for identifying suspicious money laundering transactions and identify future research areas. Key findings of the review are, researchers have preferred variants of Convolutional Neural Networks, and AutoEncoder; graph deep learning together with natural language processing is emerging as an important technology for AML; XAI use is not seen in AML domain; 51% ML methods used in AML are non-interpretable, 58% studies used sample of old real data; key challenges for researchers are access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced. Future research directions are, application of XAI techniques to bring-out explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between research community and industry to benefit from domain knowledge and controlled access to data.
GaN 2.0: Power FinFETs, Complementary Gate Drivers and Low-Cost Vertical Devices

This paper summarizes recent progress on the development of high frequency power switches based on Gallium Nitride (GaN). Both lateral and vertical device structures will be discussed, as well as a new all-GaN complementary gate driver technology which could increase the operating frequency of GaN power circuits beyond 10 MHz. The paper concludes with a discussion on future technologies that could improve the device performance even further, such as superjunction structures.
Design and Implementation of Wireless Power Transfer for Wireless Heart Rate Monitoring System

Ultrasonic Power Transfer (UPT) is a technology for wireless power transfer that can be used for medical uses with low power. Batteries are used as power sources for most wearable or portable medical systems. The battery of these systems cannot operate for a long period and need to be charged or replaced from time to time. UPT is a candidate technology that can be adopted to solve this problem. In this paper, UPT operates at a 40-kHz ultrasound transducer was employed for providing the power to low power heart rate sensor. The proposed system includes a heart rate monitor (HRM) and power units. An ultrasonic receiver and transmitter, bridge rectifier, supercapacitors, and converter are the main components of the power unit. While the HRM contains measurement and monitoring parts. The nRF24L01 wireless technology, a microcontroller-based Arduino Nano, and a heart rate sensor are the main components of the measuring circuit. The monitoring part involves nRF24L01 wireless technology, a microcontroller-based Arduino Mega 2560, and a laptop. The transfer power and efficiency achieved by UPT with a 4-Farad supercapacitor were 55.1% and 0.06318 mW at 6 cm, respectively. The heart rate measurement accuracy was 97% as compared with a consumer-ready device. These findings show that the proposed system is stronger than previous studies at 6 cm in terms of transfer power efficiency and transfer power.
Wireless Power Transfer based on Spiral–Spider Coils for a Wireless Heart Rate Sensor

Wireless power transfer (WPT)–based magnetic resonance coupling (WPT-MRC) is an encouraging technique for medium-power medical devices and other similar applications. Most wearable medical devices are operated based on several batteries. Generally, the batteries of such devices cannot be used for an extended time and typically involve a replacing or recharging process. WPT-MRC is a candidate technology that can resolve the charging of such batteries or it can directly supply power to the medical device. This paper aims to design and implement WPT based on the spiral–spider coils (WPT-S/S) for transferring high power over different air gaps to address the power problem. A heart rate measurement unit (HRMU) was implemented practically to confirm the functionality of the presented WPT-S/S. The proposed system comprises three main parts: monitoring, measurement, and power. The power unit contains receiver/transmitter coils, a capacitor resonator, and a rectifier. The measurement part contains an Arduino Nano platform (microcontroller), an nRF24L01 wireless technology for transmitting the HR measurement to the monitoring unit, and the biomedical sensor (HR sensor). The WPT-S/S system achieved 87% transfer power efficiency and 10 W DC output power over a 5-cm distance between the source coil (transmitter) and destination coil (receiver) when a resistance load of 200 Ω was employed. Also, the spiral–spider configuration succeeded in achieving adequate DC output voltage (i.e., 5 V) for supplying the HRMU at a distance of 20 cm between the coils.
On the Communication Complexity of AND Functions

Log-rank conjecture is one of challenging problems in communication complexity. It is known to hold for some special function classes such as XOR functions <inline-formula> <tex-math notation="LaTeX">$\text {f}(\text {x}\oplus \text {y})$ </tex-math></inline-formula> when the outer function f is monotone, symmetric, <inline-formula> <tex-math notation="LaTeX">$\mathsf {AC}^{0}$ </tex-math></inline-formula>, low <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{2}$ </tex-math></inline-formula>-degree, linear threshold or read-k. However, for AND functions <inline-formula> <tex-math notation="LaTeX">$\text {f}(\text {x}\wedge \text {y})$ </tex-math></inline-formula>, Log-rank conjecture is only known to hold when the outer function is a monotone function or a function with small alternating numbers. In this paper, we show that Log-rank conjecture holds for AND functions whose outer functions are read-once <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{2}$ </tex-math></inline-formula>-degree polynomials, constant- <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{2}$ </tex-math></inline-formula>-degree polynomials, and symmetric functions. For read-once and symmetric AND functions, their proof are based on the elementary analysis of Möbius sparsity of the outer functions and designing efficient protocols for computing the corresponding AND functions in terms of the Möbius sparsity of their outer functions. For a constant- <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{2}$ </tex-math></inline-formula>-degree AND function <inline-formula> <tex-math notation="LaTeX">$\text {f}(\text {x}\wedge \text {y})$ </tex-math></inline-formula>, we use the concept of the variable rank to design a decision tree for computing f where the tree depth is bounded above by a polynomial in the logarithm of the Möbius sparsity of f. The communication protocol can be obtained easily from the constructed decision tree. Our results deepen the understanding of the log-rank conjecture for AND functions.
Detection of Weak Small Image Target Based on Brain-Computer Interface

The paper proposes an innovative frame for enhanced detection of targets in images based on EEG. In this study, we have achieved the process of long-term EEG signal in single trial. We have designed the experimental paradigms and performed the experiment on 12 subjects. We analyzed the EEG signals by extracting dynamic features. We determined the target detection according to single-trial EEG, simultaneously the target position by eye movement signals. The results proved that the method we adopted is significantly useful and reached satisfactory accuracy. Furthermore, the research can be used as a supplementary means for target detection of machine learning. By the fusion of human experience and intelligence, this work breaks through some bottlenecks of traditional target detection, such as the detection of uncertain targets, small targets, severely distorted targets and incomplete targets. It can significantly improve the detection rate of image information and compatible migration capability.
The World of Communication & Computing Platform In Research Perspective: Opportunities and Challenges

Computing paradigms are introduced for solving complex problems by analyzing, designing and implementing by complex systems. Computing can be defined as the effective use of computer or computer technology to solve tasks that are goal oriented. Computing is used in development of producing scientific studies, building intelligent systems, channeling different media for communication. Over the last few years, internet became so popular which lead to the increase in computer processing capacity, data storage and communication with one another. Computing has evolved from one technology to another in its field and formed a robust framework over the years. In this paper a survey on different computing paradigms like evergreen computing is cloud computing, to deal with basic scheduling is grid computing, for multi task handing is parallel computing, to handle smart phone data’s that is mobile computing, cluster computing, and distributed computing is carried out. These technologies improved the way computing functions and made it easier to the computer world. The applications and research issues of the most of the computing paradigms are discussed in this article. The recent research issues in computing platform are scheduling and security. The scheduling is dealing with data processing from one computing platform to other computing device. Security is one of the important research issues.
Interference Analysis in Cognitive Hybrid FSO/RF System

This paper presents novel closed form expressions for the outage probability and the average bit error for a Cognitive Hybrid FSO/RF system operating in an interference limited environment. Fixed primary RF occupancy and inter-weave cognitive radio principles are employed. The interference arises due to miss detection of the presence of primary RF. Innate optical channel conditions of turbulence and pointing error are considered in this work. The results obtained presents the impact of optical, RF thresholds, interference power and primary channel occupancy on the system performance. The presented analysis showcases the utility of a Cognitive Hybrid FSO/RF link even under interference.
Pedestrian Indoor Localization and Tracking Using Hybrid Wi-Fi/PDR for iPhones

This paper provides a hybrid approach between Wi-Fi and pedestrian dead reckoning (PDR) for the iPhones in indoor environments, and addresses the following two problems. First, since Apple Inc. no longer provides public information about currently-connected Wi-Fi (e.g., service set identifier, received signal strength and channel), Wi-Fi based pedestrian tracking apps that run on the iPhones are more restricted compared to other ones on the Android platforms. Second, even the PDR approach provides such a great way for self-localization, it suffers from accumulated errors of inertial sensors embedded in the smartphones. We propose a conversion function from a Wi-Fi status value to a proximity for the localization purpose. Then, a mobile iPhone collects mobility information from inertial measurements unit (IMU), inputs to the PDR, and combines with Wi-Fi proximity in order to perform accurate self-localization and tracking. Moreover, we improve the PDR by reducing drifting effects caused by the IMU biases. Experiment results show that the proposed scheme is effective and has low complexity, while bringing the benefits from smartphone IMU.
Fundamental Limits of Wireless Caching Under Mixed Cacheable and Uncacheable Traffic

We consider cache-aided wireless communication scenarios where each user requests both a file from an a-priori generated cacheable library (referred to as ‘content’), and an uncacheable ‘non-content’ message generated at the start of the wireless transmission session. This scenario is easily found in real-world wireless networks, where the two types of traffic coexist and share limited radio resources. We focus on single-transmitter, single-antenna wireless networks with cache-aided receivers, where the wireless channel is modelled by a degraded Gaussian broadcast channel (GBC). For this setting, we study the delay-rate trade-off, which characterizes the content delivery time and non-content communication rates that can be achieved simultaneously. We propose a scheme based on the separation principle, which isolates the coded caching and multicasting problem from the physical layer transmission problem. We show that this separation-based scheme is sufficient for achieving an information-theoretically order-optimal performance, up to a multiplicative factor of 2.01 for the content delivery time, when working in the generalized degrees of freedom (GDoF) limit. We further show that the achievable performance is near-optimal after relaxing the GDoF limit, up to an additional additive factor of 2 bits per dimension for the non-content rates. A key insight emerging from our scheme is that in some scenarios considerable amounts of non-content traffic can be communicated while maintaining the minimum content delivery time, achieved in the absence of non-content messages; compliments of ‘topological holes’ arising from asymmetries in wireless channel gains.
On the Performance of Co-existence between Public eMBB and Non-public URLLC Networks

To ensure the high level of automation required in today’s industrial applications, next-generation wireless networks must enable real-time control and automation of dynamic processes with the requirements of extreme low-latency and ultra-reliable communications. In this paper, we provide a performance assessment for the co-existence of a public enhanced mobile broadband (eMBB) and a local non-public factory (URLLC) network and evaluate the network conditions under which the stringent latency and reliability requirements of factory automation applications are met. The evaluations consider both an unsynchronized and a synchronized time division duplexing (TDD) deployment between the networks, as well as scenarios both with and without any macro eMBB traffic located inside the factory. The results show that an unsynchronized deployment is possible if the isolation between the networks is sufficiently high, either as a result of a separation distance, wall loss or the use of separate frequencies for the networks. A synchronized deployment will avoid the cross-link interference, but it will not resolve the problems related to the closed access and the near-far interference. If the factory contains eMBB traffic served by the overlaid macro cells, the performance of both networks will suffer due to a high level of cross-link and near-far interference. The problems related to the cross-link interference can be resolved by synchronizing the networks, while the level of the near-far interference can be reduced by allowing the eMBB users to be connected to base stations located inside the factory. Finally, if an unsynchronized deployment is desired, the factory should be deployed on an isolated frequency.
A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques

Stress is an escalated psycho-physiological state of the human body emerging in response to a challenging event or a demanding condition. Environmental factors that trigger stress are called stressors. In case of prolonged exposure to multiple stressors impacting simultaneously, a person’s mental and physical health can be adversely affected which can further lead to chronic health issues. To prevent stress-related issues, it is necessary to detect them in the nascent stages which are possible only by continuous monitoring of stress. Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring. In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques. This paper investigates the stress detection approaches adopted in accordance with the sensory devices such as wearable sensors, Electrocardiogram (ECG), Electroencephalography (EEG), and Photoplethysmography (PPG), and also depending on various environments like during driving, studying, and working. The stressors, techniques, results, advantages, limitations, and issues for each study are highlighted and expected to provide a path for future research studies. Also, a multimodal stress detection system using a wearable sensor-based deep learning technique has been proposed at the end.
Adaptive Integrated Coordinated Control Strategy Based on Sliding Mode Control for MMC-MTDC

Traditional double closed-loop PI control and DC voltage-active power droop control are suitable for the modular multilevel converter based Multi terminal flexible DC transmission system (MMC-MTDC). However, due to the strong nonlinearity of the system and the fixed droop coefficient, the system has poor stability under transient conditions, which is prone to full load. In order to improve the response speed and stability of the system, an adaptive integrated coordinated control strategy based on sliding mode theory is proposed. The strategy adopts a double closed loop cascade control structure, the outer-loop controller performs adaptive droop control based on the comparison of the MMC real-time power margin with the power reference command, and the inner-loop controller is designed based on a sliding film variable structure with current error as the sliding film plane. The simulation model of MMCMTDC direct current transmission system was built in PSCAD/EMTDC. The simulation results of the proposed control strategy show that the control strategy can effectively avoid the full load margin of the system when the MMC is small, and the strategy also can improve the adaptability and dynamic performance of the system compared with the traditional control strategy.
Photonic Generation and Application of a Bandwidth Multiplied Linearly Chirped Signal With Phase Modulation Capability

Photonic generation of a bandwidth multiplied linearly chirped signal with phase modulation capability is proposed and its applications in communication and radar systems are demonstrated. In the generator, a dual-polarization quadrature phase shift keying modulator is used to simultaneously perform bandwidth multiplication and phase modulation. The generator can be functionally expanded to a transceiver for wireless communication, which can be used to perform optical generation and coherent demodulation of a phase modulated linearly chirped signal. Meanwhile, the generator can be used to increase the radar detection distance by improving the signal time bandwidth product. The signal generation and application are verified by simulation. A linearly chirped signal with an instantaneous frequency of 8–12 GHz or 16–24 GHz is obtained by using a 4–6 GHz electrical driving signal. The proposed transceiver is demonstrated to perform 156.25-bit/s wireless communication. When the emitting power is 11 dBm and the communication distance is 1 km, the bit error rate can be better than 10−4. While for radar detection, the peak to sidelobe ratio of the compressed pulse is increased from 1.1 dB to 9.2 dB, through which the distance improvement can be verified.
CCJRF-ICN: A Novel Mechanism for Coadjuvant Caching Joint Request Forwarding in Information Centric Networks

Information centric networking (ICN) shifts the focus of existing internet architecture from host-oriented to content-oriented model by enabling in-network caching and content-based forwarding. These ICN features help to increase network performance by decreasing content discovery delay, content server load, and network congestion. To route a content interest inside network such that content can be fetched with minimal time is a challenging task in ICN. The performance of the ICN routing protocol can be significantly improved if the decisions related to content chunk placement and request forwarding are taken in a cooperative fashion. This paper describes a novel strategy for co-operative caching joint request forwarding in ICN, focusing on decreasing content retrieval latency. To do so, the caching strategy leverages the concept of connected dominating set (CDS) for creating a virtual backbone network to eliminate caching redundancy and reduce content discovery delay. It considers content chunk placement and request forwarding tasks as strongly co-related procedures. It exploits the caching information so that the request is forwarded to a content router (CR) with the maximum likelihood of carrying needed data using the betweenness centrality (BC) of CR. The proposed approach also uses the Markov chain-based model to estimate CS hit likelihood and use it as decisional parameter while forwarding the interest packet. This mechanism helps to fetch the content within the shortest possible time duration. The CCJRF-ICN adopts the Dijkstra’s shortest path routing and works in collaboration with the CDS-driven caching joint forwarding mechanism. The simulation study of CCJRF-ICN is done inside ns-3 based ndnSIM-2.0 simulator with performance measures like content store (CS) hit ratio, content discovery delay, mean hop distance, network load, and network overhead. The simulation outcomes demonstrate that CCJRF-ICN outperforms the state-of-the-art strategies for realistic topologies (GEANT, US-26, Euro-28) and shows improvement up to 5-35% against stated performance measures.