Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT and BLE Beacons: Demand, Challenges, Requirements, and Research Opportunities- Planning-Strategy

The Internet of Things (IoT) has various technologies that improve the functionalities of IoT devices to make them capable to connect and communicate with each other. The IoT with various advanced technologies and devices performing a rapid transformation of current society towards easier and smarter. As the use of IoT increases, the new challenges, requirements, and research/ innovation need have arisen to fulfill the demands. Wireless devices with low power and high data security are the most viable demand for diverse IoT applications. In an IoT environment, various nodes/ sensors are used to collect the data and different wireless communication technologies like Infrared, Bluetooth, Li-Fi, WI-Fi, Zigbee, etc. are used to transceive the data. As per the survey, among all other available wireless technologies, Bluetooth Low Energy (BLE) beacons have proved as most promising short-range wireless communication technology due to the fact of appearing everywhere or of being very common of Bluetooth-compatible technology. However, for better performance and efficiency of BLE beacons in the IoT ecosystem, regular research has needed to ensure seamless integration. For such research here we have combined the literature of IoT with Beacons. This paper consolidates: an introduction of IoT with its current market demand, challenges and five essential requirements observed. We have also discussed the research planning and strategy to discover the solutions. It also consolidates an introduction of BLE Beacon with its market demand, a survey of existing/ proposed solutions are reviewed to identify the current problem statements for research opportunities in the future.
Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.
State of the art of Mobile Banking Services and Future Prospects in Developing Countries

Mobile banking is a service that permits people to transfer money by using their smartphones. Generally, a bank or some financial institution rendered this service to their customer. Recently, there is a radical change in the ICT sector in Bangladesh. Therefore, people can easily do online banking tasks by staying at their homes by using their mobile phones. Most people use mobile banking for paying utility bill and transferring funds between accounts. However, due to poor banking infrastructure, people had to face many problems in mobile banking. For this purpose, our study was to find those problems or barriers and future prospects of mobile banking services in Bangladesh. We have discussed the perceived risk of m-banking as well as how trust and compatibility influence m-banking.If we can fix those issues then more people will be engaged in this service.
Security Awareness among Smart Speaker Users

High-speed internet together with advanced manufacturing processes have brought about the “Internet of Things” - everyday equipment which have now become part of a larger ecosystem, communicating with one another via the internet, to provide a unique experience to the end user. ‘Smart’ speakers, lighting systems and even fitness monitors coupled with smartphones offering fast processing and internet communication speeds, now allow individuals to virtually control all aspects of their personal and official lives, at the convenience of their fingertips. However, as with all technological innovations, the Internet of Things too is vulnerable to security breaches. These devices have access to vast amounts of data, some of which may be unknown to the end users themselves. Recent findings have shown that Smart Speakers could be ‘listening’ to user conversations even when they are disabled. A confidentiality breach of user conversations could not only affect an individual's personal life, but also jeopardise their work lives, extending up to even compromises in national security. This study focuses on the level of information security awareness amongst users of Smart Speakers. Smart Speakers have been increasing in use over the past several years, aided by the simplicity of configuration and the wide range of possibilities. This research also attempts to identify if users exercise the same level of security and caution given to internet banking accounts, with their Smart Speaker.
Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Surface electromyography(sEMG) measurement has been an essential approach to analyze human behaviors because we can generally consider that sEMG signals represent the muscle activities as the final output of our nerve system. One of the most serious problems for considering sEMG signal as the muscle activity is the shift of the relative position between muscles and skin depending on a posture. The motion of forearm rotation is the prominent example of muscle-skin shifting depending on postural changes. The sEMG signal from a sensor may represent the different muscle activity when the muscle-skin shifting is happened. In this study, we discuss a method to quantify the muscle-skin shift from the sEMG signals in response to the postural changes. We use the high density sEMG sensor that is possible to measure sEMG signal as the potential map. We proposed the computation algorithm to quantify the amount of muscle-skin shifting based on the change of the sEMG signals in response to the postural changes. We conducted the experiments of wrist extension motions under three different forearm postures: forearm pronation, natural posture and forearm supination. Experimental results from three healthy subjects show that we can quantify the extent of muscle-skin shifting as an angle by using proposed algorithm.
A Turbo Maximum-a-Posteriori Equalizer for Faster-than-Nyquist Applications

Faster-than-Nyquist (FTN) transmission employs non-orthogonal signaling to improve spectral efficiency over conventional orthogonal transmission at the Nyquist rate. However, FTN signaling also introduces inter-symbol interference (ISI), which must be mitigated through additional signal processing.In this paper, we present a scalable FPGA-based architecture for a turbo maximum-a-posteriori (MAP) equalizer based on the Bahl-Cock-Jelinek-Raviv (BCJR) algorithm to mitigate the ISI. In contrast to many existing hardware implementations of BCJR, where the algorithm performs turbo decoding over a binary alphabet for an already-equalized channel, our FPGA design applies BCJR to non-binary signal constellations and for the task of equalization. To the best of our knowledge, this is the first published FPGA-based BCJR equalizer implementation suitable for FTN applications, where a binary forward-error-correction decoder is employed in tandem with the equalizer.Through careful tradeoff selection and optimization of the design space, our implementation achieves a maximum per-PE throughput of 602 Mbps, and a total of 15.4 Gbps within the constraints of a Xilinx UltraScale+(xcvu13p) device.
On the Time-Delay Estimation Performance Limit of New GNSS Acquisition Codes

In previous works, new families of Pseudo-Random Noise (PRN) codes of length 1023 chips were proposed in order to ease the acquisition engine. These studies analyzed several metrics for code design in order to improve the acquisition but no analysis was conducted on the estimation performance, which in turn drives the final position, velocity and timing estimates. The main goal of this contribution is to assess if these new PRN codes designed to improve the acquisition engine lose in achievable time-delay estimation performance with respect to the standard GPS L1 C/A Gold codes. The analysis is performed by resorting to a new compact closed-form Cramér-Rao bound expression for time-delay estimation which only depends on the signal samples. In addition, the corresponding time-delay maximum likelihood estimate is also provided to assess the minimum signal-to-noise ratio that allows to be in optimal receiver operation. GNSS, time-delay estimation, band-limited signals, Cramér-Rao bound, signal acquistion.
Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver’s behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.
Y-Shaped Antenna for 5G Enabled Gadgets and its MIMO for Smartphone Applications

In this paper a very small Y-shaped micro strip antenna is proposed for applications in 5G communication. The proposed designed has the advantages of reduced size and fabrication cost as well as aids comparatively easier integration with future 5G enabled gadgets. The antenna is 30mm X 20mm X 1.67mm in dimension with Y-shaped feed on the upper surface and a defected ground plane (DGP) on the bottom. The proposed antenna geometry offers an operating bandwidth of 15.2% (i.e. 3.28- 3.82GHz) and a VSWR below 2. Also, a double-Y shaped multiple-input-multiple-output (MIMO) antenna for smartphone applications is introduced. The S-parameters (S11 and S22) are found to be below -10dB in 3.33-3.73GHz range. The antenna provides high gain, good radiation pattern and Voltage Standing Wave Ratio (VSWR) below 2. Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC) and Directivity Gain (DG) are found to be below 0.01, below-6dB and above 9.99 respectively.
Safely Preventing Unbounded Delays During Bus Transactions in FPGA-based SoC

Advanced eXtensible Interface (AXI) is an open-standard communication bus interface implemented in most commercial off-the-shelf FPGA System-on-Chips (SoC) to exchange data within the chip. Unfortunately, the AXI standard does not mandate any mechanism to detect possible misbehavior of the connected modules. This work shows that this lack of specification has a relevant impact on popular implementations of the AXI bus. In particular, it is shown how it is easily possible to inject arbitrarily-long delays on modern FPGA system-on-chips under the presence of misbehaving bus masters. To safely solve this issue, this paper presents a general timing analysis to bound the execution of periodically-invoked hardware accelerators in nominal conditions. This timing analysis is then used to conFigure a latency-free hardware module named AXI Stall Monitor (ASM), also proposed in this paper, capable of detecting and safely solving possible stalls during AXI bus transactions. The ASM leaves a quantified flexibility to the hardware accelerators when deviating from nominal conditions. The contribution is finally supported by a set of experiments on the Zynq-7000 and Zynq Ultrascale+SoCs by Xilinx.
Synthesis of Micro-Doppler Signatures for Abnormal Gait using Multi-branch Discriminator with Embedded Kinematics

A key limiting factor in the depth, hence accuracy of deep neural networks (DNNs) designed for radar applications, is the meager amount of data typically available for training. Generative adversarial networks (GANs) have been proposed in many fields for the generation of synthetic data. It was shown, however, that when applied to micro-Doppler signature simulation, GANs suffer from performance degradation due to the generation of kinematically impossible samples. In this work, kinematic analysis of the micro-Doppler signature envelope is integrated as an additional branch in the discriminator network of a GAN to improve the kinematic fidelity of synthetic data when simulating abnormal gait signatures. Results show that the proposed multi-branch GAN network results in greater overlap in the feature space of synthetic abnormal gait samples with that of measured signatures for abnormal gait.
Target Sidelobes Removal via Sparse Recovery in the Subband Domain of an OFDM RadCom System

In this paper, the problem of target masking induced by sidelobes arising in an OFDM RadCom System is considered. To fully exploit the waveform structure and address practical scenarios, we propose to deal with the sidelobes in the subband domain via sparse recovery. Accordingly, we design a sparsifying dictionary modeling at the same time the target's peak and pedestal (i.e., random sidelobes). Results on synthetic data show that our approach allows one to remove not only the target pedestal but also range ambiguities arising when all subbands are not active.
Interdigital Structure Enhanced the Current Spreading and Light Output Power of GaN-Based Light Emitting Diodes

In this manuscript, blue light emitting diodes (LEDs) with comb-shaped mesa and interdigital electrodes structure based on gallium nitride (GaN) are presented. This kind of LEDs have showed higher light output power and more efficient current spreading, comparing with a reference large-area LED. For the optimized LED structure, the light output power and external quantum efficiency, considering the actual emission area, reach to 600 mW and 73% at a forward current of 350 mA. The experimental results are consistent with the predictions of current expansion equations. The comb-shaped mesa structure decreases the total internal reflection (TIR) and the interdigital electrodes improve the current crowding effect, as validated from the electrical and optical tests. Therefore, the efficiency and reliability of the LED devices have been significantly enhanced by reducing the transverse current path. This work should be of guiding significance for structural design of large-area/high-power optoelectronic devices.
Hardware and Processing Architecture Impacts on Adaptive Beamforming in Digital Phased Arrays

Typical approaches to receive digital beamforming (DBF) have used antenna array architectures wherein large numbers of elements are combined via fixed radio frequency (RF) manifolds into a small number of digital channels. These channels can then be used to adaptively cancel interference within limits pertaining to the number of digital channels relative to the number of interference sources and hardware considerations such as channel-pair cancellation ratio (CPCR). Emerging technologies promise new flexibility through array architectures that can potentially be digitized at or nearly at the element level. This enables the adaptation of the processing architecture to specific modes or functions and varying RF interference environments and the use of larger numbers of spatial degrees of freedom (DoFs) than has previously been practical. In this paper, we show the development of an ideal (ensemble average) covariance model for adaptive DBF that incorporates the effects of dispersion and channel mismatch and consider the impact of CPCR on ADBF performance in the context of channel and beamspace processing architectures.
UAV Micro-Doppler Signature Analysis Using DVB-S Based Passive Radar

Drones and unmanned aerial vehicles (UAVs) are increasingly popular, thus posing danger and threats to infrastructures and public safety. A technology for drone detection and classification would therefore significantly increase the level of security. In scenarios such as concerts, sport events, trade fairs, or in any situation where significant aggregation of people is present, such techniques should be non-invasive. That means they do not have to pose an additional threat to people themselves. To this end, passive radars offer an appealing solution, since they are able to offer a non-cooperative surveillance while not emitting any electromagnetic signal. On the contrary, they rely on existing transmitting infrastructure (also referred to as illuminators of opportunity, IoO), such as broadcasting signal sources (FM radio, terrestrial and satellite digital video broadcasting, cellular communication and so on). In this work, the possibility to exploit satellite television based passive radar for UAV detection is analyzed by experimental validation. In addition, micro-Doppler signatures for drones have been extracted, which might give information for subsequent UAV classification.
Global Exponential Stability of Memristive Complex-valued Neural Networks with Mixed Time Delays and Impulsive Effects

In this paper, based on inequality techniques and Lyapunov's second method, Global exponential stability (GES) problem of memristive complex-valued neural networks (MCVNNs) is discussed. The MCVNNs may be unstable due to time delay and impulsive effects. Therefore, we considered the influence of these two factors on the MCVNNs. For obtained the desire results we divide MCVNNs into two real-valued parts. Some new conditions are acquired to ensure the MCVNNs is stable. At the last, the simulations of real and imaginary parts are afforded to show the validity of the results by an example.
A New Multi-Link Time-Reversal Prefilter Design for Indoor Wireless Communications

A prefilter based on the time-reversal (TR) is proposed to control the multi-link interference (MLI) and the inter-symbol interference (ISI) for multi-link system. At first, we define a new parameter signal-to-sidelobe-plus-leakage ratio (SSLR) to represent the performance of the multi-link system. To improve the bit error rate (BER) performance, the proposed prefilter minimizes the MLI and the ISI and maximizes the peak power of received signal at the same time by maximization of the SSLR. We set an optimization problem to maximize the SSLR which is a quadratically constrained fractional quadratic problem. To solve the optimization problem, we use the epigraph form and the bisection method. Therefore, we find an $\varepsilon $ -optimal solution which is a global optimal solution. Simulation results show that link interference power is reduced by the MLI-TR prefilter and the SSLR of the MLI-TR prefilter is higher than that of the conventional TR prefilter. Moreover, the BER performance of the MLI-TR prefilter is improved in overall signal-to-noise ratio range.
Adaptive Impedance Control of Robots With Reference Trajectory Learning

In this paper, we propose an adaptive impedance control with reference trajectory learning for the robots interacting with unknown environment. A cost function considering its tracking errors and interaction force is introduced and a reference trajectory learning law based on iterative learning is presented to minimize it. Also, an adaptive impedance control is designed to follow the target impedance model with the adaptive reference trajectory to implement the convergence of tracking errors and interaction force. Through simulation and experimental studies, we find that the robot can autonomously adjust its trajectory or interaction mode when the environment types or cost function parameters are tuned, so that a more humanized and intelligent interaction can be realized.
CNC Machine Tool Wear Monitoring Based on Densely Connected Convolutional Networks

As an important part of machining, the condition of cutting tools will have a huge impact on the processing cost, efficiency and product quality, and the intelligent monitoring for tool becomes increasingly important. This paper proposes a tool wear monitoring model based on 2σ-RMS Method and Densenet (Densely Connected Convolutional Networks). The time domain signal collected during tool processing is used to divide the tool condition utilizing 2σ-RMS method. The Densenet is trained in two stages to extract features information to build a mapping relationship between features and tool wear. The method of secondary sampling and multiple estimation for averaging reduces the interference of abnormal values on the estimated results and realized estimation of total wear of the tool after each stroke. Comparing with other monitoring methods, the monitoring model proposed in this paper can more effectively characterize the wear of milling cutters, and greatly improves the accuracy of estimation.