Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Modal Cross Learning for Improved People Counting using Short-Range FMCW Radar

Radar systems enable remote-less sensing of multiple persons in its field of view. In this paper, we propose a novel people counting system using 60-GHz frequency modulated continuous wave radar sensor. The proposed deep convolutional neural network learns from supervised radar data and also through knowledge distillation via multi-modal cross-learning of representation from a synchronized camera-based deep convolutional neural network. To overcome several shortcomings of the radar data, novel multi-modal cross learning algorithm is proposed that leverage the high-level abstractions learnt from camera modality. We also propose novel focal-regularized loss function to facilitate improved feature learning. We demonstrate the superior performance of our proposed solution in counting upto 4 people and detection of more than 4 people in indoor environment in comparison to the state-of-art radar-based uni-modal learning.
Double Closed-Loop Control Strategy for Electric Springs Based on PI Controller

Electric Springs (ESs) can effectively solve the problem of load side voltage instability caused by the voltage fluctuation on the grid side. Although the single closed-loop control strategy based on the root mean square (RMS) value of the voltage at the point of common coupling (PCC) can maintain the PCC voltage stability, the harmonics on the load side and the ES output voltage will be distorted largely. In this paper, a double closed-loop control strategy with an outer voltage loop and an inner current loop is proposed to address the problem mentioned above. The simulation results show that the improved double closed-loop control strategy can effectively suppress the harmonics on the PCC voltage as well as the ES output voltage.
A Survey on IOT enabled cloud platforms

IOT is a mainstream technology of today’s technology world and has transformed recently with a significant potential for modernizing the lifestyle of modern societies today. Since the term IOT was devised in the year 1999 by Kevin Ashton there is a tremendous increase in the countable devices bridged to the internet in the last few decades. This paper provides a literature survey on various cloud services used in integration with IOT and a study about fog computing used in various application areas. Due to seamless data exchange between IOT devices and sensors, there was a need of platforms for data management, storage, and analysis. Thus cloud computing and fog computing are often used interchangeably for providing high storage capacities and processing capabilities. Different types of IOT based cloud platforms are discussed in this paper depending on their applicability along with their pros and cons precisely.
ANFIS BASED Intelligent Energy Management and optimization of CHPB System with Multiobjective function

A combined system of heating, power and biogas (CHPB) system has been developed and tested in a single building in MinQin County, Gansu Province, China. The CHPB system shows favorable performances of heating in most winter days. However, when bad weather lasts over two days, the system would not meet the heating. Therefore, for the sake of reduce the operation cost and the environmental impact of the CHPB system considering its preoperational variable is the future availability of renewable energy, intelligent energy management of the CHPB system is proposed in this paper. The factors, such as the ambient temperature, humidity, power availability, and wind speed, are predicted by adaptive Neuro-fuzzy inference system (ANFIS) and then are combined with the instantaneous value of the backup batteries state of charge (SOC) and a gas tank level to make a decision whether to use the gas or electricity stored in the backup batteries to satisfy the user demand by fuzzy system. The cost saving of the proposed multiobjective intelligent energy management (MIEM) approach compared to operation based on opportunity was equal to 9.24% compared to 12.9% for the next day as a result of the intelligent switch scheduling, and lS.49 % compared to 26.5 for CO2 emission.
Design of Constant Modulus Sequences with Doppler Shift Tolerance and Good Complete Second Order Statistics

The design of phase coded waveforms with a complete second order characterization, resilient to Doppler shifts in a specified region, is here addressed. A cost function that imposes good second order statistics over a window of interest for different Doppler shifts is used to determine the phases of the sequence that minimizes it. Due to the design criteria imposed, the resultant sequences minimize the sidelobe levels of the Ambiguity Function (AF) as well as those of an associated function hereby referred to as the Complementary Ambiguity Function (CAF). It is shown by numerical examples that the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method can be used to determine the phases of the discrete time sequence that fulfills the design restrictions.
Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian. Compared with spatial-based GCNNs, spectral-based GCNNs are capable of highly exploiting graph structure information, but always regard graphs undiredcted. Actually, there are many scenarios where the graph structures are directed, such as social networks, citation networks, etc. Treating graphs undirected may lose important information, which is helpful for graph learning tasks. This motivate us to construct a spectral-based GCNN for directed graphs. In this paper, we propose a scalable graph convolutional neural network with fast localized convolution operators derived from directed graph Laplacian, which is called fast directed graph convolutional network (FDGCN). FDGCN can directly work on directed graphs and can scale to large graphs as the convolution operation is linear with the number of edges. Furthermore, we find that FDGCN can unify the graph convolutional network (GCN), which is a classic spectral-based GCNN. The mechanism of FDGCN is thoroughly analyzed from spatial aggregation point of view. Since previous work has confirmed that considering uncertainty of graph could promote GCN a lot, the proposed FDGCN is further enhanced through extra training epochs on random graphs generated by mixed membership stochastic block model (MMSBM). Experiments are conducted for semi-supervised node classification tasks to evaluate the performance of FDGCN. Results show that our model can outperform or match state-of-the-art models in most cases.
Performance Analysis of STDMA and RA-TDMA Wireless Protocols in Industrial Scenarios

Industrial applications in the industry 4.0 scope present significant requirements and challenges from the communication technologies perspective: low latency, reliability, and determinism. However, some use cases add requirements on mobility, scalability and openness, such as flexible and large automated warehouses and production lines. Combining these two kinds of requirements calls for wireless technologies that can provide an adequate trade-off. This paper compares two protocols that rate particularly well in such trade-off, namely Self-Organizing Time Division Multiple Access (STDMA) and Reconfigurable and Adaptive Time Division Multiple Access (RATDMA). The comparison was carried out through simulations under a multipath dispersed time-variant industrial channel while subject to a variable Doppler shift. To this end, we used OMNeT++ in combination with the VEINS framework to evaluate several relevant metrics such as packet losses and packet inter-arrival time. Overall, RA-TDMA shows longer disconnections caused by the hand-over between separate networks. Concerning packet losses, the protocols perform similarly, with RA-TDMA outperforming STDMA under overlapping networks or poor clock synchronization.
Minkowski Fractal Parasitic Slot Antenna for Wideband Applications

A compact printed parasitic slot antenna integrated with Minkowski fractal patch for wideband applications is proposed in this paper. The design is mounted on FR-4 substrate with height h = 1.6 mm and a simple 500Ω microstrip line feed is used to excite the rotated slot of the antenna. The Minkowski fractal patch reduces the overall radiator size and increases the bandwidth compared to the conventional square patch. Four stubs introduced in the design further enhance the bandwidth of the antenna as well as the omnidirectional radiation pattern. The simulation results shows that this design exhibits a bandwidth improvement by 88.87% as compared to the antenna without the stub. Further, the triple notched bands of proposed antenna can accommodate the channel 3 of IEEE 802.11g (2.41 -2.43 GHz), the lower band of 5G technology at 2.5 GHz and can be used at X-band .
Research on Maintenance Strategy Based on Health Status of Fire Control System

The use and maintenance support requirements of modern weapons and equipment are constantly improving, and the maintenance mode of armored equipment is changing from "passive maintenance" to "active maintenance". The core and foundation of active maintenance is to master the health status of equipment operation comprehensively and accurately. This paper presents a research method of maintenance strategy of fire control system based on hierarchical state evaluation. Firstly, we evaluate the current operation state of fire control system layer by layer; and then a performance degradation and failure trend model is established by stochastic process. Secondly, through the competitive failure mechanism of soft failure due to loss and hard failure due to sudden occurrence, the reliability index that can reflect the operation state of the equipment is determined. The research on active maintenance strategy of fire control system not only improves the combat readiness of weapon system, but also ensures the best performance of fire control system with low cost and high efficiency.
A Stacked Autoencoder Method for the PAPR Reduction in VLC Systems

The visible light communication (VLC) system can provide data communication function as well as lighting. It is widely used in automated and sustainable smart farming for its energy efficiency, reliability, security without degrading the crops growth compared to radio frequency communication. A novel peak to average power ratio (PAPR) reduction method is applied based on stacked autoencoder network in VLC systems. The deep learning network is trained by a combined loss function. Simulation results reveal that the VLC system achieves a distinct PAPR reduction and shows an improved bit error ratio performance. The system can be easily implemented and controlled in the existing infrastructure. This research provides a basis for the feasibility of deep learning theory in the design of optical communication system.
Exponential synchronization of memristive Cohen-Grossberg neural networks with hybrid time delays

The paper focuses on the issue of global exponential synchronization for a type of impulsive memristive Cohen-Grossberg neural networks with hybrid time delays. By applying the impulsive functional differential equations, constructing suitable Lyapunov function and contradiction method, several corresponding easy-to-check criteria are derived. Moreover, the research findings in this paper can be easily used to verify or improve the conditions deduced in most of existing literature related to the stability or synchronization for impulsive memristive neural networks. A simulation is represented to checkout the validity of the theory at the end of the paper.
An Acceleration Algorithm of 3D Point Cloud Registration Based on Iterative Closet Point

We propose a novel method for 3D point cloud registration. By considering first order approximation to the data point, our algorithm finds more accurate point pairs than using the closet point strategy. Moreover, we use a new distance error measurement in the ICP framework which considers the second order geometry information rather the first order geometry information in the popular point-plane distance error. Experimental results show that the proposed scheme not only speeds up the convergence of registration, but also eliminates wrong pairing of data points. Both the efficiency of point cloud data registration and the performance of the algorithm have been significantly improved compared with the state-of-the-art methods.
Propeller Modulation Analysis of 4-Blade, 4-Engine Aircraft in FM-band Multistatic Passive Radar

This paper furthers the investigation into modulation effects of propeller-powered aircraft on signals sensed by multistatic FM-band passive radar sensors. Previous results have demonstrated that a passive radar system consisting of a single bistatic transmitter/receiver pair is able to detect and measure the modulation effects of a twin-blade, single-propeller aircraft [1], [2]. We now present supplementary results for a 4-blade, 4-engine aircraft in a single transmitter and geographically separated, dual-receiver multistatic radar system [3], again exploiting illuminators of opportunity in the commercial FM-broadcast band.
Effect of visuomotor learning in arm reaching movement on the visual boundary associated with the direction of target motion

This paper examines whether motor learning affects sensory function, in particular, visual perception in the context of a visuomotor learning paradigm involving arm reaching movements. An experimental task of arm reaching movements requires somatosensory and visual information to improve their performance in trials. The somatosensory perception, limb positional perception, has been reported to change during the motor learning. Then, we wonder whether the visual perception does not change even though it is utilized as the same as somatosensory perception during the reaching movement. To answer this question, we designed an experimental procedure that included visual tests before and after visuomotor learning. Among many characteristics of the visual perception, the perception of movement direction of a visual target, a cursor, was focused. In the normal motor learning task, participants repeat reaching movements to visual targets without seeing the actual movement of the arm: Instead, the cursor is presented to denote the current hand position. However, in our experiments, cursor was intentionally displayed in some deviations, more to the left or the right of the actual hand position. In the visual tests, the participants observed a cursor moving outward along the body midline, but deviating slightly to the left or the right. They were asked to indicate which side (left or right) the cursor was deviated to. From the participants' responses, we determined the visual perceptual direction that separated the left and the right, which we call here the visual boundary. The experiments with eight participants revealed that the visual boundary shifted in a direction opposite to that of the cursor display deviation, implying that the motor learning might affect visual perception.
Comparing Admission Control Architectures for Real-Time Ethernet

Industry 4.0 and Autonomous Driving are emerging resource-intensive distributed application domains that deal with open and evolving environments. These systems are subject to stringent resource, timing, and other non-functional constraints, as well as frequent reconfiguration. Thus, real-time behavior must not preclude operational flexibility. This combination is motivating ongoing efforts within the Time Sensitive Networking (TSN) standardization committee to define admission control mechanisms for Ethernet. Existing mechanisms in TSN, like those of AVB, its predecessor, follow a distributed architecture that favors scalability. Conversely, the new mechanisms envisaged for TSN (IEEE 802.1Qcc) follow a (partially) centralized architecture, favoring short reconfiguration latency. This paper shows the first quantitative comparison between distributed and centralized admission control architectures concerning reconfiguration latency. Here, we compare AVB against a dynamic real-time reconfigurable Ethernet technology with centralized management, namely HaRTES. Our experiments show a significantly lower latency using the centralized architecture. We also observe the dependence of the distributed architecture in the end nodes’ performance and the benefit of having a protected channel for the admission control transactions.
Optimization of Cost: Storage over Cloud Versus on Premises Storage

In today’s world of digitalization the most important object is data. There are numerous techniques available for retrieval of the data. To store this data over a large period of time various data hardware (DHW) are used. As the data is increasing day by day the more cost is incurred in storing the data. Data proliferation is the term used to refer the large amount of data that is created by government and nongovernment institutions either in structured or unstructured format. To minimize the maintenance cost generated by data proliferation IaaS as cloud computing service can be implemented. IaaS can be considered as virtualized service, which can be used to provide ‘as a service’ to the users via cloud vendors. It provides the access of database resources on demand. One can ‘pay-as-per-use’ of resource from the pool of resources and these resources can be virtualized in cloud based environment. To access the virtualized resources there are various approaches used. Private cloud approach is commonly used to maintain the integrity of the data.
Optimal Compromise among Security, Availability and Resources in the Design of Sequences for GNSS Spreading Code Authentication

Spreading code authentication has been proposed as a promising countermeasure against signal layer spoofing attacks to GNSS. It consists in replacing part of the spreading code with a secret, cryptographically generated sequence, that is also provided to legitimate receivers, allowing them to verify the signal authenticity and integrity. Different techniques and formats have been proposed, yet their formulation is typically given as a particular solution, lacking proper generality.This paper aims at providing a unified general model for the design, description, evaluation and comparison of such techniques, introducing simple performance and security metrics, abstracting from the particular cryptographic mechanisms required to generate the sequences. We derive a way to optimize the trade-offs between security level and signal availability to receivers that do not know the modified code, and between security level and required cryptographic resources.We also propose a simpler mechanism that closely approaches the optimal trade-off, and show that it significantly outperforms existing and proposed techniques, especially in the typically considered performance range. Finally, we evaluate the robustness of the proposed schemes to a partial observation of the transmitted modified code by the attacker.
Deep Convolutional Autoencoder Applied for Noise Reduction in Range-Doppler Maps of FMCW Radars

In this paper, we discuss the usage of deep Convolutional Autoencoders (CAE) for denoising Range-Doppler (RD) maps of an FMCW radar in a near-field situation with pedestrians and cyclists as moving objects. Traditional methods for noise reduction such as CFAR with Peak Detection (PD) have poor performance under highly noisy environments, especially when objects have low reflectivity, like pedestrians. We propose the use of Convolutional Autoencoders, in various configurations, to overcome those limitations. Due to its Artificial Neural Network nature, CAE can extract features, learn to identify patterns and recognize moving objects - such as humans and bicycles - while the traditional method acts passively. The results indicate that the usage of CAE overcame CFAR with PD by 76.8%, on average, for reconstructing objects in their correct positions. The results of this work may have many applications, for instance, detecting distant moving objects that are too faint for regular detection.
Enabling and Optimizing MACsec for Industrial Environments (Extended Abstract)

The specifics of industrial networks make security mechanisms necessary, that are specifically designed for them. This work proposes two modifications for MACsec, a new security protocol for protecting layer 2 traffic: a new fragmentation mechanism enabling MACsec to work within future industrial networks, and the use of ciphers currently not standardised for MACsec, leading to a significant increase in the performance of MACsec.
Neoteric Security and Privacy Sanctuary Technologies in Smart Cities

A Smart city is an area that practice several kinds of automated Internet of Things (IoT) sensors to gather information and then make efficient use of perceptions acquired from that information to organize resources, services and assets proficiently. The primary purpose of Smart cities was to refine and enhance the life style of the people, encourage development without affecting the resources for the future generations and advancement of the functionalities in urban areas. With the rapid acceleration in the development of smart devices, security and privacy problems have turn out to be a main task that needs efficacious counter actions. Majorly, this survey commences with an outline of smart cities to offer a cohesive framework. After that, there is discussion about the privacy and security problems in present smart systems through the various necessities for making of a steady and safe smart city. To conclude, we review the present security techniques and as well inspirit others to further research more this evolving ground.
A Compact, T-Slotted Wide Band Microstrip Antenna with Defected Ground Structure for Future 5G Communications

In this article, a compact wide-band microstrip patch Antenna with defected ground square structure is presented. The article presents the application for 5th generation wireless communication. The antenna is designed over a ground plane, having a minimal size of 0. 308λo × 0. 308λo × 0. 009λo with design centre frequency 22 GHz. In order to enhance the antenna bandwidth, a T-shaped slotted Patch and rectangular notch has been introduced. The antenna is covering a wideband of frequency range 23.60 GHz to 44.20 GHz, with a gain of 4.198 dB at 27.30 GHz and 4.703dB at 39.90 GHz centre frequency. The proposed antenna is compact as well as suited for higher band of 5G communications. In addition, the designed antenna exhibits omni-directional radiation pattern which is required for 5G applications.
Compact microstrip Wideband Band Stop Filter with folded stubs to increase coupling

This work presents a new type of wide band stop filter (BSF). The proposed filter topology consists of folded stubs, which controls band stop range. The filter characteristics can be easily controlled by the length of stubs and folded stubs can control coupling between two stubs. The proposed filter design has cut-off frequency of 2.3 GHz. Stopband rejection depth and bandwidth can be controlled by stubs width and spacing between stubs. Three zeros are present in stopband region. This proposed filter has 2.3 - 4.3 GHz band width with 20dB rejection. Sharp cut off rate is achieved 28.3dB per GHz frequency. The compact design structure has size of 15.48mm×29.6mm (0.16λg×0.31λg ) with port length.