Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Multi-AMR Control Framework for Parcel Sorting Centers

With the growth of eCommerce activities, there is an urgent need for robust logistics solutions to ensure mass and speedy parcel delivery. Upon the parcels are collected, one of the main issues is how to sort a large number of parcels efficiently. One of the solutions is to automate the sorting process in sorting centers by using autonomous mobile robots (AMRs). This paper investigates this solution and studies the multi-AMR control framework for automated parcel sorting centers. In the literature, various frameworks have been proposed for controlling multiple mobile robots under different scenarios. However, most of these existing frameworks may not be well-suited to the automated sorting center environment because the automated sorting center might contain a considerable number of robots moving at high speed. Thus, the routing decision of each robot and the traffic control would need to be done in real-time in order to alleviate congestion to increase the throughput. In this paper, an efficient framework is proposed for automated sorting centers in order to support many high-speed robots. In particular, the proposed framework uses two-layer hierarchical architecture, where all AMRs are considered as the clients of a centralized server. However, unlike traditional server/client architecture, the decision modules in the proposed framework are distributed between the server and clients. Such a design can shift some computationally expensive algorithms from the server-side to the client-side, and thus it is more scalable. The simulation results show that the proposed framework can achieve excellent results. For example, in a sorting center with an area of 2025m2, 410 robots are deployed, and they can handle nearly 30,000 parcels in an hour with our proposed framework. Finally, this paper also explores the various possibilities to enhance the framework further.
Region of Interest and Car Detection using LiDAR data for Advanced Traffic Management System

Traffic monitoring and management have been and still continue to be a vast area of research. With the advent of different types of sensors, road transportation is becoming safer and smarter. Advanced traffic management system (ATMS) aims at improving vehicle flow and safety by leveraging technology with the traditional traffic management systems. In recent times, Light Detection and Ranging (LiDAR) sensors play an important role in ATMS. The LiDAR data, called as point cloud, consists of thousands of points in X, Y and Z coordinate. Point cloud gives a 3-dimensional representation of the surrounding. But, point cloud data is large in size and also consists of redundant information. In real-time applications, processing this substantial sized data is difficult and time-consuming. This paper proposes a complete framework for extracting the region of interest and detecting the vehicles from the point cloud, captured at traffic monitoring systems. This framework constitutes the following sub-steps: downsampling, noise removal, ground removal, object clustering and distant irrelevant objects rejection and finally, vehicle detection using the point cloud data. The MATLAB simulation of these steps suggests that this framework can be appealing to be used for ATMS. Our framework results in 64.5% accuracy of vehicle detection in the KITTI dataset.
Decentralized Power Management of an Islanded Microgrid with AWS-based Wave Energy Converter

In this paper, a decentralized power management strategy without relying on central management and communications is proposed for an islanded microgrid with an Archimedes Wave Swing (AWS). In the proposed strategy, multiloop controllers with a multi-segment power/frequency characteristic are designed for the AWS and battery storage unit. It can autonomously supply the maximum wave power and maintain the power balance of the microgrid with the other droop-controlled micro source. Meanwhile, the state of charge (SOC) limits of the battery are also considered. Possible operating scenarios of a microgrid, including normal operating, battery discharging and battery overcharging, are investigated and tested. And the control performance is validated by simulation results.
Synapse : Towards Linked Data for Smart Cities using a Semantic Annotation Framework

Existing research in the smart city domain concentrates on data collection and storage from numerous sensors deployed in cities. The absence of metadata such as the meaning of the collected data, the context and relationships with other data, has hindered interoperability among intelligent services in the smart city. Linked data is an important concept which can improve various smart city services by adding and connecting metadata to all data existing in smart city. In this paper, we propose a semantic annotation framework, Synapse, that can add meaning to data and annotate relationships between data using ontologies. The Synapse framework provides annotations that make it easier to add a Common Ontology and metadata that can be used across multiple smart city services. Synapse provides the ability to index and annotate using ontology schema and smart city data providing the search and traversal mechanisms for semantic-based linked data. To support temporal associations, Synapse uses data time-stamps in the ontology and utilizes it as linked data. Synapse ontologies define the relationship between common concept and functions such as zones, evaluations, and observations required in smart cities, and provides a dynamic structure with which ontologies can be derived for specific domains. In order to verify the feasibility of Synapse, ontology and annotation technology was successfully applied to 90,240 data points collected from smart parking services and air quality services.
An Iterative Interaction-Design Method for Multi-Modal Robot Communication

The design space of human-robot interaction is large and multi-dimensional. A sound design requires a systematic theory-driven exploration, specification and refinement of design variables. There is a need for a practical method and tool to iteratively specify the content of the dialogue (e.g., speech acts) with the accompanying expressive behavior (e.g., gesture openness) as prescribed by social science theory, e.g., task- and person-oriented communication. This paper presents an iterative interaction-design (ID) method for multi-modal robot communication. Following the ID-method, a designer first creates his/her "own" individual design and, subsequently, provides an iteration to the evolving iterative design. To support the design method, we developed an ID-tool (available for download). The tool support entails (a) selecting the theory-based communication style; (b) creating and linking the dialogue act components for the concerning use case; and (c) setting the associated expression parameters.We conducted a study with Industrial Design students (N = 13) who followed the ID-method and used our tool to design person- and task-oriented communications for a reception robot. Our method produced distinctive task- and person-oriented dialogue styles, i.e., provided the predicted theory-based multi-modal communicative behaviors. The task-oriented style showed a more formal, shorter and less chatty communication. Overall, there was a rather smooth design convergence process, in which the individual designs were harmonized into the iterative design. For the selected design problem, the ID-tool had a satisfactory usability. Next steps include validation of the communication styles in an empirical study and, subsequently, identification of reusable design patterns.
Latency and Reliability of mmWave Multi-Hop V2V Communications Under Relay Selections

Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving traffic efficiency in future intelligent transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selection schemes in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which can more accurately capture the characteristics of mmWave signal propagation in urban grid roads than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., random with forward progress (RFP), most forward with fixed radius (MFR), and nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs’ instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by balancing the current single-hop latency and the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications.
SiGe EAM-Based Transceivers for Datacenter Interconnects and Radio Over Fiber

Silicon photonics is a key-enabling technology leveraging decades of effort and infrastructure of the microelectronics CMOS industry resulting in high yield, low cost and potential high volume manufacturing. Furthermore, due to the high index contrast of the platform, very compact, high-complexity photonic integrated circuits can be devised. To benefit from these advantages, high-speed modulators should also be compatible with silicon technology. In this respect, SiGe electro-absorption modulators (EAM) are considered as a promising candidate since they are CMOS-compatible and offer high-speed, compact, low-loss and low-power modulation. In this paper, we discuss SiGe EAM-based transceivers for next-generation datacenter interconnects (DCI) and radio-over-fiber (RoF) fronthaul in next-generation cellular networks.
Detection of wireless fake access points

Wireless networks have become a common part of everyone’s life who wants to connect to the Internet network. Despite the rapid development of wireless technologies, common users still do not pay much attention to the security questions. One of the most challenging security problems of Wi-Fi networks is to detect and prevent the fake access point attack. Existing solutions require the installation of proprietary hardware, protocol changes, measuring frame characteristics etc. Moreover, these solutions are strictly focused on an individual cyber attack detection. In this work, we propose our method that detects a fake access point and that allows the detection of multiple cyber attacks in local wireless networks. In our research, we focus on the detection of the fake access point attack, the KARMA attack, the frequency congestion attack and the de-authentication attack.
Mechanical Fault Diagnosis of Transformer Based on Improved Characteristic Matrix of Vibration Signal

The mechanical fault diagnosis of transformer is mostly determined by independent analysis of a vibration signal, which is too one-sided to reflect the mechanical state of transformer. Therefore, firstly, the vibration signals of multiple positions of the transformer are measured, and the proportion of each frequency amplitude of several groups of signals is used as the improved eigenvalue to form the characteristic matrix. Then, the matrix similarity of the characteristic matrix between the identification signal and the normal signal is optimized. Finally, the effects of load rate, power factor and imbalance on the matrix similarity of vibration signals are studied by experiments. The results show that the improvement made in this paper can effectively reduce the misjudgment caused by the change of load conditions in fault diagnosis, and can accurately diagnose the existence of faults.
Study on the Tool Enzymes of Isolating Protoplasts of Porphyra yezoensis

The efficient and economical tool enzymes for isolating protoplasts of Porphyra yezoensis from the digestive glands of four shellfish(Monodonta labio,Chlorostoma rusticum,Littorina brevicula,Acanthochiton rubrolineatus),cellulase and pectinase from internal market were studied.Results indicated that the mixture of shellfish enzyme and cellulase was more effective than any single enzyme in isolating protoplasts of Porphyra yezoensis.The mixture of digestive enzyme from Monodonta labio and cellulase was the best among them.The best conditions of enzymatic isolation were 25 ℃,pH6.5 and one hour was enough for isolation.The rate of enzymatic isolation could come up to 80.4%. A high temperature,unsuitable pH or a long time of isolation could raise the cell dead rate.The survival rate of protoplasts could raise from 65.0% to 80.7% with 2 mol/L of glucose as penetrant.
Task Distribution Based on Variable-Order Markov Position Estimation in Mobile Sensor Networks

With the popularization of intelligent hardware, wireless sensor networks have led to mobile crowdsensing (MCS) systems, which provide solutions for large-scale and complex urban data collection. Task distribution is the most important part of intelligent hardware applications. MCS can improve the task distribution efficiency by accurately predicting the location of a perceived user for task distribution. This paper proposes a task location estimator based on a variable-order Markov time window sensing (TEMTWS) algorithm. This method is based on time window modeling, and the association between user tasks is established by sensing the historical track data of user execution tasks. First, the task execution frequency and task vector are calculated, and the organizer at each position is selected. To obtain more perceptual users, similarity estimation is performed on the users and organizers within the time window, and users with high relevance are grouped into the same cluster. An experiment is conducted with the Gowalla dataset to verify the algorithm. The results show that the proposed algorithm outperforms the standard Markov K-means algorithm and K-means-GA algorithm in terms of the prediction accuracy.
Opening the ancillary service market: new market opportunities for energy storage systems in Italy

Along with energy policy updates at European level, Italy recently opened its ancillary service market for distributed energy resources. This paper investigates the newly accruing market opportunities for an exemplary battery energy storage system of 1 MW. Based on effective market behavior, derived from a two years data set with hourly resolution, the study provides a comprehensive evaluation of economic options. Two operation modes are therefore confronted, providing upward and downward balancing services in combination with the intraday electricity market. Additionally, different bidding strategies are applied and the economic potential in the different Italian market zones is assessed.
New Vision for 5G Backbone Network Architecture

Today’s routing infrastructure uses BGP for interdomain routing, which suffers from several well-known issues - slow convergence, lack of transparency, limited control in routing, and limited scalability for handling the massive IPv6 address space. As a result, it cannot meet the stringent requirements 5G ultra-reliable low-latency communication (URLLC), which has been designed for latency-critical real-time applications such as tele robotics and augmented reality, and must scale up to support millions of IoT devices. To address these shortcomings of today’s routing infrastructure, we propose a four layer hierarchical network architecture, that associates a real (geographical) address with each IP address. The real address has four parts - country, state, district, and AS - that correspond to the four layers of routing hierarchy, and is used for inter-domain routing; the IP address is used for routing only within an AS. The hierarchical routing using geographical addresses enables quick convergence, fewer number of routing hops, and a very small number of forwarding rules in each router. Each network layer can further be sliced for the three broad categories of 5G users i.e mBBC, MTC and uRLLC. As deploying a new architecture across the globe is challenging, we discuss how it can be incrementally deployed one country at a time, exploiting existing infrastructure for international routing.
Miniature Wideband Rat-Race Coupler in Silicon-Based Integrated Passive Device Technology

In this paper, a novel miniature wideband rat-race coupler design is proposed. The proposed rat-race coupler employs differential bridged-T coils and a phase inverter as building blocks, such that both compact size and wide bandwidth can be achieved at the same time. Specifically, a miniature rat-race coupler for 2.5 GHz application is implemented in silicon-based integrated passive device technology. The circuit size is only 3.275 mm×2.247 mm. The measured to-dB return loss bandwidth for the difference port is larger than 93% while the 15-dB return loss bandwidth is larger than 63%. Very small amplitude and phase errors as well as good isolation performance are also achieved. To the best of our knowledge, the proposed one is the smallest ever reported rat-race coupler with a bandwidth larger than 30%.
Design of Dual Band-Notched UWB Antenna with Sharp Roll-Off Characteristics

A novel design of fragment-type etch patterns on ultra-wideband antenna with sharp roll-off band notches is proposed. Optimization searching of high-performance band notch evaluated by roll-off criterion (ROC) is implemented by using multi-objective optimization searching algorithm. To reduce the computation cost, proper slits are preset, and the searching efficiency is improved greatly. For demonstration, dual band notches of ROC=0.63 at the WiMAX band and ROC=0.65 at the WLAN band has been achieved by adopting two slits.
SILU: Strategy Involving Large-scale Unlabeled Logs for Improving Malware Detector

Machine learning is becoming a key component to automatically detect malware-infected hosts by analyzing network logs in a security operations center (SOC). However, machine learning usually requires a large amount of labeled training data, which is difficult to acquire since labels are manually set by professional security analysts. On the other hand, abundant unanalyzed logs are kept stored in daily operation and stay unlabeled even though they could compensate for the lack of existing labeled training data. This paper proposes SILU, a novel semi-supervised learning method, which fully leverages unlabeled data and enhances detection capability without increasing manually labeled data. SILU learns from combined labeled and unlabeled training data to automatically augment labeled training data and then generates a classifier through the screening process. Unlike most semi-supervised learning methods used in cyber security, which use test data as unlabeled training data, SILU does not require retraining every time test data change since it can use different datasets for unlabeled training and test data. This helps SOC operation for practically suppressing detecting time. In addition, though SILU partially includes a supervised learning method, it does not require a specific supervised learning method. Therefore, SILU can be added on to any type of classifier of a supervised learning method. Moreover, SILU can suppress the deterioration of classification performance for test data through the screening process. We evaluated SILU using two types of real-world logs: proxy logs from a large enterprise and NetFlow from a large ISP. We demonstrated that by evaluating with different types of classifiers, SILU always improves detection capability for supervised learning methods. SILU also outperforms current semi-supervised methods. As a whole, SILU works as an add-on to existing supervised learning methods with little overhead and performs better than conventional supervised learning methods. Our evaluation also shows that using NetFlow from ISP as unlabeled training data works better than using only labeled proxy logs in the same enterprise. These results suggest that SILU can extend detection capability more when different organizations, e.g., SOCs and ISPs, collaborate and share unlabeled data.
Estimation of Mental Health Quality of Life using Visual Information during Interaction with a Communication Agent

It is essential for a monitoring system or a communication robot that interacts with an elderly person to accurately understand the user’s state and generate actions based on their condition. To ensure elderly welfare, quality of life (QOL) is a useful indicator for determining human physical suffering and mental and social activities in a comprehensive manner. In this study, we hypothesize that visual information is useful for extracting high-dimensional information on QOL from data collected by an agent while interacting with a person. We propose a QOL estimation method to integrate facial expressions, head fluctuations, and eye movements that can be extracted as visual information during the interaction with the communication agent. Our goal is to implement a multiple feature vectors learning estimator that incorporates convolutional 3D to learn spatiotemporal features. However, there is no database required for QOL estimation. Therefore, we implement a free communication agent and construct our database based on information collected through interpersonal experiments using the agent. To verify the proposed method, we focus on the estimation of the "mental health" QOL scale, which is the most difficult to estimate among the eight scales that compose QOL based on a previous study. We compare the four estimation accuracies: single-modal learning using each of the three features, i.e., facial expressions, head fluctuations, and eye movements and multiple feature vectors learning integrating all the three features. The experimental results show that multiple feature vectors learning has fewer estimation errors than all the other single-modal learning, which uses each feature separately. The experimental results for evaluating the difference between the estimated QOL score by the proposed method and the actual QOL score calculated by the conventional method also show that the average error is less than 10 points and, thus, the proposed system can estimate the QOL score. Thus, it is clear that the proposed new approach for estimating human conditions can improve the quality of human–robot interactions and personalized monitoring.
Supervised Deep Learning for MIMO Precoding

In this paper, we aim to design an end-to-end deep learning architecture for a broadcast MIMO system with precoding at the transmitter. The objective is to transmit interferencefree data streams to multiple users over a wireless channel. We propose end-to-end learning of communication systems modelled as a Deep autoencoder network with a novel cost function to achieve this goal. This architecture enables optimization of the transmitter and receiver network weights jointly over a wireless channel. We also introduce a way to precode the transmitter embeddings before transmission. An end-to-end training of the autoencoder framework of transmitter-receiver pairs is employed while training the proposed transmit-precoded MIMO system model. Several numerical evaluations over Rayleigh block-fading (RBF) channels with slow fading are presented to prove this approach. Specific training methods are suggested to improve performance over RBF channels in this paper.