Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Item Identification and Counting in Ultra-dense Beacon Networks

While many technologies (e.g., RFID, QR code, etc.) have been developed for items identification, they fail to provide continuous monitoring for items in transit. This paper introduces a Bluetooth Low Energy (BLE) beacon-based system, which can be deployed easily with any off-the-shelf smartphone without modification on the existing infrastructures. However, it is an elusive challenge to achieve a fast item identification and counting involving massive items stacked up inside a confined space (e.g., a container), resulting in an ultra-dense beacon network (UDBN). To this end, we propose a novel beaconing solution capable of informing the receiver about their own presence as well as the presence of their neighboring beacons for identification purpose. Specifically, our proposed solution provides a well-designed yet innovative protocol data unit (PDU) which allows the beacon to encapsulate its neighboring information into its own advertising packet. A prototype consisting of 300 beacons is implemented to demonstrate the feasibility of our proposed solution for real-world applications. The extensive experiment confirm the superiority of our proposed solution in delivering a fast item identification and counting in UDBN.
Human Activity Recognition based on WiFi Signal Using Deep Neural Network

Human activity recognition can be applied to various applications, ranging from intelligent security to medical care. Action recognition via WiFi had caught great attention due to its ubiquity, non-occlusion and privacy preservation. Movement of human body can cause fluctuations in the wireless signal reflection, resulting in fluctuation of channel state information (CSI), which is computed from the received WiFi signal containing detailed information of current channel conditions for different sub-carriers and spatial streams, and this can be used to classify human activities. In this paper, we focus on the human activity recognition problem based on the features extracted from CSI data using deep neural network. First, the application of human activity classification as well as data preprocessing techniques are introduced in details, and then a fully connected network is developed as the baseline network model. Furthermore, the weight sharing technique is introduced with the similarity network structure, and a new hybrid loss containing the classification loss and similarity loss is proposed. Experiment results are also included and show that the proposed network can achieve higher classification accuracy than the baseline model.
Industrial Energy Efficiency Towards Green Deal Transition. Case of Latvia.

Abstract Energy efficiency policy has been one of the European Union top priorities for decades and will continue to play a vital role in the next 10 years with the introduction of The Clean energy for all Europeans. Likewise, in Latvia energy efficiency has been given high priority; however, the energy efficiency targets for industry has lacked ambitions. This research focuses on evaluating the Latvian industrial energy efficiency policy using top-down approach and benchmarking energy intensity of Latvian industry to the average of the European Union’s. Results confirm that on average Latvian industry consumes 2.6 times more energy to produce the same amount of value added compared to the average in the European Union; however, every saved energy unit in Latvia would save twice less CO2 emissions considering already largely decarbonized energy mix. In the spotlights of the Green Deal proposed by the European Commission, much higher contribution in terms of CO2 reduction and energy efficiency will be expected from the industry. Nevertheless, energy efficiency targets for Latvian industry should be sector-specific, separately addressing CO2 intensive sectors, and non-intensive CO2 sectors with low added value.
An Effective Routing Mechanism Based on Fuzzy Logic for Software-Defined Data Center Networks

In order to solve the problem of low throughput and poor performance in data center network, this paper proposes a traffic scheduling algorithm exploiting fuzzy logic inference based on Software Defined Networking (SDN). The algorithm uses the characteristics of separation of control and forwarding in SDN architecture to choose paths for data flows by the centralized control of controller. It calculates the candidate path set of accessible paths between the source host and the destination host, then comprehensively considers path hops and bandwidth utilization, evaluates the candidate path using fuzzy logic model, and selects the optimal path. The experimental results show that compared with ECMP, Hedera and FSEM, the proposed algorithm improves the network throughput and load balancing, thus improving the overall network performance of the data center.
A Novel FMCW Radar-Based Scheme for Indoor Localization and Trajectory Tracking

In recent years, with the rapid development of the Internet of Things (IoT) related technologies, the location-based service (LBS) has attracted increasing attention. Frequency modulated continuous wave (FMCW) is a promising technique for indoor localization due to its low deployment costs and strong anti-interference ability. In this paper, we develop a novel FMCW radar-based positioning scheme for indoor localization and target trajectory detection. In particular, we propose an OSCA-CFAR algorithm with star-shaped reference sliding window to improve the accuracy of indoor positioning. Furthermore, the extended Kalman filter is introduced into the scheme to solve the nonlinear problem of motion trajectory tracking. Finally, we carry out extensive experiments with a real testing platform. Experimental results demonstrate that the proposed scheme is able to achieve better performance compared with the existing schemes.
Functional and Neurochemical Identification of Ghrelin Receptor (GHSR)-Expressing Cells of the Lateral Parabrachial Nucleus in Mice

The lateral parabrachial nucleus (lPBN), located in the pons, is a well-recognized anorexigenic center harboring, amongst others, the calcitonin gene-related peptide (CGRP)-expressing neurons that play a key role. The receptor for the orexigenic hormone ghrelin (the growth hormone secretagogue receptor, GHSR) is also abundantly expressed in the lPBN and ghrelin delivery to this site has recently been shown to increase food intake and alter food choice. Here we sought to explore whether GHSR-expressing cells in the lPBN (GHSRlPBN cells) contribute to feeding control, food choice and body weight gain in mice offered an obesogenic diet, involving studies in which GHSRlPBN cells were silenced. We also explored the neurochemical identity of GHSRlPBN cells. To silence GHSRlPBN cells, Ghsr-IRES-Cre male mice were bilaterally injected intra-lPBN with a Cre-dependent viral vector expressing tetanus toxin-light chain. Unlike control wild-type littermates that significantly increased in body weight on the obesogenic diet (i.e., high-fat high-sugar free choice diet comprising chow, lard and 9% sucrose solution), the heterozygous mice with silenced GHSRlPBN cells were resistant to diet-induced weight gain with significantly lower food intake and fat weight. The lean phenotype appeared to result from a decreased food intake compared to controls and caloric efficiency was unaltered. Additionally, silencing the GHSRlPBN cells altered food choice, significantly reducing palatable food consumption. RNAscope and immunohistochemical studies of the lPBN revealed considerable co-expression of GHSR with glutamate and pituitary adenylate cyclase-activating peptide (PACAP), and much less with neurotensin, substance P and CGRP. Thus, the GHSRlPBN cells are important for diet-induced weight gain and adiposity, as well as in the regulation of food intake and food choice. Most GHSRlPBN cells were found to be glutamatergic and the majority (76%) do not belong to the well-characterized anorexigenic CGRP cell population.
Neurobiological After-Effects of Low Intensity Transcranial Electric Stimulation of the Human Nervous System: From Basic Mechanisms to Metaplasticity

Non-invasive low-intensity transcranial electrical stimulation (tES) of the brain is an evolving field that has brought remarkable attention in the past few decades for its ability to directly modulate specific brain functions. Neurobiological after-effects of tES seems to be related to changes in neuronal and synaptic excitability and plasticity, however mechanisms are still far from being elucidated. We aim to review recent results from in vitro and in vivo studies that highlight molecular and cellular mechanisms of transcranial direct (tDCS) and alternating (tACS) current stimulation. Changes in membrane potential and neural synchronization explain the ongoing and short-lasting effects of tES, while changes induced in existing proteins and new protein synthesis is required for long-lasting plastic changes (LTP/LTD). Glial cells, for decades supporting elements, are now considered constitutive part of the synapse and might contribute to the mechanisms of synaptic plasticity. This review brings into focus the neurobiological mechanisms and after-effects of tDCS and tACS from in vitro and in vivo studies, in both animals and humans, highlighting possible pathways for the development of targeted therapeutic applications.
IIoT Based Hierarchical Data Distribution Strategy over Edge and End Device

In this paper, we propose a novel strategy of Industrial Internet of Things (IIoT) based hierarchical data distribution over edge and end device. Machine learning tasks such as workpiece classification based on image recognition often need to be executed at edges and end devices in an IIoT setting, where end devices have limited computing resources which are abundant in edge server. Thus pre-trained shallow neural network (SNN) and deep neural network (DNN) can be deployed at end devices and edge respectively. Traditional data distribution strategy allocates all data to SNN model deployed at end device while data that fails to classify at end device is transmitted to the edge server. However, this traditional strategy will lead to the edge server being idle and the data processing time being too long, which is difficult to meet the requirements of time-sensitive tasks. Therefore, each end device needs to make appropriate data distribution decisions to shorten the data processing time. In this paper, considering the complexity and the error rate of the machine learning model, the computing capability of the end device and the edge server, an optimization problem is formulated to minimize the data processing time. The optimal solution is obtained by analysis and calculation, and the optimal data allocation strategy is proposed. The simulation experiment is designed to deeply study the effects of crucial parameters on the allocation strategy and demonstrate the effectiveness of the proposed strategy.
Semi-LDPC Convolutional Codes with Low-latency Decoding Algorithm

In this paper, we propose a new coding scheme called semi-low-density parity-check (semi-LDPC) convolutional code, whose parity-check matrix consists of both sparse and dense sub-matrices. We propose a sliding-window decoding algorithm with a window size of two, in which a list decoding algorithm is utilized to reduce decoding latency. Simulation results show the proposed semi-LDPC convolutional code with list decoding can obtain a coding gain up to 1 dB over the existing LDPC convolutional codes with the same decoding latency.
A Data Center Load Balancing Algorithm Based on Artificial Bee Colony Algorithm

Aiming at the link congestion and unbalanced load for data center network when scheduling the elephant flow by traditional methods, a load balancing algorithm based on artificial bee colony algorithm IALB (improved artificial bee colony load balancing) is proposed. In the paper we introduce the concept of stability is devoted to selecting the best transmission path for the elephant flow under the multiple constraints of path length, link load, and link stability. In order to find the best path, the algorithm redefines the fitness function according to the number of large flows on the link, the real-time load of the link, and the link stability. Experimental results show that, compared with the traditional ECMP algorithm and Hedera algorithm, the IALB algorithm can perform better in terms of link utilization and network throughput.
A Hierarchical Hybrid Intrusion Detection Approach in IoT Scenarios

Internet of Things (IoT) fosters unprecedented network heterogeneity and dynamicity, thus increasing the variety and the amount of related vulnerabilities. Hence, traditional security approaches fall short, also in terms of resulting scalability and privacy. In this paper we propose H2ID, a two-stage hierarchical Network Intrusion Detection approach. H2ID performs (i) anomaly detection via a novel lightweight solution based on a MultiModal Deep AutoEncoder (M2-DAE), and (ii) attack classification, using soft-output classifiers. We validate our proposal using the recently-released Bot-IoT dataset, inferring among four relevant categories of attack (DDoS, DoS, Scan, and Theft) and unknown attacks. Results show gains of the proposed M2-DAE in the case of simple anomaly detection (up to −40% false-positive rate when compared with several baselines at same true positive rate) and for H2ID as a whole when compared to the best-performing misuse detector approach (up to ≈ +5% F1 score). Besides the performance advantages, our system is suitable for distributed and privacy-preserving deployments while limiting re-training necessities, in line with the high efficiency as well as the flexibility required in IoT scenarios.
Multiscale Comparative Evaluation of the GPM and TRMM Precipitation Products Against Ground Precipitation Observations Over Chinese Tibetan Plateau

This study aims to evaluate the performances of two latest released global precipitation measurement (GPM)-era satellite precipitation final run products (integrated merged multisatellite retrievals IMERG-V06B and Global Satellite Mapping of Precipitation GSMaP-V07) and one tropical rainfall measuring mission (TRMM)-era product (TMPA-3B42-V07) at hourly, daily, and monthly scale over the Tibetan plateau (TP), with special focus on the performances at different rain intensities, subbasins, and elevations at daily scales. Besides, hourly scale evaluations were carried out for one grid box value of IMERG-V06B and GSMaP-V07 versus 15 rain gauges on the rainy season, 2019. Results indicated that: First, GSMaP-V07 outperformed TMPA-3B42-V07 and IMERG-V06B with higher correlation coefficient and lower relative bias, mean absolute error, and root-mean-squared error values at daily and monthly scale. Second, satellite products showed overestimation during light rain and underestimation throughout moderate rain, heavy rain, and rainstorm; Third, satellite products have performed relatively better in lower elevation (<3000 m) regions, but they greatly overestimated the precipitation (more than 50%) at high-elevation (>4000 m). Fourth, satellite precipitation products performed better in the source region of the Yellow River, Yangtze River, Lancang River, and Nujiang River basins, whereas the products greatly overestimated the precipitation in the source region of the Yarlung zangbo River basin. In arid climate regions (Qiangtang Basin and Qaidam Basin), GSMaP-V07 had better performances than other products; and finally, although GSMaP-V07 showed higher overestimation than IMERG-V06B product at hourly scale, it has better consistency with rain-gauge observations. For satellite precipitation data application within the TP, we recommend the GSMaP-V07 over the IMERG-V06B. Furthermore, improvement on the multisatellite rainfall retrieval algorithm is required.
Collaborative Mobile Computation Offloading to Vehicle-Based Cloudlets

This paper investigates collaborative computation offloading in a vehicular network. Although there are increasingly more smart vehicles on roads, a significant number of legacy vehicles that are not equipped with powerful computing devices are expected to exist for a long time. When mobile devices located in these legacy vehicles require computation offloading, they can offload the tasks to nearby smart vehicles that are available to serve as cloudlet servers. Due to high mobility of the vehicles, multiple tasks of an application may have to be offloaded to different vehicle-based cloudlets. The offloading problem is formulated as a Markov decision process (MDP) by considering the randomness of the vehicle moving speeds and wireless channel conditions. The objective is to minimize the average completion time of the application. The complexity for solving the problem directly, however, is prohibitively high due to the large size of the state space and state transition probability matrix. The problem is solved by exploring the special structure of the state space, which helps reduce the computational complexity. A heuristic solution, namely, site-by-site and task-by-task (SSTT), is then proposed that makes the offloading decisions for individual tasks with much lower complexity. Simulation results show that the proposed SSTT solution not only achieves much lower average completion time, compared to executing all tasks locally and using distance-based offloading decisions, but also significantly reduces the energy consumption of the mobile device.
Factors influencing metal bioavailability in soils: preliminary investigations for the development of a critical loads approach for metals

The concept of critical loads, previously applied to acidifying substances, is currently being extended, within the Convention on Long Range Transboundary Air Pollution (CLRTAP), to several metals: Cd, Cu, Hg, Pb and Zn. Receptors in the soil, such as plants and microbiota, are directly exposed to these metals via the soil solution and this should ideally be reflected in environmental quality objectives which, presently, are commonly based on ‘total’ rather than bioavailable metal contents of soils. An understanding of the factors influencing the partition of metals between the solid and solution phases of soils is an essential prerequisite of any attempt to apply the critical loads approach in a manner which relates to the effects of metals on soil organisms.
Prediction of Parkinson's Disease using Principal Component Analysis and the Markov Chains

Parkinson's Disease (PD) is one of the neurodegenerative diseases (ND) that affects the whole life of the patients and make him/her benumb. Copious efforts have been conducted and another fraction of scientists is functional to figure out pre-diagnosis of the neurodegeneration. This proposal concomitantly tries to solve the problem for diagnosing as well as classifying the PD. With the prescribed prospect, Principal Component Analysis (PCA) is being used for extricating PD for its foremost advantage of dimension reduction with a minimal loss of information, in addition, Markov chain Model applied for classifying the PD based on the Unified Parkinson's Disease Rating Scale (UPDRS) for the reason that the chronic diseases cannot be demarcate in a deterministic pattern.
Oxidative degradation of 4-nitrophenol in UV-illuminated titania suspension

An internally-irradiated annular photoreactor has been used to investigate the oxidative degradation of aqueous 4-nitrophenol with titania as the photocatalyst. Reaction runs were performed over a 3-h period and in practically all cases, complete degradation was possible within about 2 h. The kinetics was determined as a function of nitrophenol concentration, oxygen partial pressure, catalyst loading, pH, temperature and light intensity. The reaction was characterised by a relatively low activation energy of 7.83 kJ mol−1 although transport intrusions were negligible. Rate decreased almost exponentially with pH while a quadratic (maximum) behaviour with respect to both oxygen pressure and nitrophenol concentration is symptomatic of self-inhibition possibly due to the formation of intermediates which competitively adsorb on similar sites to the reactants. Increased catalyst dosage also improved the reaction rate although the possible effects of light scattering and solution opacity caused a drop at loadings higher than about 1.20 g dm−3. Rate, however, has a linear dependency on light intensity, suggesting that hole–electron recombination processes were negligible at the conditions investigated. © 2001 Society of Chemical Industry
Random Service Theory Based Link Blockage Model for Near-optical Wireless Communication Systems

Due to the inability of diffraction and refraction for radio propagation in extremely high frequency (EHF) bands, the link budget assessment involving blockage impacts is essential to the “near optical” wireless communication systems working on these bands. Based on random service theory, a novel blockage model and analytical framework are proposed in the context of random obstruction environment with human activities. The probability of non-blockage for a radio link is derived analytically from the state distribution of an M/M/c/c random service model. Also, the interplay between the communication environments and radio link is analyzed and illustrated by numerical simulations with respect to the link blockage occurrence.
UAVs in rail damage image diagnostics supported by deep-learning networks

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.
ISAR Imaging and Micro-Doppler Signature Extraction for Target with Rotating Parts Based on Multiple Measurement Vectors

In this paper, an ISAR imaging algorithm based on signal separation is proposed for the target with rotating parts. Specifically, the echoes of the target subject and the rotating parts are separated by the sparse reconstruction method. Profited from the Doppler difference between the target subject signal and the rotating parts signal, establish the MMV model of the target subject signal in azimuth and the target subject ISAR image can be obtained after signal reconstruction. Meanwhile, the ISAR imaging and micro-Doppler signature extraction for rotating parts can be completed by inverse Radon transform. Simulations are given to validate the effectiveness of the proposed algorithm.