Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of occupancy monitoring for removing uncertainty within building energy management systems

This paper provides an overview of methods and models used for the localised detection of building occupants by combining data from a range of sensor types, and the prediction of future occupancy rates based on past data. The occupancy detection proposed here is designed to be implemented as part of a real-time responsive building energy management system, catering building energy use directly to occupant needs. The initial stages of testing used sensor data collected in a small office building in Nottingham, UK. A Neural Network model was trained using data from local environmental sensors, including CO2 level, motion detection, temperature, window status and the detection of personal mobile devices through Wi-Fi and Bluetooth connections. A predictive neural network model was also trained using simulated occupancy rates, with consideration for the level of uncertainty in the model outputs. The results of the study show that the combination of a select group of sensors can provide a lower error in the estimated number of occupants per zone than any single sensor type alone. However, when limited training data is available, it is not viable to include all sensors in the model, as this leads to overfitting. The sensors that give the greatest information gain were found to be best identified by comparing the occupancy estimation made by each sensor individually. It was found that the predictive model outperformed simpler occupancy prediction heuristics, especially when occupant behaviours differ from typical patterns.
Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System

Improving classification accuracy of motor imagery-based brain computer interface (MI-BCI) systems has been discussed widely in the BCI research community. Analyses of multi-class MI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to binary-class data. This paper introduces a new model for multi-class MI-BCI data classification. The approach encompasses two main stages: feature extraction and fuzzy classification. In the feature extraction phase, a common spatial pattern algorithm is employed to extract significant discriminant features from the multi-class data. The obtained features serve as inputs to a fuzzy classifier that is designed by fusing the fuzzy standard additive model and particle swarm optimization method. The proposed fuzzy model aims to handle uncertainty, noise, and outliers existed in MI-BCI data. Experimental studies are carried out using two benchmark BCI competition data sets. Our results show the effectiveness of the fuzzy classifier against its competing techniques. The proposed model paths the way for paralysed or disordered patients to use movement imagery to control different assistive devices in their daily activities.
Motivating content sharing in mobile social network through collective bidding

Mobile social networks (MSNs) enable users to discover and share contents with each other, especially at ephemeral events such as exhibitions and conferences. Nevertheless, the incentive of users to actively share their contents in MSNs may be lacking if the corresponding cost is high. In this paper, we propose a content pricing and sharing framework in MSN that is built on users' collective bidding and content cost sharing. The content sharing problem is formulated as a distributed system that achieves cooperative outcome while preserving non-cooperative decision making among the users through the proposed collective bidding and broadcast nature of wireless communication. That is, co-located peers individually propose payments to their encounters whose contents they are interested in based on their perceived values of the contents. The respective content owners share their contents if the proposed payments can collectively compensate the cost of sharing their contents with these peers. We show that this guarantees individual rationality and promotes content sharing among the opportunistic encounters in the network. Performance evaluation shows that the proposed mechanism reduces the time and cost to collect contents of interest in the network and significantly improves network utilization.
Cancelling strong Doppler shifted returns in OFDM based passive radar

In passive bistatic radar, the detection of moving targets is enhanced by cancellation of low-Doppler returns from the surveillance signal. Strong returns from small moving objects can also impact upon detection statistics. Methods of point clutter removal may require precise estimates of the associated Doppler, or may be numerically intensive. We consider a method of low-Doppler clutter cancellation which exploits the structure of an OFDM illuminating signal, and extend it to cancel specific blocks of delay and Doppler shift. The method is illustrated with simulated data and also applied to a real data set.
Optimization for training CNN deep models based on swarm intelligence

CNNs (Convolutional Neural Networks) achieves considerable performance on the practically important text tasks (e.g. text classification). Nevertheless, neural network's accuracy significantly deteriorates for complex applications. However, they require researchers to specify the exact architecture and set hyper-parameters, regularization parameters including the filter region size, and so on. For instance, all the parameters and randomly initialized values determine how accurate the model is, and the final result produced by the model. To tackle these challenges, many researches proposed good ideas. In this paper, we propose the use of Fish School Search (FSS) in Convolutional Neural Networks (CNNs) such models. The FSS is a modern and new algorithm for optimization purposes. It has succeeded to join the family of other optimization methods. The use of FSS on the training process aims to optimize the results of the solution vectors on CNN in order to improve text mining tasks. In our work, we propose a generic method that could be used and applied on text mining tasks as well as in several data science tasks.
RF-Photonic Filters via On-Chip Photonic–Phononic Emit–Receive Operations

The creation of high-performance narrow-band filters is of great interest for many radio frequency (RF) signal processing applications. To this end, numerous schemes for electronic, microelectromechanical systems-based, and microwave photonic filters have been demonstrated. Filtering schemes based on microwave photonic systems offer superior flexibility and tunability to traditional RF filters. However, these optical-based filters are typically limited to gigahertz (GHz) widths and often have large RF insertion losses, posing challenges for integration into high-fidelity RF circuits. In this paper, we demonstrate a novel type of microwave filter that combines the attractive features of microwave photonic filters with high-Q phononic signal processing using a photonic–phononic emit–receive process. Through this process, an RF signal, which is encoded on a guided optical wave, is converted into a GHz-frequency acoustic wave, where it is filtered through shaping of acoustic transfer functions before being converted back to the optical domain. In contrast to prior phononic filters that utilize gain or loss based on stimulated Brillouin scattering, optical amplification is not used to mediate signal processing. This emit–receive functionality, realized in an integrated silicon waveguide, produces megahertz-bandwidth bandpass filtering while supporting low RF insertion losses necessary for high dynamic range in a microwave photonic link. We also demonstrate record-high internal efficiency for emit–receive operations of this type, and show that the emit–receive operation is uniquely suitable for the creation of serial filter banks with minimal loss of fidelity. This photonic–phononic emitter–receiver represents a new method for low-distortion signal processing in an integrated all-silicon device.
Adaptive random beamforming for MIMO wireless power transfer system

The radio-frequency (RF) enabled wireless power transfer (WPT) system can be benefit from the MIMO technique. However, due to the limited resource, internet of things (IoT) devices can only feedback partial information which is received signal strength (RSS) value instead of channel state information (CSI). Thus, channel estimation based beamforming scheme from receiver side is not applicable for real applications. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to supply multiple batteryless IoT devices with high received power efficiency. Our algorithm does not require the complex channel estimation and adapts the beamforming scheme only according to the partial feedback information. We employ Gibbs sampling and re-sampling methods to generate several random beamforming weight vectors, and choose the optimal one. A simulated annealing algorithm is employed to control the convergence rate. We use the proposed algorithm to supply power in two cases: the maximum power transmission and robust power transmission. The simulation results indicate that this algorithm can fast converge to an optimal value and provide far-field power to multiple IoT devices.
At-risk system identification via analysis of discussions on the darkweb

Threat assessment of systems is critical to organizations' security policy. Identifying systems likely to be at-risk by threat actors can help organizations better defend against likely cyber attacks. Currently, identifying such systems to a large extent is guided by the Common Vulnerability Scoring System (CVSS). Previous research has demonstrated poor correlation between a high CVSS score and at-risk systems. In this paper, we look at hacker discussions on darkweb marketplaces and forums to identify the platforms, vendors, and products likely to be at-risk by hackers. We propose a reasoning system that combines DeLP (Defeasible Logic Programming) and machine learning classifiers to identify systems based on hacker discussions observed on the darkweb. The resulting system is therefore a hybrid between classical knowledge representation and reasoning techniques and machine learning classifiers. We evaluate the system on hacker discussions collected from nearly 300 darkweb forums and marketplaces provided by a threat intelligence company. We improved precision by 15%–57% while maintaining recall over baseline approaches.
On the Symmetry Reduction of Information Inequalities

Information inequalities can be used to derive the fundamental limits of information systems. Many information inequalities and problem-specific constraints are linear equalities or inequalities of joint entropies, and thus, outer bounding the fundamental limits can be viewed as and in principle computed through linear programming. However, for many practical engineering problems, the resultant linear program (LP) is very large, rendering such a computational approach almost completely inapplicable in practice. It was shown recently that symmetry can be used to effectively reduce the scale of the LP; however, the precise amount of reduction was not well understood. In this paper, we provide a method to pinpoint this reduction by counting the number of orbits induced by the symmetry on the set of the LP variables and the LP constraints, respectively. The Pólya counting theorem is a powerful tool for such counting task, which requires identifying the cycle index function. We propose a generic three-layer decomposition of the group structures for the quantities in typical information systems to facilitate such a calculation. Three problems are studied using this approach: extremal pairwise cyclically symmetric entropy inequalities, the regenerating code problem, and the caching problem, for which explicit formulas are provided for the cycle indices of the induced permutations.
Improving wireless multicast communications with NC: Performance assessment over a COTS platform

Multicast services are believed to play a relevant role in next wireless networking scenarios. In this paper we exploit Tunable Sparse Network Coding techniques to increase reliability of multicast communications. We show that the proposed network coding scheme yields a better performance than state-of-the-art solutions, which are traditionally based on retransmissions. We first use a model to analytically compare the two approaches. Then, we validate and broaden this analysis by means of an experimental campaign over a testbed deployed with Commercial Of-The-Shelf devices. This platform, comprising low cost devices (Raspberry-PI), allows us to assess the feasibility of the proposed solution, which offers a relevant gain in terms of performance.
Proposing a Solution for Single-Event Upset in 1T1R RRAM Memory Arrays

Resistive random access memory (RRAM) is a promising emerging technology to provide nonvolatile and scalable data storage in advanced technologies. As a noncharge-based device, the intrinsic RRAM device is immune to single-event effects. However, single-event upset (SEU) due to the MOSFET in the one-transistor-one-RRAM (1T1R) array can be observed. A novel methodology, which can be easily integrated to the other memory system components, is proposed in this paper to detect and correct SEU in 1T1R RRAM memory array. Using the HfOx 1T1R RRAM array as an example, our simulation results show that, for an 8-Gb memory array, the proposed technique can detect and fix the soft errors induced by the heavy ion strikes with 0.66% increase in the chip area. Also, the suggested methodology minimally increases the energy consumption of the read and write operations by 0.2% and 0.1%, respectively.
An Energy-Efficient Multiobjective Scheduling Model for Monitoring in Internet of Things

To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via lossy links. This paper addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal vertex cover (VC) problem with constraint generation. Assuming periodical functioning, VCs are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of VCs to periods is modeled as a multiobjective generalized assignment problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a traveling salesman path problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks.
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition

The current trend in automatic speech recognition is to leverage large amounts of labeled data to train supervised neural network models. Unfortunately, obtaining data for a wide range of domains to train robust models can be costly. However, it is relatively inexpensive to collect large amounts of unlabeled data from domains that we want the models to generalize to. In this paper, we propose a novel unsupervised adaptation method that learns to synthesize labeled data for the target domain from unlabeled in-domain data and labeled out-of-domain data. We first learn without supervision an interpretable latent representation of speech that encodes linguistic and nuisance factors (e.g., speaker and channel) using different latent variables. To transform a labeled out-of-domain utterance without altering its transcript, we transform the latent nuisance variables while maintaining the linguistic variables. To demonstrate our approach, we focus on a channel mismatch setting, where the domain of interest is distant conversational speech, and labels are only available for close-talking speech. Our proposed method is evaluated on the AMI dataset, outperforming all baselines and bridging the gap between unadapted and in-domain models by over 77% without using any parallel data.
SEROSURVEY, HEMATOLOGY, AND CAUSES OF MORTALITY OF FREE-RANGING AMERICAN MARTENS (MARTES AMERICANA) IN MICHIGAN

Abstract: To better understand the clinical pathology, diseases, and causes of mortality of reintroduced American martens (Martes americana) in Michigan, a study was conducted from 2011 to 2015 in the Upper and Lower Peninsulas of Michigan. Samples obtained from live trapping (n = 58) or harvested carcasses (n = 34) were serologically tested for select pathogens. Antibodies against Toxoplasma gondii and canine distemper virus were detected in 58 and 3.4% of samples, respectively. All samples were seronegative for Leptospira spp. and negative for Dirofilaria immitis antigen. Urine samples tested for Leptospira spp. via immunofluorescent antibody assay (n = 7), polymerase chain reaction (n = 6), or both (n = 3) were all negative. Parvovirus DNA was detected in 9.1% of small intestine samples (n = 22) collected from carcasses and in 3.7% of fecal samples (n = 27) collected during live trapping. Complete blood counts (n = 64) and serum biochemistries (n = 63) were obtained from 49 live-trapped martens. Biochemical parameters found to be significantly different (P < 0.05) between genders were calcium, creatinine, glucose, and phosphorus. There was no significant difference between genders for any hematologic parameter. Significant differences (P < 0.05) between summer and winter seasons were found in total estimated white blood cell count, neutrophils, lymphocytes, monocytes, alkaline phosphatase, bicarbonate, calcium, creatinine, globulin, glucose, phosphorus, potassium, sodium, and total protein. There was no significant difference in blood cell count or serum biochemistry values between radio-collared (n = 17) and noncollared (n = 47) martens. Animals seropositive for T. gondii were found to have significantly higher (P < 0.05) eosinophil and globulin levels than seronegative animals. The primary natural cause for mortality of radio-collared American martens was predation. Histologic examinations revealed a high percentage (60%) of martens with verminous or granulomatous pneumonia.
Adaptive operation-space control of redundant manipulators with joint limits avoidance

In this paper, adaptive operation-space control with joint limits avoidance is proposed for a redundant robot manipulator. Utilizing redundant properties of the robotic manipulator, joint limits avoidance is achieved without interfering the main-task objective in the operation-space. Two control objectives are unified under one common control framework. To cope with the unknown and unstructured dynamic nonlinearities of the robot model, neural networks (NNs) are constructed under the proposed torque control design as to approximate the unknown dynamics and achieve small tracking errors. Simulation studies are carried out to verify the effectiveness of the proposed framework.
Wireless location tracking by a sensor-assisted particle filter and floor plans in a 2.5-D space

Indoor localization systems have attracted considerable attention recently. A lot of works have used wireless signals from existing base stations to track users' locations. The major challenge to such systems is the signal-drifting problem. A promising direction to conquer this problem is to fuse the tracked wireless signals with inertial sensing data. In this work, we consider location tracking in a multi-floor building, which we call a 2.5-D space, by taking wireless signals, inertial sensing data, and indoor floor plans of a 2.5-D space as inputs and building a SPF (sensor-assisted particle filter) model to fuse these data. Inertial sensors are to capture human mobility, while particles reflect our belief of the user's potential locations. Our work makes the following contributions. First, we propose a model to partition a 2.5-D space into multiple floors connected by stairs and elevators and further partition each floor, according to its floor plan, into logical units connected by passages. Second, based on the 2.5D space model, we then propose particle sampling and resampling mechanisms over the logical units using wireless signal strengths and inertial sensing data to adjust our beliefs of the user's potential locations. Third, to conquer the signal-drifting problem, we propose a weighting mechanism to control the distribution of particles based on user's activities of walking on grounds/stairs and taking elevators. A prototype has been developed and tested to verify the model and its accuracy.
Power quality monitoring system under different environmental and electric conditions

Power quality monitoring has become very important because it allows preventing damages on sensitive equipment. To perform a proper monitoring, it is necessary equipment with certain features: high sampling rate, portability, high-storage capacity, reconfigurability, among others. This paper presents the results of using a proprietary power quality monitoring system in three different scenarios. The system can acquire and store data for extended periods, and then data can be processed to identify power quality disturbances on the electric grid. Results show that the equipment can acquire, store and process data to identify a vast range of power quality disturbances. Moreover, environmental and electric conditions are not equal for the three locations and the equipment is able to properly work regardless these factors.
Electronic systems for wearable applications: Design methodologies

This work presents methodologies and techniques for the design of wearable sensor systems to be used for the measurement of physiological and environmental parameters. Such devices are becoming widespread in healthcare, sport and fitness, and are taking part to the Internet-of-Things revolution. The paper aims to describe some of the challenges faced at the design stage and possible solutions to be adopted.
Personalized optimal bicycle trip planning based on Q-learning algorithm

Traveling by bicycle has become a rising trend recently for its convenience and flexibility, which calls for considerate bicycle trip planning schemes. While research for traditional trip planning has focused on quantized quality of point-of-interest (POI) or correlations among POIs, problems appear for distinct influential factors in bicycle trips and being unable to plan in a foreseeable stage with satisfying various demands of cyclists. In this paper, to alleviate the deficiencies of conventional approaches that merely concentrating on temporary interests and fully depending on greedy algorithm, the active Q-learning algorithm derived from reinforcement learning (RL) is adopted for Q-value iteration for planning overall optimal bicycle trips. To further meet personal improvised demands such as containing some specific places in the trip, Tailored Trip is provided and a dynamic and flexible place inserting algorithm is proposed to automatically tweak the trip and keep the planning optimum status. Experiments have been conducted to intuitively evaluate the performance of our schemes on two real-world datasets. The planning results clearly illustrate that the optimal node choosing policy is continuously reinforced in our schemes and the result for Tailored Trip highlights the guarantee of overall superiority after trip tweaking.