Miracle-dz/UniFAR-FTUs huggingface dataset:
Honey Bee Sound Classification Using Spectrogram Image Features

The main problem for beekeeping farmers in Thailand or around the world is lack of work skills or work experience. This is because beekeeping is a very delicate process and must be carefully monitored with the environment that is changing. The honey bee is known as the European honey bee (Apis mellifera). In thewhole life of honey bee, sound plays an important role in the development of key elements of the honey bee colony. Factors or beehive situations that affect beekeeping include: 1. A lost Queen 2. Smoke 3. Enemy attack 4. Mites 5. Lacks pollen and 6. Normal. In our research, we developed a process of sound classification of a honey bee colony in each situation in bee boxes with spectrogram image features. We examined these in 6 groups to train the classification models. The achieved accuracy was 99.82% in Logistic Regression. The result shows high efficiency of understanding honey bee sound classifications using the spectrogram image feature.
A 2.4-GHz 10-W Class Bridge Rectifier and Its Efficiency Analysis With the Behavioral Model

This article presents a 2.4-GHz band 10-W class bridge rectifier that achieves a high-measured efficiency of 90.2%. To clarify the mechanism for obtaining high efficiency, a theoretical analysis with the novel behavioral model of bridge rectifiers is demonstrated. The behavior model includes three functional blocks, namely; 1) impedance transform, which defines the RF impedance for the bridge diode; 2) RF impedance matching; and 3) rectification with the ideal current waveform of the bridge diode. Closed-form formulas for the rectification efficiency and the maximum input power are derived using the behavioral model. The RF impedance can be optimized to obtain high efficiency with the derived formulas, similar to source-pull operations in harmonic balance simulations or experiments. The fundamental limitation on the rectification efficiency is obtained for a bridge rectifier with the optimum RF impedance. In the experimental investigation of the 2.4-GHz 10-W class bridge rectifier, the measured efficiency of 90.2% agreed well with the theoretical value of 91.2% with represented formulas. Furthermore, the derived formulas are applied to the 5.8-GHz 1-W rectifier, and its effectiveness can be confirmed. It is evident that the analyzed efficiencies are close to the fundamental limitations.
Towards High Productivity and Performance for Irregular Applications in Chapel

Large scale irregular applications, such as sparse linear algebra and graph analytics, exhibit fine-grained memory access patterns and operate on very large data sets. The Partitioned Global Address Space (PGAS) model simplifies the development of distributed-memory irregular applications, as all the memory in the system is viewed logically as a single shared address space. The Chapel programming language provides a PGAS programming model and offers high productivity for irregular application developers, as remote communication is performed implicitly. However, irregular applications written in Chapel often struggle to achieve high performance due to implicit fine-grained remote communication. In this work, we explore techniques to bridge the gap between high productivity and high performance for irregular applications using the Chapel programming language. We present high-level implementations of the Breadth First Search (BFS) and PageRank applications. We then describe optimized versions that utilize message aggregation and data replication in ways that could potentially be applied automatically, improving performance by as much as 1,219x for BFS and 22x for PageRank. When compared to MPI+OpenMP implementations that employ optimizations of the same type as those applied to the Chapel codes, our optimized code is 3.7x faster on average for BFS but 1.3x slower for PageRank.
A Highly Reliable Ultralow-Latency Wireless Solution for Industrial Control Loops: Design and Evaluation

We present the results of implementing and testing a wireless solution intended for direct one-to-one replacement of communication cables in fast industrial control loops. With an ultralow cycle time of 8000 packets/s on each unidirectional link, we measured packet error rates in the range of 1 error per 1 billion packets in the presence of industrial production-hall channel conditions, achieving an unprecedented combination of high reliability and low latency in fading channels. The transceivers were built using only easily accessible and affordable components. Frequency diversity and spatial diversity are exploited through coded orthogonal frequency-division multiplexing (OFDM) modulation and 2x2 multiple-input-multiple-output (MIMO) transmission. The replacement of cables by the devised wireless solution opens up possibilities from simplified reconfigurability and reduced maintenance cost to the introduction of entirely new production processes that are currently not possible.
Blind Adaptive Channel Estimation using Structure Subspace Tracking

In this work, an adaptive blind structure subspace algorithm is developed. The proposed algorithm tracks the signal subspace and estimates the channel in a multiple-input multiple-output (MIMO) system. The algorithm utilizes the inherent Toeplitz structure embedded in many communication systems and the orthogonal projection approximation subspace tracking (OPAST) algorithm to estimate the desired channel. The proposed scheme is robust and capable of estimating channels adaptively at a relatively low computational cost. The simulation results demonstrate the appealing benefits of the proposed method.
KR-LISTA: Re-Thinking Unrolling for Covariance-Driven Sparse Inverse Problems

This paper considers the problem of joint support recovery in Multiple Measurement Vector (MMV) models. We show that by exploiting correlation priors, one can boost the performance and computational cost of unrolled data-driven techniques for joint support recovery. We propose a novel unrolling of the Iterative Shrinkage Thresholding Algorithm (ISTA) for correlation-aware support recovery, which preserves the special "Khatri-Rao" structure that underlies the model. The proposed network, termed as "KR-LISTA", provides a parameter-efficient unrolling which enables training with limited data. Our numerical simulations demonstrate the effectiveness of KR-LISTA at test time for different values of SNR, and support sizes which were not provided to the network during training. In addition, KR-LISTA is also seen to be effective even in presence of model uncertainties. 1
Vibrational Radar Backscatter Communications

In this paper, we introduce a novel approach for backscatter communications using mmW automotive radar illumination of a vibrating transducer whose time-varying surface displacement is used to perform digital signaling. Radar backscat- ter communications, as exemplified by RFID tags, is typically limited to UHF frequencies due to the need for efficient transponder re-radiation of RF illumination. This has largely precluded backscatter communications at millimeter-wave (mmW) frequencies without the use of expensive retrodirective antenna arrays. This paper derives the effective communications channel model and optimum detector for M-ary vibrational radar backscatter communications (VRBC). Because of a wideband mmW radar's ability to separate transponders with high range and bearing resolution, the effective number of VRBC channels can scale without introducing mutual interference among transponders. Vibrational signal designs are presented with both simulation and real data experiments using a commercially-available low-power mmW radar and vibrational source. Essentially error-free data rates of 1 kbits/sec have been achieved in a laboratory setting.
Data-Blind ML: Building privacy-aware machine learning models without direct data access

Traditional Machine Learning (ML) pipeline development requires the ML practitioner to directly access the data to analyze, clean and preprocess it, in order to develop an ML model, train it and evaluate its performance. When the data owner has no infrastructure for in-house development, such pipelines are outsourced. It is common that data has some level of privacy constraints that will impose a laborious and maybe expensive infrastructure, including among others contracts drafting and infrastructure improvement. Traditional approaches rely either on anonymization which does not entirely protect from identity disclosure, or on synthetic data generation which requires expertise not necessarily available to the organization. In this paper, we present Data-Blind ML, an automated framework, fueled by synthetic generative learning and distributed computing paradigms, which enables an organization to outsource the development and training of ML models without sharing any sample from the real dataset. In addition, the framework allows the ML practitioner to get feedback of the model’s performance against the actual real data without accessing it directly.
Graph-Based Interpolation of Local Activation Time on the Cardiac Surface

We propose a novel method for spatial interpolation of local activation times (LATs), a feature extracted from intracardiac electrograms (EGMs) which are sampled at various points of interest on the heart. Cardiologists utilize the projection of the LAT signal onto a 3D visualization of the heart to aid in determining the effects of intervention or the source of arrhythmias during cardiac ablation procedures. For an accurate and complete LAT map, technicians must record at many locations, a slow process which results in samples that are still sparse due to time constraints and must be interpolated to estimate the feature value over the entire surface. The method proposed here utilizes a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem with a closed-form solution. The method is evaluated on a patient dataset obtained by the CARTO electroanatomic mapping system during a premature ventricular complex (PVC) ablation procedure. Our approach outperforms standard interpolation techniques, resulting in a 2x higher signal-to-noise ratio (SNR).
Recurrent Time-Varying Multi-Graph Convolutional Neural Network for Personalized Cervical Cancer Risk Prediction

Cervical cancer screening programs have reduced the incidence of cervical cancer, but suffer from over- and too infrequent screening as women’s risk of developing cervical cancer differs. Personalized risk prediction models contribute toward efficient, personalized cancer screening. This paper presents a personalized time-dependent cervical cancer risk prediction scheme to aid experts in recommending screening intervals. From partially observed screening histories, the proposed approach learns time-varying row-graphs that model the time-varying relations among the screening records of patients and a column-graph that encodes smoothness of an individual screening history. Then, leveraging these geometric structures, we reconstruct the entire latent risk of each individual from scarce screening data. In order to accomplish this, a novel time-varying multi-graph convolution neural network is proposed. These estimated latent risk profiles are used to forecast the cancer risk of new patients. The proposed approach is tested both on synthetic and real-life screening data obtained from the Cancer Registry of Norway.
Bayesian CRLB for Blind Indoor Localization with Imperfect Receiver Synchronization

We examine the bounds on the error variance for radio-based indoor localization. In the considered dense multipath scenario, an asynchronously transmitting (blind) node sends a wideband signal which is captured by a fixed infrastructure comprising several multi-channel receiver anchors at known positions. The transmitter location can be inferred by calculations based on time difference of arrival and angle of arrival. This requires a certain degree of phase synchronization between the channels of the anchors and time synchronization between the anchors themselves. We present statistical models of the synchronization impairments and derive the resulting Bayesian information matrix. Evaluating the Bayesian Cramér-Rao lower bound (CRLB) on the localization error variance in regard to different aspects of synchronization quality provides insights into the specific requirements on the implementation of a suitable synchronized receiver infrastructure. In conjunction with associated simulations and real-world measurements, this has the potential to further facilitate cost-efficient localization of low-power nodes in the context of the internet of things.
AM/PM Characterization of Wideband Power Amplifiers

This paper presents the linearity assessment of two state-of-the-art wideband power amplifiers, a single stage class AB and a two-way 6 dB Doherty, in terms of amplitude-to-phase-modulation (AM/PM) distortion. The simulated and measured characteristics are shown and discussed. The class AB amplifier maintains AM/PM within 6° from 1 to 4 GHz, i.e. on a 120% bandwidth, while the Doherty amplifier has similar performance from 2.2 to 2.9 GHz, i.e. on a 28 % bandwidth. This characterization campaign highlights the superior linearity of the class AB amplifier over relative bandwidths that exceed 100% but, at the same time, the potential of the Doherty amplifier to maintain very limited (within 10°) amplitude-to-phase-modulation distortion over bandwidths up to around 60 %. Both power amplifiers are well in line with the state of the art also in terms of linearity, besides output power, gain, and efficiency performance.
Thermal-comfort Control using Occupancy Detection and Fuzzy Logic for Air-conditioning Systems

Integration of sensing system and a split air conditioner (AC) gives better occupants' comfort control in a built environment. To reach optimal control from the air-conditioning system, various parameters such as temperature, humidity, and wind speed are used to indicate thermal comfort of the occupants. In this work, we proposed thermal-comfort control through an occupancy-detection algorithm and a fuzzy inference system (FIS). The proposed method aims to maintain occupant's thermal comfort between a comfort bound [0.5, −0.5] of Predicted Mean Vote (PMV) index. The occupancy-detection algorithm estimates a distance between the occupant and the split AC, which is an input variable for the FIS. The FIS use the estimated distance and the PMV index at the occupant's position to find the optimal set mode of the split AC in real time. Two conventional controls of the split AC, FIXED mode and AUTO mode, are compared with the proposed method. Our experimental results show that the FIXED mode and the AUTO mode can control the PMV index that are within the comfort bound for 13.19% and 80.22% of a total experimental time, while the proposed method achieves 100% of the total experimental time.
Frequency Hopping Acquisition Based on Variable Forgetting Factor Recursive Least Squares Algorithm

The main drawback of frequency hopping pattern synchronization capture algorithms is time-consuming. To address this problem, variable forgetting factor recursive least square (VFFRLS) frequency hopping pattern acquisition algorithm is proposed in this paper. First, the coefficients of the adaptive spectrum estimation model are calculated iteratively used the VFFRLS algorithm. Then, instantaneous frequency of the frequency hopping signal is estimated, and the result is converted into a binary sequence. The most significant bit of the binary sequence is loaded into the linear feedback shift register. Finally, the correct frequency point could be obtained. The theoretical expressions of bit error rate, capture probability and average capture time are derived under the Additive White Gaussian Noise environment. Moreover, the capture performance is verified through numerical simulation.
PENGARUH JENIS DAN KONSENTRASI BAHAN PENSTABIL TERHADAP KARAKTERISTIK MINUMAN DARI BEKATUL SELAMA PENYIMPANAN

Rice bran is the outer coating of the rice kernel and is generated dunng the rice milling. From a nutritional point of view, rice bran is the most valuable byproduct. Rice bran consists of: pericarp, aleurone, germ, and part of the endosperm which is the outer layer of the rice kernel. They are removed during whitening and polishing of husked rice. Rice bran beverage is one of the grain-based products that are still being developed due to its nutritional component. Turbidity is an important physical charac­ teristics in rice bran beverage that could be maintained by adding stabilizer. This research will observe the effect of stabilizer on physical and chemical characteristics office bran beverage during 8 weeks of storage. The results showed thatpH and total titratable acid remained stable while turbidity and total soluble solid showed a signifi­ cant decrease during 8 weeks of storage. Adding the stabilizers effected turbidity, total soluble solid, and viscosity of rice bran beverage.
Performance Analysis and Optimization for IoT Mobile Edge Computing Networks With RF Energy Harvesting and UAV Relaying

This paper studies unmanned aerial vehicle (UAV)-aided nonorthogonal multiple access (NOMA)-based mobile-edge computing (MEC) in Internet of Things (IoT) systems in which the uav acts as a relay (UR). Specifically, we consider a scenario with two clusters IoT devices (IDs) (i.e., a high-priority cluster IA and a low-priority cluster IB) with limited resources, so these IDs cannot compute their tasks and must offload them to a base station (BS) through a UR. We propose a protocol named time switching - radio frequency (RF) energy harvesting (EH) UR NOMA (TS-REUN), which is divided into 5 phases. By applying the TS-REUN protocol, the IDs in the two clusters and the UR harvest RF energy from the broadcast signal of the power beacons (PB). Then, the IDs offload their tasks to the MEC server located at the BS. After server processing, the IDs receive the calculation results from the BS via the UR. The effects of both imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) on the REUN-based mec (REUN-MEC) are taken into account. To evaluate the performance of the system, we derive closed-form expressions for the successful computation probability (SCP) and energy consumption probability (ECP) in the Nakagami- $\mathfrak {m}$ fading channel. Moreover, we propose an optimization problem formulation that maximizes the SCP by optimizing the position and the height of the UR and the time switching ratio (TSR). The problem was addressed by employing an algorithm based on particle swarm optimization (PSO). In addition, the Monte Carlo simulation results confirmed the accuracy of our analysis based on system performance simulations with various system parameters, such as the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and the height of the UR.
The Development of Small Medium Micro Enterprises: The Case Studies of The Gauteng Universities

Purpose: The development of small medium micro enterprises in any economy rests with entrepreneurs. Gauteng universities like all other universities in South Africa have the responsibility of producing entrepreneurs for the South African economy. The purpose of this paper is to identify and discuss methods and processes that can transform entrepreneurship studies at Gauteng universities such that they are able to produce entrepreneurs and to suggest ways in which students can become entrepreneurs upon graduation. Research design/methodology: The research looks at how entrepreneurship studied at tertiary institutions should be better integrated with the South African Government policies aimed at eradicating poverty and reducing unemployment through the creation of SMME’s in the South African economy. In realisation of this, questionnaires are administered to different role-players in order to delve into entrepreneurship. Furthermore, the research examines the personality traits, which are required for individuals to become entrepreneurs, to establish if they are indeed a necessity in order for an individual to become an entrepreneur. Lastly, the study looks at projects and planning done during these courses at the Gauteng universities with a view to assess their ability to produce the required entrepreneurs. Part of the recommendations is to infuse practical aspects of entrepreneurship into entrepreneurial courses.
Development of an IoT Based Health Monitoring System for e-Health

Healthcare monitoring system in hospitals and other health institutes has increased significantly. In recent days healthcare monitoring systems with new technologies are becoming of great concern to countries all over the world. Nowadays IOT (internet of things) is such an emerging technology. Iot consists of various sensors and communication devices which are necessary tools for IOT based health monitoring systems. Among the various applications that Internet of Things (IoT) facilitated to the world, Health care and Health monitoring applications are most important. It also responded if any medical emergency was needed. IoT (Internet of Things) has brought a remarkable approach in healthcare during the global pandemic. The remote health surveillance monitors certain parameters of a patient using digital technology and allows a correct evaluation of health right at home. This brilliant internet revolution has not only minimized patient movement during COVID but also ensured smart healthcare for all ages. IoT heads to set up a strategic connection between the patient & the doctors. The goal is to track the important health parameters such as - Blood pressure, heart rate, blood glucose level etc. and evaluate the data to figure out any sort of medical emergency. From diagnosing heart disease to finding ICU bed availability in the nearest hospitals, digital technology is all set to assist. Using certain medical sensor devices & web-based apps, the health data is passed to the portal from where concerned doctors can provide medical assistance. The data collection, transmission & visualization can use a smartphone as a hub, thus making the operation smooth & flexible. It is the most practical & economical solution to people of all ages while avoiding direct contact & preventing the spread of the virus. The wearable real-time health tracking devices privilege the elder citizen through continuous monitoring while ensuring immediate measures in case of emergency. In this paper, a review of IoT based smart health monitoring systems is presented. The latest innovative technologies used for IoT based smart health monitoring systems with their benefits and challenges have been discussed. This review's goal is to effectively and continuously monitor the multiple patients in a hospital ward and as well as remotely located patients so that it will ultimately reduce hospital operating costs, all other communication costs and improve the quality of health service.
Improved Iterative Closest Point (ICP) Point Cloud Registration Algorithm based on Matching Point Pair Quadratic Filtering

Aiming at the problems of long computing time and poor registration accuracy in current point cloud registration, an improved ICP algorithm based on matching point pair secondary filtering was proposed, which combined ground segmentation and point cloud filtering algorithm for pre-processing. Firstly, ground segmentation is performed on the point cloud data obtained by Lidar, and ground points are filtered. Next, Kdtree_ICP is used for point cloud registration, and the abnormal matching point pairs obtained by Kdtree search are filtered during the matching process. Finally, the point cloud data of outdoor ground is used for experimental verification. Experimental results show that the proposed method greatly improves the computational speed and accuracy, and the algorithm is stable and reliable.