Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of LDPC Coded Massive MIMO-OFDM System

Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) wireless communication is well known in the literature. However, the problems occur in classical MIMO system can be overcome with large number of array antennas such systems, termed as Massive MIMO. But, the latency may be more for these systems using traditional equalizers such as Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). Hence, this paper proposes LDPC coded Massive MIMO OFDM system using Approximate Message Passing (AMP) equalizer. The performance of the proposed system is analysed through simulations. In this simulation, different transmit and receive antennas (64,128), (64,256), (64,512) and (64, 1024) and 16QAM are used. Finally, the performance of LDPC coded and uncoded massive MIMO OFDM using AMP equalizer is analyzed in comparison with ZF and MMSE equalizers using BER and latency as performance measures.
Particulate Characterization of CNG Fuelled Public Transport Vehicles at Traffic Junctions

ABSTRACTCompressed natural gas (CNG) is considered as cleaner fuel compared to gasoline and has emerged as an important alternate transport fuel in view of its abundant availability globally. CNG has been implemented in mass transport sector globally. This study explores the emission characteristics of two different classes of CNG operated vehicles used for public transportation in Indian Cities. One is a heavy-duty CNG bus and the other one is a light-duty CNG tempo. Both are very popular commuting vehicles in Indian cities. The number of such CNG vehicles is quite large therefore their emissions affect the health of traffic policemen, who are responsible for regulating the traffic during their long duty-hours. They are continuously exposed to harmful regulated and unregulated pollutants from such vehicles. Regulated gaseous emissions and particulate bound polyaromatic hydrocarbons (PAHs) were determined for both vehicles at different engine speeds at no load, which simulates typical traffic junction scenario. For the heavy-duty CNG operated bus, particulate number-size distribution and mass-size distributions were also evaluated. Carbon monoxide (CO), Hydrocarbons (HC) and nitric oxide (NO) emissions were found to be higher for the CNG operated bus compared to the CNG tempo. Particle bound PAHs were quite significant for CNG fuelled vehicles however they reduced at higher engine speeds. Particulate number emissions increased with increasing engine speed, especially in the accumulation mode (50–250 nm) for the heavy-duty CNG bus. Despite moderate increase in particle number emissions, particulate mass contributions of the accumulation mode particles increased significantly with increasing engine speed.
Comparative analysis of ROS based 2D and 3D SLAM algorithms for Autonomous Ground Vehicles

Simultaneous localization and mapping algorithm (SLAM) is a technique for estimating sensor motion and reconstructing the structure in an unknown environment. SLAM is extensively used in the concept of autonomous driving or navigation which helps robotic vehicles to move autonomously. This paper presents a comparative analysis of different ROS based 2D SLAM algorithms such as GMapping, Hector SLAM, Karto SLAM and 3D SLAM algorithm such as Real-Time Appearance-Based Mapping. Scenarios with a scaling factor resembling an underground parking was built in real-time and simulation for analysis and validation purposes. The maps generated by applying the respective SLAM algorithms in real-time and simulation was analyzed and validated quantitatively based on quality and accuracy using the Structure Similarity Index.
CoLoRa: Enabling Multi-Packet Reception in LoRa

LoRa, more generically Low-Power Wide Area Network (LPWAN), is a promising platform to connect Internet of Things. It enables low-cost low-power communication at a few kbps over upto tens of kilometers with a 10-year battery lifetime. However, practical LPWAN deployments suffer from collisions, given the dense deployment of devices and wide coverage area. We propose CoLoRa, a protocol to decompose large numbers of concurrent transmissions from one collision in LoRa networks. At the heart of CoLoRa, we utilize packet time offset to disentangle collided packets. CoLoRa incorporates several novel techniques to address practical challenges. (1) We translate time offset, which is difficult to measure, to frequency features that can be reliably measured. (2) We propose a method to cancel inter-packet interference and extract accurate feature from low SNR LoRa signal. (3) We address frequency shift incurred by CFO and time offset for LoRa decoding. We implement CoLoRa on USRP N210 and evaluate its performance in both indoor and outdoor networks. CoLoRa is implemented in software at the base station and it can work for COTS LoRa nodes. The evaluation results show that CoLoRa improves the network throughput by 3.4× compared with Choir and by 14× compared with LoRaWAN.
Grocery Delivery or Customer Pickup—Influences on Energy Consumption and CO2 Emissions in Munich

The number of supermarkets offering grocery delivery has increased in recent years. Many studies conclude that CO2 emission savings result from this concept. Since the delivery of groceries also consumes energy and produces emissions, break-even points can be calculated, where the delivery is environmentally beneficial compared to customer pickup. In this paper, influences of differing vehicle use on break-even points for savings of energy and CO2 emissions are analyzed for the case of Haidhausen Süd, a district in Munich, Germany. Internal combustion engine and electric vehicles are investigated to depict current as well as future trends. After an introduction to the methodology used, the potential to save energy and CO2 emissions related to the delivery of groceries in the chosen district of Munich are evaluated. Subsequently, influences on the break-even points are presented and discussed. As the results show, a delivery of groceries leads to energy and carbon dioxide savings in a wide range of private vehicle use for grocery shopping trips. Nevertheless, if the complete customer vehicle fleet is electrified, the use of delivery vehicles with an internal combustion engine could cause an additional environmental impact at the current modal split for shopping trips in Germany.
Paddlewheel SBU based Zn MOFs: Syntheses, Structural Diversity, and CO2 Adsorption Properties

Four Zn metal–organic frameworks (MOFs), {[Zn2(2,6-ndc)2(2-Pn)]·DMF}n (1), {[Zn2(cca)2(2-Pn)]·DMF}n (2), {[Zn2(thdc)2(2-Pn)]·3DMF}n (3), and {[Zn2(1,4-ndc)2(2-Pn)]·1.5DMF}n (4), were synthesized from zinc nitrate and N,N′-bis(pyridin-2-yl)benzene-1,4-diamine (2-Pn) with naphthalene-2,6-dicarboxylic acid (2,6-H2ndc), 4-carboxycinnamic acid (H2cca), 2,5-thiophenedicarboxylic acid (H2thdc), and naphthalene-1,4-dicarboxylic acid (1,4-H2ndc), respectively. MOFs 1–4 were all constructed from similar dinuclear paddlewheel {Zn2(COO)4} clusters and resulted in the formation of three kinds of uninodal 6-connected non-interpenetrated frameworks. MOFs 1 and 2 suit a topologic 48·67-net with 17.6% and 16.8% extra-framework voids, respectively, 3 adopts a pillared-layer open framework of 48·66·8-topology with sufficient free voids of 39.9%, and 4 features a pcu-type pillared-layer framework of 412·63-topology with sufficient free voids of 30.9%. CO2 sorption studies exhibited typical reversible type I isotherms with CO2 uptakes of 55.1, 84.6, and 64.3 cm3 g−1 at 195 K and P/P0 =1 for the activated materials 1′, 2′, and 4′, respectively. The coverage-dependent isosteric heat of CO2 adsorption (Qst) gave commonly decreased Qst traces with increasing CO2 uptake for all the three materials and showed an adsorption enthalpy of 32.5 kJ mol−1 for 1′, 38.3 kJ mol−1 for 2′, and 23.5 kJ mol−1 for 4′ at zero coverage.
Determining Method-Call Sequences for Object Creation in C++

Unit tests in object-oriented programming languages must instantiate objects as an essential part of their set-up. Finding feasible method-call sequences for object creation and selecting a most desirable sequence can be a time-consuming challenge for developers in large C++ projects. This is caused by the intricacies of the C++ language, complexity of recursive object creation, and a large number of alternatives. We confirm the significance of the problem by analysis of 7large C++ projects and a survey with 143 practitioners. We then design an approach for recommending method-call sequences for object creation that align with criteria gathered by the survey. Our approach exploits accurate and efficient compiler-based source code analysis to build an object dependency graph that is processed by a divide-and-conquer algorithm.An evaluation on a large industrial project shows that our tool finds solutions that require in 99% of 1104 cases identical or fewer objects compared to manually crafted solutions. Developer feedback and manual analysis confirm these results. Moreover, solutions found by our tool require up to 6 times fewer objects on average compared to approaches from prior work.
Bio-Magnetic/Eddy-Current Sensor Design for Biological Object Detection

Estimations of electrical conductivity are essential for biological object detection. This paper presents a distributed current source (DCS) based method to develop a bio-magnetic/eddy-current (Bio-M/EC) sensor for biological object detection. The DCS method directly calculates magnetic flux densities (MFDs) generated by the eddy-current induced in the biological object, which accounts for the effects of displacement current. The electromotive force (EMF) of the differential coil system is formulated in a closed-form solution to numerically investigate the effects of sweep frequency analysis applied on the Bio-M/EC sensor for biological object classification and tumor detection. The proposed method, along with a prototype of Bio-M/EC sensor, has been experimentally compared using sweep frequency analysis for detecting meat and bone.
Sliding-mode Control with Multi-sensor Fusion for orientation of Spherical Motion Platform

This paper presents control system design for multi-degree-of-freedom (DOF) motion platform referred here to as spherical motion platform (SMP) based on compliant spherical wheels. The SMP is capable of providing smooth unlimited rotational motions along all axes and three DOFs translational motions. The various motion platforms have been explored and applied to industry for virtual reality. However, existing motion platforms have limited motion range and motion coupling. In this paper, the full-scaled SMP is developed as practical applications for virtual reality. In addition, the compliant mechanism of spherical wheels helps the SWMP controlled without slipping between a cockpit sphere and the spherical wheels. In addition, for orientation measurement, sensor fusion of both an optical sensor and an inertial measurement unit (IMU) is utilized to control the cockpit rotation without mechanical contact and compensates accumulated error of the IMU. A sliding mode controller with active driving control is designed to deal with disturbances including uncertain dynamic model and coupling effect, respectively. Finally, the experimental results show the sensing and control operation are successfully implemented using the real scale SMP and can be applied to virtual reality simulation as motion simulator.
Cardiorespiratory interactions during three different temperatures – a case report

Our study explored the cardiorespiratory interactions during three different ambient temperatures. We established coupling functions of cardiorespiratory interactions by applying dynamic Bayesian inference to investigate if and how thermoregulation affects those oscillators. Our preliminary results indicate that both, the coherence and coupling between cardiorespiratory oscillations change with individual level of temperature stress.
Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems

Federated learning (FL) is a distributed deep learning method that enables multiple participants, such as mobile and IoT devices, to contribute a neural network while their private training data remains in local devices. This distributed approach is promising in the mobile systems where have a large corpus of decentralized data and require high privacy. However, unlike the common datasets, the data distribution of the mobile systems is imbalanced which will increase the bias of model. In this article, we demonstrate that the imbalanced distributed training data will cause an accuracy degradation of FL applications. To counter this problem, we build a self-balancing FL framework named Astraea, which alleviates the imbalances by 1) Z-score-based data augmentation, and 2) Mediator-based multi-client rescheduling. The proposed framework relieves global imbalance by adaptive data augmentation and downsampling, and for averaging the local imbalance, it creates the mediator to reschedule the training of clients based on Kullback–Leibler divergence (KLD) of their data distribution. Compared with FedAvg, the vanilla FL algorithm, Astraea shows +4.39 and +6.51 percent improvement of top-1 accuracy on the imbalanced EMNIST and imbalanced CINIC-10 datasets, respectively. Meanwhile, the communication traffic of Astraea is reduced by 75 percent compared to FedAvg.
Pulse Transit Time-Pulse Wave Analysis Fusion Based on Wearable Wrist Ballistocardiogram for Cuff-Less Blood Pressure Trend Tracking

The objective of this study was to investigate the efficacy of pulse transit time (PTT)-pulse wave analysis (PWA) fusion in cuff-less blood pressure (BP) trend tracking based on the wearable wrist ballistocardiogram (BCG). We constructed PTT and 36 candidate BCG PWA features based on the BCG and photoplethysmogram (PPG) signals acquired at the wrist. We performed a model-based analysis to select 6 candidate BCG PWA features most sensitive to BP. We aggregated the 6 BCG PWA features into novel predictors of diastolic, systolic, and pulse BP (DP, SP, and PP) orthogonal to PTT by covariance-maximizing dimensionality reduction. Then, we evaluated and compared the efficacy of BCG PTT and BCG PTT-PWA fusion in tracking the trend of DP, SP, and PP. Unique innovations of this study, generalizable to a range of physiological signals beyond the BCG, are (i) the systematic selection of PWA features based on model-based analysis and (ii) the development of PWA-based BP predictors orthogonal to PTT. Using the experimental wrist BCG and PPG signals collected from 23 human subjects, we demonstrated that (i) BCG PTT-PWA fusion may significantly outperform BCG PTT; (ii) PTT may play the primary role in tracking BP while BCG PWA features may still have an independent and complementary value (especially in tracking PP); and (iii) model-based selection of BCG PWA features can enhance the robustness of BP trend tracking based on BCG PTT-PWA fusion.
Control System Loop-Shaping as a Mathematical Optimization Problem: An Ensemble of Models

This paper proposes an extension to the established loop-shaping method, where the loop-shaping method is modeled mathematically and solved as a mathematical optimization program. In this process, a novel cost function is proposed and employed as the objective of optimization in the controller tuning problem. This cost function evaluates different characteristics of the system in the frequency domain, such as the stability margins and the operability bandwidth. These characteristics reflect the quality of load disturbance rejection, set-point tracking, and operability bandwidth of the control system. Consequently, the proposed technique circumvents running a time-domain simulation completely. More importantly, the proposed technique can be used with any controller structure. The generic cost function can be customized to fit any control objective and application. This is conducted in the second half of this paper, with the aim of obtaining a system with near-optimum time-domain performance with respect to the integral of time times absolute error (ITAE) criterion. At the same time, the control system obtained with the customized cost function has better operability bandwidth than that of a system optimized in the time-domain for the ITAE criterion. The proposed optimization model is analyzed for two plant models: first order and second order plus dead-time (FOPDT), (SOPDT), respectively.
Wearable Scanner Platform Based on Fiber Sensor Array for Real Time Breath Detection

In the near future wearable sensors when integrated into garments will have a significant impact on the future of digital health care. A new wireless T-shirt based on non-invasive network sensors was developed and tested recently as a scanner for real time breath detection of mainly male gender volunteers. The sensor is made of flexible glass fiber antenna emitting at 2.45 GHz and connected to a Bluetooth transmitter. The scanner is a network of six sensors integrated into a stretchable textile and placed in a well defined position on the thorax and on the abdomen. The breathing signal is extracted from the received signal strength indicator (RSSI) signal emitted from the sensors array and detected by a base station. In this work, we tested the feasibility assessment for breath monitoring of five female gender volunteers of different age and physical morphology in standing positions using the designed male-shape T-shirt. As a result, the wearable scanner detected successfully the breathing rates of the female volunteers with a frequency varying from 0.27 Hz to 0.40 Hz.
NV-SP: A New High Performance and Low Energy NVM-Based Scratch Pad

Non-Volatile Memory technologies are rising as a candidate for a universal memory. NVMs offer solutions for the high power consumption that contemporary memory suffers from. Hence, we propose augmenting the traditional SRAM cache with an additional NVM device instead of entirely replacing SRAM with NVM. The L1 instruction-cache is augmented with a Non-Volatile Scratch-Pad, coined NV-SP, that stores instructions causing the highest number of misses. Experiments were evaluated for performance and energy of the SRAM I-cache and the NV-SP when implemented using Magnetic RAM and Phase-Changing RAM technologies. Results have shown that MRAM NV-SP had effectively improved the performance of the I-cache.
Blockchain based Consensus Checking in Cloud Storage

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm’s data consistency, efficiency, and reliability.
Comparison of Orthogonal vs. Union of Subspace Based Pilots for Multi-Cell Massive MIMO Systems

In this paper, we analytically compare orthogonal pilot reuse (OPR) with union of subspace based pilots in terms of channel estimation error and achievable throughput. In OPR, due to the repetition of the same pilot sequences across all cells, inter-cell interference (ICI) leads to pilot contamination, which can severely degrade the performance of cell-edge users. In our proposed union of subspace based method of pilot sequence design, pilots of adjacent cells belong to distinct sets of orthonormal bases. Therefore, each user experiences a lower level of ICI, but from all users of neighboring cells. However, when the pilots are chosen from mutually unbiased orthonormal bases (MUOB), the ICI power scales down exactly as the inverse of the pilot length, leading to low ICI. Further, as the number of users increases, it may no longer be feasible to allot orthogonal pilots to all users within a cell. We find that, with limited number of pilot sequences, MUOB is significantly more resilient to intra-cell interference, yielding better channel estimates compared to OPR. On the other hand, when the pilot length is larger than the number of users, while OPR achieves channel estimates with very high accuracy for some of the users, MUOB is able to provide a more uniform quality of channel estimation across all users in the cell. We evaluate the fairness of OPR vis-à-vis MUOB using the Jain’s fairness metric and max-min index. Via numerical simulations, we observe that the average fairness as well as convergence rates of utility metrics measured using MUOB pilots outperform the conventional OPR scheme.
Arduino based Automated Sports Court Drawing Bot

Stadium maintenance staff are one of those people who work hard to maintain the court correctly. One of the items that often require rework is the lines drawn on the court for maintenance of track or boundaries or even the entire court when it is football stadium etc. Besides, for indoor stadiums that host volleyball, basketball, tennis, etc., lines need to be drawn. Redrawing the court after each match is one of the major problems of concern. In such a scenario, a system is proposed that can draw the court lines autonomously. The objective of this research work is to present a design of an autonomous sports court drawing bot that can draw the linear lines automatically which can aid in the maintenance work of the sports stadium.
e-Sight: Real-time cloud platform for visualizing edge transport infrastructure information

The huge amount of streaming information generated by the new wave of edge devices that are used to monitor a plethora of everyday aspects, prompts the need for efficient techniques to handle, process, aggregate, and visualize this stream of data. One such field is the continuous transport infrastructure monitoring, where smartphones inside driving vehicles act as edge sensing and computing nodes to measure the quality of the road pavement among other things. Although the location accuracy of such devices is within the acceptable bounds, the accumulated error can lead to large deviations from the location of interest, reducing the measurement credibility. The data represent a geographically vast infrastructure network in need of real-time, bird-eye visualization. This paper describes the implementation of such a real-time platform and the challenges the task provides. By implementing relatively new open-source cross-platform environments, the platform is scalable and versatile, reducing the costs associated with cloud-based implementation systems.