Constant-Envelope Multi-Level Chirp Modulation: Properties, Receivers, and Performance

Constant envelope multi-level chirp modulations, with and without memory, are considered for data transmission. Specifically, three sub-classes referred to as symbol-by-symbol multi-level chirp modulation, full-response phase-continuous multi-level chirp modulation and full-response multi-mode phase-continuous multi-level chirp modulation are considered. These modulated signals are described, illustrated, and examined for their properties. The ability of these signals to operate over AWGN is assessed using upper bounds on minimum Euclidean distance as a function of modulation parameters. Coherent and non-coherent detection of multi-level chirp signals in AWGN are considered and optimum and sub-optimum receiver structures are derived. The performance of these receivers have been assessed using upper and lower bounds as a function of SNR, modulation parameters, modulation levels, decision symbol locations, and observation length of receiver. Optimum multi-level chirp modulations have been determined using numerical minimization of symbol error rate. Closed-form expressions are derived for estimating the performance of multi-level chirp signals over several practical fading channels. Finally, spectral characteristics of digital chirp signals are presented and illustrated

M-ary Chirp Modulation for Data Transmission

M-ary chirp modulations, both discontinuous- and continuous-phase, for M-ary data transmission are proposed and examined for their error rate performances in additive, white, Gaussian noise (AWGN) channel. These chirp modulated signals are described and illustrated as a function of time and modulation parameters. M-ary chirp modula­ tion with discontinuous phase is first proposed and then the M-ary Continuous Phase Chirp Modulation (MCPCM) is considered. General descriptions of these modula­ tion systems are given and properties of signals representing these modulations are given and illustrated. Optimum algorithms for detection of these signals in AWGN are derived and structures of optimum receivers are identified. Using the minimum Euclidean distance criterion in signal-space; upper bounds on Signal-to-Noise Ratio (SNR) gain relative to Multiple Phase Shift Keying (MPSK) are established for 2-. *4-, and 8-ary MCPCM systems. It is observed that the maximum likelihood coherent and non-coherent receivers for MCPCM are non-linear and require multiple-symbol observations. Since symbol error probability performance analyses of these receivers are too complex to perform, union upper bounds on their performances are derived and illustrated as a function of SNR, number of observation symbols, and modulation parameters for MCPCM. Optimum 2-, 4-, and 8-ary modulation schemes that mini­ mize union upper bound on symbol error rates have been determined and illustrated. Our results show that 2-, 4-, and 8-ary optimum coherent MCPCM systems, with 5-symbol observation length, offer 1.6 dB, 3.6 dB, and 8 dB improvements relative to 2-ary, 4-ary, and 8-ary PSK systems, respectively. Also, it is shown that opti­ mum 2-ary and 4-ary non-coherent MCPCM systems can outperform 2-ary and 4-ary coherent PSK systems, respectively

A single-phase compact-sized matrix converter with symmetrical bipolar buck and boost output voltage control

The development of single-phase symmetrical bipolar voltage gain matrix converters (MC) is growing rapidly as they find their application in power systems for dynamic restoration of line voltages, high voltage AC–DC converters, and variable frequency controllers for many industrial processes. However, the existing trend in matrix converter technology is a buck–boost operation that has inherently serious issues of high voltage and current surges or stresses. This is a big source of the high voltage and current rating of semiconductor switching devices. There is also a problem of high ripples both for voltage as well for current, requiring large size of filtering capacitors and inductors. The non-symmetrical control of the voltage gain increases the control complication. A large count of operating transistors is critical regarding their cost, size, and power conversion losses, as the space and cost required by their gate control circuits are much larger than the size and cost of the switching transistors. Thus, in this research work, a new single-phase MC is introduced only employing six fully controlled switching devices, ensuring similar operation or outputs as is obtained from the existing topologies that require the use of eight or more fully controlled switching devices, and the reduction by two or more switching transistors helps to compact the overall size and lower the overall cost. The separation in its voltage buck and boost operation enables smooth control of the voltage gain through duty cycle control. The low values of the voltage and current surges reduce the power rating and losses of the switching devices. The flow of the current in the filtering inductor is kept unidirectional to avoid the current interruption and reversal problem once the operation of the converter is abruptly switched from inverting to non-inverting and vice versa. All these factors are comprehensively detailed through the circuit’s description and comparative analysis. Simulation and practical results are presented to confirm the effectiveness of the developed circuit topology

Introducing adaptive machine learning technique for solving short-term hydrothermal scheduling with prohibited discharge zones

The short-term hydrothermal scheduling (STHTS) problem has paramount importance in an interconnected power system. Owing to an operational research problem, it has been a basic concern of power companies to minimize fuel costs. To solve STHTS, a cascaded topology of four hydel generators with one equivalent thermal generator is considered. The problem is complex and non-linear and has equality and inequality constraints, including water discharge rate constraint, power generation constraint of hydel and thermal power generators, power balance constraint, reservoir storage constraint, initial and end volume constraint of water reservoirs, and hydraulic continuity constraint. The time delays in the transport of water from one reservoir to the other are also considered. A supervised machine learning (ML) model is developed that takes the solution of the STHTS problem without PDZ, by any metaheuristic technique, as input and outputs an optimized solution to STHTS with PDZ and valve point loading (VPL) effect. The results are quite promising and better compared to the literature. The versatility and effectiveness of the proposed approach are tested by applying it to the previous works and comparing the cost of power generation given by this model with those in the literature. A comparison of results and the monetary savings that could be achieved by using this approach instead of using only metaheuristic algorithms for PDZ and VPL are also given. The slipups in the VPL case in the literature are also addressed

Dragonfly algorithm-based optimization for selective harmonics elimination in cascaded H-bridge multilevel inverters with statistical comparison

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms’ search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

A Smart ANN-Based Converter for Efficient Bidirectional Power Flow in Hybrid Electric Vehicles

Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller

A Smart ANN-Based Converter for Efficient Bidirectional Power Flow in Hybrid Electric Vehicles

Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller

Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar

Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved