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The investigation of new memory schemes, neural networks, computer systems, and many other improved electronic devices is very important for the future generation’s electronic circuits and for their widespread application in all the areas of industry. In this respect, the analysis of new efficient and advanced electronic elements and circuits is an essential field of highly developed electrical and electronic engineering. The resistance-switching phenomenon, observed in many amorphous oxides, has been investigated since 1970 and is a promising technology for constructing new electronic memories. It has been established that such oxide materials have the ability for changing their conductance in accordance with the applied voltage, and for memorizing their state for long-time interval. Similar behaviour has been predicted for the memristor element by Leon Chua in 1971. The memristor is proposed in accordance with symmetry considerations and the relationships between the four basic electric quantities—electric current i, voltage v, charge q, and magnetic flux Ψ. The memristor is an essential passive one-port element together with the resistor, inductor, and capacitor. The Williams HP research group has made a link between resistive switching devices and the memristor proposed by Chua. A number of scientific papers related to memristors and memristor devices have been issued, and several memristor models have been proposed. The memristor is a highly nonlinear component. It relates the electric charge q and the flux linkage, expressed as a time integral of the voltage. The memristor element has the important capability for remembering the electric charge passed through its cross-section and its respective resistance, when the electrical signals are switched off. Due to its nano-scale dimensions, non-volatility, and memorizing properties, the memristor is a sound potential candidate for application in computer high-density memories, artificial neural networks, and many other electronic devices.

Memristor --- Titanium dioxide memristor --- Nonlinear ionic drift --- Memristor model --- Window function --- Voltage-dependent nonlinearity --- Non-volatility --- Memristor circuits --- Resistance-switching --- Memristor synaptic circuits --- Memristor Neural Network

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This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR

Vehicle-to-X communications --- Intelligent Transport Systems --- VANET --- DSRC --- Geobroadcast --- multi-sensor --- fusion --- deep learning --- LiDAR --- camera --- ADAS --- object tracking --- kernel based MIL algorithm --- Gaussian kernel --- adaptive classifier updating --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- joystick --- two-wheeled --- terrestrial vehicle --- path planning --- infinity norm --- p-norm --- kinematic control --- navigation --- actuation systems --- maneuver algorithm --- automated driving --- cooperative systems --- communications --- interface --- automated-manual transition --- driver monitoring --- visual tracking --- discriminative correlation filter bank --- occlusion --- sub-region --- global region --- autonomous vehicles --- driving decision-making model --- the emergency situations --- red light-running behaviors --- ethical and legal factors --- T-S fuzzy neural network --- road lane detection --- map generation --- driving assistance --- autonomous driving --- real-time object detection --- autonomous driving assistance system --- urban object detector --- convolutional neural networks --- machine vision --- biological vision --- deep learning --- convolutional neural network --- Gabor convolution kernel --- recurrent neural network --- enhanced learning --- autonomous vehicle --- crash injury severity prediction --- support vector machine model --- emergency decisions --- relative speed --- total vehicle mass of the front vehicle --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- drowsiness detection --- smart band --- electrocardiogram (ECG) --- photoplethysmogram (PPG) --- recurrence plot (RP) --- convolutional neural network (CNN) --- squeeze-and-excitation --- residual learning --- depthwise separable convolution --- blind spot detection --- machine learning --- neural networks --- predictive --- vehicle dynamics --- electric vehicles --- FPGA --- GPU --- parallel architectures --- optimization --- panoramic image dataset --- road scene --- object detection --- deep learning --- convolutional neural network --- driverless --- autopilot --- deep leaning --- object detection --- generative adversarial nets --- image inpainting --- n/a

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Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.

memristor --- artificial synapse --- neuromorphic computing --- memristor-CMOS hybrid circuit --- temporal pooling --- sensory and hippocampal responses --- cortical neurons --- hierarchical temporal memory --- neocortex --- memristor-CMOS hybrid circuit --- defect-tolerant spatial pooling --- boost-factor adjustment --- memristor crossbar --- neuromorphic hardware --- memristor --- compact model --- emulator --- neuromorphic --- synapse --- STDP --- pavlov --- neuromorphic systems --- spiking neural networks --- memristors --- spike-timing-dependent plasticity --- RRAM --- vertical RRAM --- neuromorphics --- neural network hardware --- reinforcement learning --- AI --- neuromorphic computing --- multiscale modeling --- memristor --- optimization --- RRAM --- simulation --- memristors --- neuromorphic engineering --- OxRAM --- self-organization maps --- synaptic device --- memristor --- neuromorphic computing --- artificial intelligence --- hardware-based deep learning ICs --- circuit design --- memristor --- RRAM --- variability --- time series modeling --- autocovariance --- graphene oxide --- laser --- memristor --- crossbar array --- neuromorphic computing --- wire resistance --- synaptic weight --- character recognition --- neuromorphic computing --- Flash memories --- memristive devices --- resistive switching --- synaptic plasticity --- artificial neural network --- spiking neural network --- pattern recognition --- strongly correlated oxides --- resistive switching --- neuromorphic computing --- transistor-like devices --- artificial intelligence --- neural networks --- resistive switching --- memristive devices --- deep learning networks --- spiking neural networks --- electronic synapses --- crossbar array --- pattern recognition

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The field of Brain–Computer Interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of faster and more reliable assistive technologies based on direct links between the brain and an external device. Novel applications of BCIs have also been proposed, especially in the area of human augmentation, i.e., enabling people to go beyond human limitations in sensory, cognitive and motor tasks. Brain-imaging techniques, such as electroencephalography, have been used to extract neural correlates of various brain processes and transform them, via machine learning, into commands for external devices. Brain stimulation technology has allowed to trigger the activation of specific brain areas to enhance the cognitive processes associated to the task at hand, hence improving performance. BCIs have therefore extended their scope from assistive technologies for people with disabilities to neuro-tools for human enhancement. This Special Issue aims at showing the recent advances in BCIs for human augmentation, highlighting new results on both traditional and novel applications. These include, but are not limited to, control of external devices, communication, cognitive enhancement, decision making and entertainment.

Brain–Computer Interface (BCI) --- speller --- Graphical User Interface (GUI) --- SSVEP --- P300 --- MI --- hybrid --- human performance --- performance prediction --- indoor room temperature --- office-work tasks --- electroencephalography (EEG) --- brain computer interface --- complete locked-in state --- communication --- Artificial Neural Network --- 20-questions-game --- augmented cognition --- brain–computer interfaces --- superintelligence --- heuristic search --- electroencephalography --- brain-computer interfaces --- waveform --- p300 --- SIFT --- PE --- MP --- SHCC --- n/a

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The Special Issue “Refrigeration Systems and Applications” aims to encourage researchers to address the concerns associated with climate change and the sustainability of artificial cold production systems, and to further the transition to the more sustainable technologies and methodologies of tomorrow through theoretical, experimental, and review research on the different applications of refrigeration and associated topics.

energy consumption --- thermal load --- domestic refrigeration system --- two-phase ejector --- CO2 --- transcritical system --- exergy analysis --- irreversibility --- transiting exergy --- magnetic refrigeration --- magnetocaloric effect --- LaFe13 ? x ? yCoxSiy --- gadolinium --- artificial neural network --- modelling --- twin-screw refrigeration compressor --- CFD --- thermodynamic performance --- P-? indicator diagram --- chiller energy consumption --- artificial neural network (ANN) --- HVAC --- nanofluids --- caloric cooling --- barocaloric --- solid-state cooling --- acetoxy silicone rubber --- Cu nanofluids --- ethylene-glycol nanofluids --- heat transfer --- absorption refrigeration system --- thermodynamic analysis --- calculation model --- LiCl-H2O --- LiBr-H2O --- off-design behaviors --- phase change material --- thermal energy storage --- vapor compression system --- HVAC --- energy efficiency --- distillation --- heat pump --- hydraulic turbine --- refrigerant reclamation --- R-410A --- superheating --- r1234yf --- r1234ze(e) --- HFO --- ejector refrigeration technology --- magnetic refrigeration --- magneto-caloric effect --- coefficient of performance --- eddy currents --- experimental --- parasitic heat load --- modelling