Digital health tools for the passive monitoring of depression: a systematic review of methods

The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.

MEASUREMENT OF WIND POWER AND ENERGY USING AN ANEMOMETER WITHOUT MOVING PARTS

The paper deals with the application of a newly developed anemometer without moving parts. It is digitized and has built-in electronics that convert the vibrations of two aluminum fixed frames into two digital signals: one, which shows the strength (speed absolute value)) of the wind, and the other, which shows its direction. Both of these signals are used to calculate wind power and energy. Earlier works have shown that the two-bit stochastic digital measurement method overcomes (eliminates) the problem of the offset of the analog adder. The authors of this paper apply this idea to the digital output of the sensor, where the role of the offset of the analog adder is taken over by the integral nonlinearity of the digital output of the anemometer. The first step in this direction is digitally dithering the sensor output. This principle is presented in detail, as well as a rough estimate of the accuracy gain in measuring wind energy. The obtained result shows that the accuracy in measuring wind energy is not worse than the limit accuracy in the case of a cup anemometer that generates sinusoidal voltage.

Hybrid PWM Techniques for a DCM-232 Three-Phase Transformerless Inverter with Reduced Leakage Ground Current

Pulse Width Modulation (PWM) strategies are crucial for controlling DC–AC power converters. In particular, transformerless inverters require specific PWM techniques to improve efficiency and to deal with leakage ground current issues. In this paper, three hybrid PWM methods are proposed for a DCM-232 three-phase topology. These methods are based on the concepts of carrier-based PWM and space vector modulation. Calculations of time intervals for active and null vectors are performed in a conventional way, and the resulting waveforms are compared with a carrier signal. The digital signals obtained are processed using Boolean functions, generating ten signals to control the DCM-232 three-phase inverter. The performance of the three proposed PWM methods is evaluated considering the reduction in leakage ground current and efficiency. The proposed modulation techniques have relevant performances complying with international standards, which make them suitable for transformerless three-phase photovoltaic (PV) inverter markets. To validate the proposed hybrid PWM strategies, numerical simulations and experimental tests were performed.

GaAs-Based Serial-Input-Parallel-Output Interfaces for Microwave Core-Chips

Microwave core-chips are highly integrated MMICs that are in charge of all the beam-shaping functions of a transmit-receive module within a phased array system. Such chips include switches, amplifiers and attenuators, phase shifters, and possibly other elements, each to be controlled by external digital signals. Given the large number of control lines to be integrated in a core-chip, the embedding of a serial to parallel interface is indispensable. Digital design in compound semiconductor technology is still rather challenging due to the absence of complementary devices and the availability of a limited number of metallization layers. Moreover, in large arrays, high chip yield and repeatability are required. This paper discusses and compares challenges and solutions for the key sub-circuits of GaAs serial to parallel converters for core-chip applications, reviewing the pros and cons of the different implementations proposed in the literature.

Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor

The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals.

Research on Comparison of LiDAR and Camera in Autonomous Driving

With the improvement of vehicles automation, autonomous vehicles become one of the research hotspots. Key technologies of autonomous vehicles mainly include perception, decision-making, and control. Among them, the environmental perception system, which can convert the physical world’s information collection into digital signals, is the basis of the hardware architecture of autonomous vehicles. At present, there are two major schools in the field of environmental perception: camera which is dominated by computer vision and LiDAR. This paper analyzes and compares the two majors schools in the field of environmental perception and concludes that multi-sensor fusion is the solution for future autonomous driving.

Improving the Performance of Distance Relay Using Wavelet Transform

With proliferation of power grids, different types of faults are more likely to occur. The purpose of system protection relays is to detect abnormal signals that indicate faults in the transmission system and to separate the fault section from the rest of the system to prevent the fault from propagating to other parts of the system. Proliferation of electronic devices led to creation of digital relays made of microprocessors. Hence, analog measurements are converted into digital signals for processing by microprocessors. Air grids are more likely to cause faults than other components of the power system; thus, disturbances affecting the system must be detected quickly and accurately. Therefore, the problem of fault detection and classification is an important factor for economic exploitation of the power grid. Accurate fault handling results in faster repair operations, better system availability, lower operating costs and timesavings. The proposed design in this study was based on detecting the type of fault caused in transmission lines. In order to improve the condition of the relays in the transmission grid, analysis of the signals reached to the relay on both sides of the line was used to detect the fault and its type. The main purpose was to quickly detect the type of fault using wavelet transform. For this purpose, the signal was sampled after the fault occurs and the feature signal was extracted after analysis by wavelet transform. These features were included in the decision tree classifier and the type of the fault was decided.

Rabinovich-Fabrikant Chaotic System and its application to Secure Communication

This paper aims for the implementation and analysis of Rabinovich-Fabrikant System and its properties where it is found that for the value of parameters having α=0.1, γ=0.077 the system was stable and it was able to Mask any kind of digital signals properly and it showed non-linearity properties which paved the path to secure communication.

Dual-Cell Patch-Clamp Recording Revealed a Mechanism for a Ribbon Synapse to Process Both Digital and Analog Inputs and Outputs

A chemical synapse is either an action potential (AP) synapse or a graded potential (GP) synapse but not both. This study investigated how signals passed the glutamatergic synapse between the rod photoreceptor and its postsynaptic hyperpolarizing bipolar cells (HBCs) and light responses of retinal neurons with dual-cell and single-cell patch-clamp recording techniques. The results showed that scotopic lights evoked GPs in rods, whose depolarizing Phase 3 associated with the light offset also evoked APs of a duration of 241.8 ms and a slope of 4.5 mV/ms. The depolarization speed of Phase 3 (Speed) was 0.0001–0.0111 mV/ms and 0.103–0.469 mV/ms for rods and cones, respectively. On pairs of recorded rods and HBCs, only the depolarizing limbs of square waves applied to rods evoked clear currents in HBCs which reversed at −6.1 mV, indicating cation currents. We further used stimuli that simulated the rod light response to stimulate rods and recorded the rod-evoked excitatory current (rdEPSC) in HBCs. The normalized amplitude (R/Rmax), delay, and rising slope of rdEPSCs were differentially exponentially correlated with the Speed (all p < 0.001). For the Speed < 0.1 mV/ms, R/Rmax grew while the delay and duration reduced slowly; for the Speed between 0.1 and 0.4 mV/ms, R/Rmax grew fast while the delay and duration dramatically decreased; for the Speed > 0.4 mV/ms, R/Rmax reached the plateau, while the delay and duration approached the minimum, resembling digital signals. The rdEPSC peak was left-shifted and much faster than currents in rods. The scotopic-light-offset-associated major and minor cation currents in retinal ganglion cells (RGCs), the gigantic excitatory transient currents (GTECs) in HBCs, and APs and Phase 3 in rods showed comparable light-intensity-related locations. The data demonstrate that the rod-HBC synapse is a perfect synapse that can differentially decode and code analog and digital signals to process enormously varied rod and coupled-cone inputs.