This paper presents a neuromorphic system for visual pattern recognition realized in hardware. A new learning rule based on modified spike-timing-dependent plasticity is also presented and implemented with passive synaptic devices. The system includes an artificial photoreceptor, a Pr _0.7 Ca _0.3 MnO ₃ -based memristor array, and CMOS neurons. The artificial photoreceptor consisting of a CMOS image sensor and a field-programmable gate array converts an image into spike signals, and the memristor array is used to adjust the synaptic weights between the input and output neurons according to the learning rule. A leaky integrate-and-fire model is used for the output neuron that is built together with the image sensor on a single chip. The system has 30 input neurons that are interconnected to 10 output neurons through 300 memristors. Each input neuron corresponding to a pixel in a 5 × 6 pixel image generates voltage pulses according to the pixel value. The voltage pulses are then weighted and integrated by the memristors and the output neurons, respectively, to be compared with a certain threshold voltage above which an output neuron fires. The system has been successfully demonstrated by training and recognizing number images from 0 to 9.

Cardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient’s survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases. A noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia. A portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%. Current home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

We demonstrate a multispectral double-image-based cryptosystem that exploits only a tiny number of random white noise samples for proper decryption. Primarily, one of the two downsampled images is converted into the phase function after being shuffled by Arnold transform (AT), while the other image is modulated as an amplitude-based image after AT. Consecutively, a full double-image encryption can be achieved by employing classical double random phase encryption (DRPE) technique in the fractional Fourier transform domain with corresponding fractional orders. In this study, the encrypted complex data is randomly sampled via compressive sensing (CS) framework by which only 25% of the sparse white noise samples are being reserved to realize decryption with zero or small errors. As a consequence, together with correct phase keys, fractional orders and proper inverse AT operators, $l_p$ minimization must be utilized to decrypt the original information. Thus, in addition to the perfect image reconstruction, the proposed cryptosystem provides an additional layer of security to the conventional DRPE system. Both the mathematical and numerical simulations were carried out to verify the feasibility as well as the robustness of the proposed system. The simulation results are presented in order to demonstrate the effectiveness of the proposed system. To the best of our knowledge, this is the first report on integrating CS with encrypted complex samples for information security.

This brief presents a second-order incremental delta-sigma analog-to-digital converter (ADC) for CMOS image sensors (CISs). The ADC that employs a cascade of integrators with a feedforward architecture uses only one operational transconductance amplifier (OTA) by sharing the OTA between the first and second stages of the modulator. Further power and area savings are achieved by using a self-biasing amplifier and the proposed level-shifting technology, which allows active signal summation at the quantizer input node without using an additional OTA. Fabricated in the 0.18-μm CIS process, the 10-bit ADC occupies a die area of 0.002 mm ² and consumes 29.5 μW from a 1.8-V supply. The measured differential nonlinearity and integral nonlinearity are less than +0.22/-0.2 and +0.71/-0.89 LSB, respectively. Operating at 20 MS/s, the ADC provides signal-to-noise-distortion ratios of 57.7 and 62.3 dB for signal bandwidths of 156.25 and 78.125 kHz, respectively.

This paper presents a new method for three-dimensional (3D) scene acquisition via reconstruction with multispectral information and its Fourier-based encryption using computational integral imaging, by which the field of view, resolution, and information security are increased, respectively. The color imaging sensors covered with a Bayer color filter array captures elemental images (EI) at different spectral bands (400 and 700 nm intervals in the visible spectrum). Subsequently, double random phase encryption (DRPE) in the Fourier domain is employed on Bayer formatted EI to encrypt the captured 3D scene. Proper 3D object reconstruction only can be achieved by applying inverse decryption and a geometric ray backpropagation algorithm on the encrypted EI. Further, the high-resolution multispectral 3D scene can be visualized by using various adaptive interpolation algorithms. To objectively evaluate our proposed method, we carried out computational experiments for 3D object sensing, reconstruction, and digital simulations for DRPE. Experiment results validate the feasibility and robustness of our proposed approach, even under severe degradation.