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.
Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite ...significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.
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.
Online social media (OSM) has emerged as the most pertinent and readily available platform for individuals to effectively express their perspectives. Users connect seamlessly in an unstructured ...network, allowing information to flow within seconds. This interconnectedness, while enabling rapid information dissemination, also opens the door to significant challenges such as misinformation, disinformation, cyberbullying, privacy concerns, polarized opinions, and digital footprints. Users on social media are active with different intentions, which could include information sharing, social connections, shaping public opinion, or launching campaigns either for or against certain organizations with specific objectives. Depending on the users’ intentions, the content can be either malicious or non-malicious. Malicious content can induce fear, uncertainty, or financial damage, leading to societal polarization or reduced revenue for commercial organizations. Therefore, the detection of users with malicious intentions is crucial to curb the spread of harmful content in society. This paper proposes a deep learning-based framework that explores social media in three different domains: users’ profiles, the content being shared, and the analysis of users’ unstructured ego-networks. The framework is established on an inductive learning-based graph neural network for a 3D analysis of social media platforms. The proposed model can serve as a benchmark and provide a baseline for researchers. The performance of the proposed model is compared with available approaches, such as SVM and LSTM. A series of experiments demonstrates the out-performance of the proposed framework on real-world PHEME dataset. Additionally, the proposed framework may also be used as an OSINT (Open-Source Intelligence) tool, depending on the availability of customized data.
•Proposed graphSAGE framework for 3D analysis of social media.•It learns user’s profile, shared contents and unstructured flow of information.•It is the kind of a benchmark in itself to the best of our knowledge, providing a baseline.•Performed series of experiments on the real-world dataset to prove the efficacy of the framework.
In this paper, we propose adversarial predictive coding (APC), a novel method for detecting abnormal events. Abnormal event detection (AED) is to identify unobserved events from a given training ...dataset consisting of normal events, and it is considered as one of the most important objectives in developing intelligent surveillance systems. Given videos and motion flows of normal events, the APC derives a normal event model by applying an adversarial prediction approach on the jointly learnt latent feature space from the videos and motion flows. Since latent space requires more abstracted and noise-free information than the raw data space, the APC can derive a more discriminative model for normal events compared with other deep learning-based AED methods which directly apply uni-modal losses such as mean square error and cross-entropy to low-level data such as video frames. We demonstrate the effectiveness of our method in detecting abnormal events using UCSD-Ped, Avenue, and UCF-Crime datasets. The experimental results show that the APC surpass the existing state-of-the-art AED methods by deriving a more discriminative model for normal events.
A CMOS image sensor (CIS) that can perform on-chip binary convolution is presented. The CIS can greatly reduce memory usage and computational complexity by directly generating a feature map for a ...binary neural network. The pixel readout of the CIS is performed in the column-parallel fashion using incremental delta-sigma analog-to-digital converters (ADCs). The CIS operates in two different modes: convolution and normal modes. When the column ADC is working in the convolution mode, it works as a first-order delta-sigma ADC and generates convolved images using a binary kernel. In the normal operation mode, the ADC is switched to a second-order delta-sigma ADC with little hardware modification and used to capture high-quality images. To demonstrate the CIS architecture, a 192 × 128-pixel CIS, which occupies an active die area of 14.44 mm 2 , is fabricated in a 0.18 μm standard CMOS process. The performance of the CIS is evaluated through measurements and network simulations. In the normal operation mode, the CIS achieves a read noise of 14.79 e - rms and a full-well capacity of 6,420 e - with a resulting dynamic range of 53 dB. The power consumptions of the CIS are 49.2 and 52.5 mW during the normal and convolution modes, respectively.
Despite recent interest in using zebrafish in human disease studies, sparked by their economics, fecundity, easy handling, and homologies to humans, the electrophysiological tools or methods for ...zebrafish are still inaccessible. Although zebrafish exhibit more significant larval-adult duality than any other animal, most electrophysiological studies using zebrafish are biased by using larvae these days. The results of larval studies not only differ from those conducted with adults but also are unable to delicately manage electroencephalographic montages due to their small size. Hence, we enabled non-invasive long-term multichannel electroencephalographic recording on adult zebrafish using custom-designed electrodes and perfusion system. First, we exploited demonstration of long-term recording on pentylenetetrazole-induced seizure models, and the results were quantified. Second, we studied skin-electrode impedance, which is crucial to the quality of signals. Then, seizure propagations and gender differences in adult zebrafish were exhibited for the first time. Our results provide a new pathway for future neuroscience research using zebrafish by overcoming the challenges for aquatic organisms such as precision, serviceability, and continuous water seepage.
A CMOS image sensor (CIS) that performs compressive sensing (CS) image encoding without compromising the operating speed and hardware complexity is presented in this article. The conversion rate and ...the frame rate of the sensor are increased by using a high-order sigma-delta (ΣΔ) analog-to-digital converter (ADC) to obtain linear measurements of selected pixel values for CS encoding. Image distortion caused by the nonconstant weight function of high-order ΣΔ ADCs is eliminated by using the proposed sampling technique. The sampling technique makes the effective weights of the inputs to the ADC equal by applying a solution for the set partition problem. In addition, unlike the existing block-based CS encoding scheme that requires complicated analog multiplexers, the proposed column-based CS encoding scheme can be implemented in a hardware-efficient column-parallel fashion, as in conventional CISs. The CIS is fabricated in a 0.11-μm 1P4M CIS process, and the sampling technique and encoding scheme are successfully demonstrated. It achieves a readout noise of 2.63 e - rms and a dynamic range of 67.96 dB with a power consumption of 56.38 mW. The resulting figure of merit is 0.375 e - ·nJ, which is the lowest among those of recently reported state-of-the-art CS-CISs.
Recently, growing interest in implantable bionics and biochemical sensors spurred the research for developing non-conventional electronics with excellent device characteristics at low operation ...voltages and prolonged device stability under physiological conditions. Herein, we report high-performance aqueous electrolyte-gated thin-film transistors using a sol-gel amorphous metal oxide semiconductor and aqueous electrolyte dielectrics based on small ionic salts. The proper selection of channel material (i.e., indium-gallium-zinc-oxide) and precautious passivation of non-channel areas enabled the development of simple but highly stable metal oxide transistors manifested by low operation voltages within 0.5 V, high transconductance of ~1.0 mS, large current on-off ratios over 10(7), and fast inverter responses up to several hundred hertz without device degradation even in physiologically-relevant ionic solutions. In conjunction with excellent transistor characteristics, investigation of the electrochemical nature of the metal oxide-electrolyte interface may contribute to the development of a viable bio-electronic platform directly interfacing with biological entities in vivo.
The interleukin-6 (IL-6) pathway is one of the mechanisms that link inflammation and angiogenesis to malignancy. Because the C-reactive protein (CRP) is a representative marker for inflammation, CRP ...has recently been associated with the progression of disease in many cancer types. The principal objective of this study was to determine the preoperative serum levels of IL-6 and CRP in gastric carcinoma, and to correlate them with disease status and prognosis.
A total of 115 patients who underwent gastrectomy were enrolled in this study. Serum levels of IL-6 were assessed via Enzyme-Linked Immuno-Sorbent Assay (ELISA), and CRP was measured via immunoturbidimetry. Histological findings included tumor size, depth of tumor invasion, lymph node (LN) metastasis, and TNM stage (6th AJCC Stage Groupings: The staging systems; Primary tumor, regional LN, metastasis).
Increases in cancer invasion and staging are generally associated with increases in preoperative serum IL-6 levels. IL-6 and CRP levels were correlated with invasion depth (P < 0.001, P = 0.001), LN metastasis (P < 0.001, P = 0.024) and TNM stage (P < 0.001, P < 0.001). The presence of peritoneal seeding metastasis is associated with IL-6 levels (P = 0.012). When we established the cutoff value for IL-6 level (6.77 pg/dL) by ROC curve, we noted significant differences in time to progression (TTP; P < 0.001) and overall survival (OS; P = 0.010). However, CRP evidenced no significance with regard to patients' TTP and OS levels. Serum IL-6 levels were correlated positively with CRP levels (r2 = 0.049, P = 0.018).
Preoperative serum IL-6 and CRP levels might be markers of tumor invasion, LN metastasis, and TNM stage. Preoperative high IL-6 levels were proposed as a poor prognostic factor for disease recurrence and overall survival in patients with gastric cancers.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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