EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing

Spiking neural networks (SNNs) have attracted considerable attention as third-generation artificial neural networks, known for their powerful, intelligent features and energy-efficiency advantages. These characteristics render them ideally suited for edge computing scenarios. Nevertheless, the current mapping schemes for deploying SNNs onto neuromorphic hardware face limitations such as extended execution times, low throughput, and insufficient consideration of energy consumption and connectivity, which undermine their suitability for edge computing applications. To address these challenges, we introduce EdgeMap, an optimized mapping toolchain specifically designed for deploying SNNs onto edge devices without compromising performance. EdgeMap consists of two main stages. The first stage involves partitioning the SNN graph into small neuron clusters based on the streaming graph partition algorithm, with the sizes of neuron clusters limited by the physical neuron cores. In the subsequent mapping stage, we adopt a multi-objective optimization algorithm specifically geared towards mitigating energy costs and communication costs for efficient deployment. EdgeMap-evaluated across four typical SNN applications-substantially outperforms other state-of-the-art mapping schemes. The performance improvements include a reduction in average latency by up to 19.8%, energy consumption by 57%, and communication cost by 58%. Moreover, EdgeMap exhibits an impressive enhancement in execution time by a factor of 1225.44×, alongside a throughput increase of up to 4.02×. These results highlight EdgeMap's efficiency and effectiveness, emphasizing its utility for deploying SNN applications in edge computing scenarios.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone's ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

Autonomous 6-DOF Manipulator Operation for Moving Target by a Capture and Placement Control System

The robot control technology combined with a machine vision system provides a feasible method for the autonomous operation of moving target. However, designing an effective visual servo control system is a great challenge. For the autonomous operation of the objects moving on the pipeline, this article is dedicated to developing a capture and placement control system for the six degrees of freedom (6-DOF) manipulator equipped with an eye-in-hand camera. Firstly, a path planning strategy of online capture and offline placement is proposed for real-time capture and efficient placement. Subsequently, to achieve the fast, stable, and robust capture for a moving target, a position-based visual servo (PBVS) controller is developed by combining estimated velocity feedforward and refined PID control. Feedforward control is designed using the estimated velocity by a proposed motion estimation method for high response speed. PID control is refined by dead zone constraint to reduce the manipulator’s jitter caused by the frequent adjustment of manipulator control system. Besides, the proportional, integral, and differential coefficients of PID controller are adaptively tuned by fuzzy control to reject the noise, disturbance, and dynamic variation in the capture process. Finally, validation experiments are performed on the constructed ROS–Gazebo simulation platform, demonstrating the effectiveness of the developed control system.

Sex-specific plasticity in CRF regulation of inhibitory control in central amygdala CRF1 neurons after chronic voluntary alcohol drinking

Despite strong preclinical evidence for the ability of corticotropin releasing factor 1 (CRF1) antagonists to regulate alcohol consumption, clinical trials have not yet demonstrated therapeutic effects of these compounds in alcohol use disorder (AUD) patients. Several confounding factors may limit the translation of preclinical CRF1 research to patients, including reliance on experimenter-administered alcohol instead of voluntary consumption, a preponderance of evidence collected in male subjects only and an inability to assess the effects of alcohol on specific brain circuits. A population of particular interest is the CRF1-containing neurons of the central amygdala (CeA). CRF1 CeA neurons are sensitive to ethanol, but the effects of alcohol drinking on CRF signalling within this population are unknown. In the present study, we assessed the effects of voluntary alcohol drinking on inhibitory control of CRF1+ CeA neurons from male and female CRF1:GFP mice using ex vivo electrophysiology and determined the contributions of CRF1 signalling to inhibitory control and voluntary alcohol drinking. Chronic alcohol drinking produced neuroadaptations in CRF1+ neurons that increased the sensitivity of GABA_A receptor-mediated sIPSCs to the acute effects of alcohol, CRF and the CRF1 antagonist R121919, but these adaptations were more pronounced in male versus female mice. The CRF1 antagonist CP-154,526 reduced voluntary alcohol drinking in both sexes and abolished sex differences in alcohol drinking. The lack of alcohol-induced adaptation in the female CRF1 system may be related to the elevated alcohol intake exhibited by female mice and could contribute to the ineffectiveness of CRF1 antagonists in female AUD patients.

A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.

A low power level-crossing ADC for wearable wireless ECG sensors

Ultra-low power consumption is desired in most wearable biomedical devices. The event-driven based Analog-to-Digital converter (ADC) could be an excellent candidate for low power system because of the reduction in sampling points for biosignals. In the existing event-driven based ADC architectures, two or more high precision comparators are utilized to sample the input signal. In this paper, we propose a new scheme utilizing only one high precision comparator to genertate samples with the assistance of a low precision one. From the Matlab simulations on real ECG signal, it is shown that around 25% reduction on the number of samples can be be achieved compared with the Nyquist sampling scheme.

Vector graph assisted pedestrian dead reckoning using an unconstrained smartphone

The paper presents a hybrid indoor positioning solution based on a pedestrian dead reckoning (PDR) approach using built-in sensors on a smartphone. To address the challenges of flexible and complex contexts of carrying a phone while walking, a robust step detection algorithm based on motion-awareness has been proposed. Given the fact that step length is influenced by different motion states, an adaptive step length estimation algorithm based on motion recognition is developed. Heading estimation is carried out by an attitude acquisition algorithm, which contains a two-phase filter to mitigate the distortion of magnetic anomalies. In order to estimate the heading for an unconstrained smartphone, principal component analysis (PCA) of acceleration is applied to determine the offset between the orientation of smartphone and the actual heading of a pedestrian. Moreover, a particle filter with vector graph assisted particle weighting is introduced to correct the deviation in step length and heading estimation. Extensive field tests, including four contexts of carrying a phone, have been conducted in an office building to verify the performance of the proposed algorithm. Test results show that the proposed algorithm can achieve sub-meter mean error in all contexts.

Establishment of a porcine model for lobar lung auto-transplantation

OBJECTIVE

The aim of this study was to develop a porcine model of left single lung auto-transplantation.

METHODS

Eighteen 50-kg male domestic pigs underwent left pneumonectomy and lobar lung auto-transplantation (left lower lobe). Each animal was allocated to a perfusion protocol during surgery: group I, cold saline (n = 6); group II, cold heparin (n = 6); and group III, cold Euro-Collins (n = 6). We measured changes of partial pressure of oxygen in pulmonary vein blood (PvO(2)), partial pressure of carbon dioxide in pulmonary vein blood (PvCO(2)), lung compliance, and mean pulmonary artery pressure.

RESULTS

The postoperative survival rate was 100%. PvO(2), PvCO(2), mean pulmonary artery pressure, and lung compliance of the left lower lobe showed a significant difference between the saline and the heparin groups or the Euro-Collins group (P < .05), whereas there was no significant difference between the heparin and the Euro-Collins groups.

CONCLUSION

Compared with other species, humans and pigs show remarkable anatomical and physiological similarity. It is useful experimental animal model to evaluate pulmonary function and grafting protocols following lobar lung transplantation.

Ulinastatin attenuates lung ischemia-reperfusion injury in rats by inhibiting tumor necrosis factor alpha

Ischemia-reperfusion (I/R) injury may influence graft function following transplantation. Ulinastatin, a urinary trypsin inhibitor has been shown to attenuate I/R injury in various organs such as intestine, heart, and kidney in animals. The present experiment was designed to evaluate the effect of pretreatment with ulinastatin on I/R-induced lung injury.

METHODS

After establishing a constant left lung warm ischemia-reperfusion model in rats, 45 animals were randomly divided into three experimental groups: sham group (n = 15), IR group (n = 15), and ulinastatin (5000 U/kg pre-ischemia) + IR group (n = 15). The lung injury was evaluated by tissue myeloperoxidase activity, with simultaneous estimation of the serum concentration of TNFalpha.

RESULTS

The ulinastatin-pretreated animals exhibited markedly decreased lung tissue myeloperoxidase activity (P < .05). Blood gas analysis demonstrated, that the treated animals had significantly ameliorated pulmonary oxygenation (P < .05). The serum concentration of TNF-alpha in the ulinastatin-pretreated group was markedly decreased compared with that of the I/R group (P < .05).

CONCLUSIONS

Ulinastatin attenuated I/R-induced lung injury. This function is partly related to the capacity of the agent to inhibit myeloperoxidase activity in lung tissue and decrease systemic expression to TNF-alpha.

[Surgical management of severe hemorrhage and retroperitoneal perforation after endoscopic sphincterotomy]

From 1990 to 1993, 5 patients with severe hemorrhage and retroperitoneal perforation following endoscopic sphincterotomy (EST) were surgically treated. Two patients with hemorrhage and one with perforation survived. The remaining two died of hemorrhage plus perforation. Early diagnosis and surgical intervention was very important to lower the mortality, especially in those with common bile duct stones retention, acute pancreatitis, or cholangitis. For patients with late stage perforation or recurrent hemorrhage, palliative procedure aimed at isolating the EST incision from the digestion of bile and pancreatic juice should be performed.

Hemagglutinins in fungus extracts and their blood group specificity

A total of 833 fungi harvested from 1977 to 1994 were tested and 422 extracts (47.8%) produced hemagglutination of human red cells. The lectins in fungus extracts which showed blood-group-specific or related reactions were partially purified by ammonium sulfate precipitation and gel filtration on a Sephadex G-100 column. Anti-H-like agglutinins were found in extracts of Pleurocybella porrigens, Naematoloma sublateritium and Pholiota squarrosa. These extracts agglutinated strongly with human group O red cells and rather weakly with A and B cells. Anti-A agglutinins were found in extracts of Hohenbuehelia serotina, Paxillus panuoides, Melanoleuca melaleuca and Hygrophorus capreolarius. The extract of Clavulinopsis fusiformis contained anti-B agglutinin. The ABH reactivities of the extracts were cofirmed by an agglutination inhibition test with ABH secretor saliva and blood group substances from human gastric linings and by the destruction of inhibiting activity using blood-group-specific decomposing enzymes. L-Fucose was the most active inhibiting monosacharide of anti-H-like agglutinins. The reaction of anti-A agglutinins was strongly inhibited by N-acetyl-D-galactosamine. D-Galactose and raffinose and melibiose which contain alpha-galactosyl residues were potent inhibitors of C. fusiformis agglutinin.

Computer 3-dimensional reconstruction of intraglandular lymph vessels and ductal systems of the human submandibular gland

Computer three-dimensional reconstruction of serial sections is currently an active area of research. In this paper we combine computer graphics, image processing and biomedical techniques to reconstruct a stereo model of intraglandular lymphatic vessels, veins, arteries and ducts from serial microsections of the human submandibular gland.