Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twin, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers

Coverage Optimization Strategy for WSN based on Energy-aware

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption

Treatment of hypertrophic scars and keloids using an intralesional 1470 nm bare-fibre diode laser: a novel efficient minimally-invasive technique

Hypertrophic and keloid scars result from abnormal wound healing and can have a variable response to a number of available treatment modalities. The evolution of laser treatments in recent years has shown a wide range of clinical applications including their use in the treatment of scars. We investigated the effectiveness of a 1470 nm diode laser using an intralesional optical fibre delivery device in the treatment of hypertrophic and keloid scars. We evaluated its safety and efficacy as a novel and minimally invasive treatment alternative for scar modulation and volume reduction. A prospective cohort study was performed involving 21 patients with hypertrophic scars (HS) (n = 9) and keloids (n = 12) resulting from various aetiology. Patients were treated with one to three treatment sessions. Comprehensive evaluations were performed using the Vancouver Scar Scale, Doppler ultrasound, Cutometer, Mexameter and PeriCam PSI. Scar thickness was reduced by an average of 0.308 ± 0.138 cm (p < 0.001). In particular the two subgroups showed a significant 27.7% and 28.2% reduction in scar thickness of HS and Keloids, respectively. Scar firmness showed a significant improvement of 1.2% (p < 0.05) for HS, though for keloids this was 0.4% (p = 0.26). Keloids had a significant reduction in pigmentation at 21.3%. Blood perfusion had a significant reduction of 29.6% in HS and 22.7% in Keloids. Overall VSS total score improvement of 42% in the HS and at 37.9% in the Keloid subgroup. No adverse events such as hypo/hyperpigmentation, skin infection, or recurrence were reported. This study shows that the intralesional 1470 nm bare-fibre diode laser significantly improved hypertrophic and keloid scars based on both subjective and objective analyses and supports this type of laser therapy as a safe and effective minimally-invasive treatment option

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

A Low Energy Algorithm of Wireless Sensor Networks Based on Fractal Dimension

For the energy limitation of nodes and imbalance energy consuming among nodes, this paper proposes an optimization algorithm --Low Energy Algorithm-- of wireless sensor networks based on fractal dimension algorithm for the purpose of reduction of the energy consumption. The nodes in WSN cannot be located evenly, and cannot move with the monitoring environment changed once be located. Considering the characteristics of WSN, the paper designs an optimized clustering method accompany with dimension by calculating the dimension of each cluster to determine the cluster which needs to be adjusted dynamically. If the cluster with high value of dimension, increasing more nodes in this cluster. If the cluster with low value of dimension, reducing more nodes in the cluster. The simulation results show that the LEA algorithm improves energy efficiency, prolongs the network lifetime, and balances energy consumption in the sensor network

Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

To reduce the blind zone in network coverage, we propose a coverage optimization algorithm of wireless sensor network based on mobile nodes. This algorithm calculates the irregularity of blind zone in network coverage and obtains the minimum approximate numerical solution by utilizing the quantitative relationship between energy consumption of related nodes and the position of the mobile nodes. After determining the optimal relative position of the mobile nodes, the problem of blind zone between the static nodes is addressed. Simulation result shows that the proposed algorithm has high dynamic adaptability and can address the problem of blind zone maximally. Besides increasing the network coverage, the algorithm also reduces the network energy consumption, optimizes network coverage control and exhibits high convergence

A Coverage Algorithm based on Key Node Scheduling

Network coverage rate is a key standard of measuring the quality of network coverage. This thesis aims at solving the differences between node energy and task, which causes coverage holes and blind spots of Wireless Sensor Network, and proposes a coverage algorithm, based on key node scheduling, divides node subsets, and adjusts the states of individual nodes according to node energy and coverage rate. This algorithm ensures network connectivity while reducing coverage holes and redundancy. Simulation results show that, the proposed algorithm can effectively reduce failure nodes, energy consumption, improve network coverage rate, and demonstrates network convergence and stability