"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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SLIDE 16: PERFORMANCE METRICS & BENCHMARK RESULTS Presenter: Rida - 40 seconds Graph 1: Cryptographic Operation Latency - Bar chart: Encryption vs Decryption vs Sign vs Verify - All operations: less than 2ms on Pi 5 - Y-axis: milliseconds (0-2.5) Graph 2: Key Deletion Latency - Line graph: Time vs Number of keys (1, 5, 10, 50 keys) - All deletions: less than 100ms - Demonstrates O(n) linear scaling Graph 3: Tamper Detection End-to-End Latency - Wire break → ESP32 detects → UART message → Pi receives → HSM locked - Total latency: less than 200ms - Breakdown: Detection (10 microseconds) + UART (50ms) + Processing (100ms) + API response (40ms) Graph 4: System Resource Usage During Operations - CPU usage: less than 5% idle, less than 20% during crypto - Memory: ~150MB baseline, +50MB during operation - Network: less than 1 Mbps on direct ethernet Graphics: Professional charts with labels and legends
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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SLIDE 16: PERFORMANCE METRICS & BENCHMARK RESULTS Presenter: Rida - 40 seconds Graph 1: Cryptographic Operation Latency - Bar chart: Encryption vs Decryption vs Sign vs Verify - All operations: less than 2ms on Pi 5 - Y-axis: milliseconds (0-2.5) Graph 2: Key Deletion Latency - Line graph: Time vs Number of keys (1, 5, 10, 50 keys) - All deletions: less than 100ms - Demonstrates O(n) linear scaling Graph 3: Tamper Detection End-to-End Latency - Wire break → ESP32 detects → UART message → Pi receives → HSM locked - Total latency: less than 200ms - Breakdown: Detection (10 microseconds) + UART (50ms) + Processing (100ms) + API response (40ms) Graph 4: System Resource Usage During Operations - CPU usage: less than 5% idle, less than 20% during crypto - Memory: ~150MB baseline, +50MB during operation - Network: less than 1 Mbps on direct ethernet Graphics: Professional charts with labels and legends
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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Nano Banana
SLIDE 16: PERFORMANCE METRICS & BENCHMARK RESULTS Presenter: Rida - 40 seconds Graph 1: Cryptographic Operation Latency - Bar chart: Encryption vs Decryption vs Sign vs Verify - All operations: less than 2ms on Pi 5 - Y-axis: milliseconds (0-2.5) Graph 2: Key Deletion Latency - Line graph: Time vs Number of keys (1, 5, 10, 50 keys) - All deletions: less than 100ms - Demonstrates O(n) linear scaling Graph 3: Tamper Detection End-to-End Latency - Wire break → ESP32 detects → UART message → Pi receives → HSM locked - Total latency: less than 200ms - Breakdown: Detection (10 microseconds) + UART (50ms) + Processing (100ms) + API response (40ms) Graph 4: System Resource Usage During Operations - CPU usage: less than 5% idle, less than 20% during crypto - Memory: ~150MB baseline, +50MB during operation - Network: less than 1 Mbps on direct ethernet Graphics: Professional charts with labels and legends
03
Hero
"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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FLUX
"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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ChatGPT Image
SLIDE 16: PERFORMANCE METRICS & BENCHMARK RESULTS Presenter: Rida - 40 seconds Graph 1: Cryptographic Operation Latency - Bar chart: Encryption vs Decryption vs Sign vs Verify - All operations: less than 2ms on Pi 5 - Y-axis: milliseconds (0-2.5) Graph 2: Key Deletion Latency - Line graph: Time vs Number of keys (1, 5, 10, 50 keys) - All deletions: less than 100ms - Demonstrates O(n) linear scaling Graph 3: Tamper Detection End-to-End Latency - Wire break → ESP32 detects → UART message → Pi receives → HSM locked - Total latency: less than 200ms - Breakdown: Detection (10 microseconds) + UART (50ms) + Processing (100ms) + API response (40ms) Graph 4: System Resource Usage During Operations - CPU usage: less than 5% idle, less than 20% during crypto - Memory: ~150MB baseline, +50MB during operation - Network: less than 1 Mbps on direct ethernet Graphics: Professional charts with labels and legends
03
Hero
Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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Nano Banana
SLIDE 16: PERFORMANCE METRICS & BENCHMARK RESULTS Presenter: Rida - 40 seconds Graph 1: Cryptographic Operation Latency - Bar chart: Encryption vs Decryption vs Sign vs Verify - All operations: less than 2ms on Pi 5 - Y-axis: milliseconds (0-2.5) Graph 2: Key Deletion Latency - Line graph: Time vs Number of keys (1, 5, 10, 50 keys) - All deletions: less than 100ms - Demonstrates O(n) linear scaling Graph 3: Tamper Detection End-to-End Latency - Wire break → ESP32 detects → UART message → Pi receives → HSM locked - Total latency: less than 200ms - Breakdown: Detection (10 microseconds) + UART (50ms) + Processing (100ms) + API response (40ms) Graph 4: System Resource Usage During Operations - CPU usage: less than 5% idle, less than 20% during crypto - Memory: ~150MB baseline, +50MB during operation - Network: less than 1 Mbps on direct ethernet Graphics: Professional charts with labels and legends
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Hero
"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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"subject": { "name": "Tom Cruise", "pose": "front-facing close-up", "expression": "calm, composed, intense focus, quiet menace", "symmetry": "perfectly symmetrical, face divided vertically" }, "style": { "left_half": { "visual_style": "hyper-realistic", "features": "lifelike skin textures, sharp cheekbones, piercing eyes", "lighting": "cinematic key lighting from above and side, moody shadows", "mood": "cold, authoritative, grounded in realism" }, "right_half": { "visual_style": "liquid metal, futuristic, reflective", "features": "smooth surface, chrome sheen, subtle motion effect", "inspiration": "T-1000 from Terminator 2" }, "division_line": { "style": "jagged, organic, like torn steel or cracking ice", "symbolism": "fusion of human and machine" } }, "camera": { "lens": "85mm prime", "aperture": "f/1.8", "focus": "sharp on eyes", "framing": "tight headshot with minimal headroom" }, "lighting": { "type": "dramatic, directional", "quality": "hard light on left side, metallic reflections on right", "background": "muted, minimal — soft gray gradient to isolate subject" }, "post_processing": { "skin_detail": "preserved on left half", "reflectivity": "animated glimmer or subtle motion blur on metal side", "contrast": "high contrast between organic and artificial" }, "aspect_ratio": "4:5", "moodboard_tags": [ "sci-fi portrait", "man vs machine", "Terminator T-1000", "Tom Cruise hero shot", "cinematic close-up", "hyperreal vs abstract" ]
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SLIDE 16: PERFORMANCE METRICS & BENCHMARK RESULTS Presenter: Rida - 40 seconds Graph 1: Cryptographic Operation Latency - Bar chart: Encryption vs Decryption vs Sign vs Verify - All operations: less than 2ms on Pi 5 - Y-axis: milliseconds (0-2.5) Graph 2: Key Deletion Latency - Line graph: Time vs Number of keys (1, 5, 10, 50 keys) - All deletions: less than 100ms - Demonstrates O(n) linear scaling Graph 3: Tamper Detection End-to-End Latency - Wire break → ESP32 detects → UART message → Pi receives → HSM locked - Total latency: less than 200ms - Breakdown: Detection (10 microseconds) + UART (50ms) + Processing (100ms) + API response (40ms) Graph 4: System Resource Usage During Operations - CPU usage: less than 5% idle, less than 20% during crypto - Memory: ~150MB baseline, +50MB during operation - Network: less than 1 Mbps on direct ethernet Graphics: Professional charts with labels and legends
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Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.
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