[{"data":1,"prerenderedAt":665},["ShallowReactive",2],{"$pUM5K5hofV":3},[4,561],{"_path":5,"_dir":6,"_draft":7,"_partial":7,"_locale":8,"title":9,"description":8,"subtitle":10,"year":11,"featured":12,"cover":13,"category":14,"tags":15,"stills":19,"captions":29,"diagrams":36,"body":43,"_type":555,"_id":556,"_source":557,"_file":558,"_stem":559,"_extension":560},"/projects/analog-resistance","projects",false,"","analoger_Widerstand","Turning surveillance into sound & oscilloscope patterns",2025,true,"/images/analog-resistance/analoger-w-2.webp","multimedia",[16,17,18],"installation","data-sonification","sound",[20,23,26],{"src":21,"alt":22},"/images/analog-resistance/2-post.webp","Still frame with morning light and handheld motion blur",{"src":24,"alt":25},"/images/analog-resistance/1-post.webp","Still frame with kitchen window flare and grain",{"src":27,"alt":28},"/images/analog-resistance/3-post.webp","Still frame highlighting lab processing streaks",[30,33],{"src":31,"alt":32},"/images/analog-resistance/caption-1.webp","Analog Resistance – CRT caption module",{"src":34,"alt":35},"/images/analog-resistance/caption-2.webp","Analog Resistance – CRT caption module 2",[37,40],{"src":38,"alt":39},"/images/analog-resistance/analoger-w-psd-004.webp","Analog Resistance – function diagram 1",{"src":41,"alt":42},"/images/analog-resistance/analoger-w-psd-006.webp","Analog Resistance – function diagram 2",{"type":44,"children":45,"toc":538},"root",[46,55,69,74,211,217,225,237,242,278,284,327,333,366,372,376,382,392,410,421,427,448,453,458,464,469,475,480,485,496,499],{"type":47,"tag":48,"props":49,"children":51},"element","h2",{"id":50},"description",[52],{"type":53,"value":54},"text","Description",{"type":47,"tag":56,"props":57,"children":58},"p",{},[59,61,67],{"type":53,"value":60},"analoger_Widerstand (2025; ",{"type":47,"tag":62,"props":63,"children":64},"em",{},[65],{"type":53,"value":66},"analog resistance",{"type":53,"value":68}," in English) is an installation that transforms live surveillance feeds into sound, voltage, and machine-generated text. Instead of showing the images, each frame is reduced to simple descriptors that drive analog oscillators, whose patterns appear on a vintage oscilloscope. A machine-learning captioning module and a visitor-tracking camera feed two CRT monitors, completing the loop.",{"type":47,"tag":56,"props":70,"children":71},{},[72],{"type":53,"value":73},"Using offline analog hardware is deliberate: its signals cannot be stored or tracked. The installation exposes surveillance while resisting its digital logic, turning monitoring into something unstable, visible, and unrecordable.",{"type":47,"tag":75,"props":76,"children":84},"div",{"className":77},[78,79,80,81,82,83],"not-prose","grid","gap-10","md:grid-cols-5","items-stretch","my-10",[85,87,88,128,129,130],{"type":53,"value":86},"\n  ",{"type":53,"value":86},{"type":47,"tag":75,"props":89,"children":94},{"className":90},[91,92,93],"md:col-span-2","flex","justify-center",[95,97,127],{"type":53,"value":96},"\n    ",{"type":47,"tag":75,"props":98,"children":109},{"className":99},[100,101,102,103,104,105,106,107,108],"relative","aspect-[9/16]","w-full","max-w-sm","rounded-xl","overflow-hidden","ring-1","ring-neutral-800","bg-neutral-900",[110,112,126],{"type":53,"value":111},"\n      ",{"type":47,"tag":113,"props":114,"children":125},"iframe",{"src":115,"className":116,"frameBorder":120,"allow":121,"referrerPolicy":122,"title":123,"loading":124,"allowFullScreen":12},"https://player.vimeo.com/video/1124460157?badge=0&autopause=0&player_id=0&app_id=58479",[117,118,102,119],"absolute","inset-0","h-full","0","autoplay; 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Rather than displaying the images, each frame is reduced to three quantitative descriptors—edge density, luminance variance, and dominant hue—which are converted into control voltages that drive analog oscillators. Their signals form shifting patterns on a vintage laboratory oscilloscope, revealing the data as pure electricity.\n    ",{"type":53,"value":96},{"type":47,"tag":56,"props":303,"children":304},{},[305],{"type":53,"value":306},"\n      A second module uses a local machine-learning model to caption what the algorithm “sees” in each unseen frame. These captions appear on a CRT monitor, exposing only the system’s interpretation—never the source image.\n    ",{"type":53,"value":96},{"type":47,"tag":56,"props":309,"children":310},{},[311],{"type":53,"value":312},"\n      A third module employs an on-site camera to track visitors. This data modulates noise and subtle sonic textures, integrating the audience into the feedback loop. 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We routinely trade privacy for convenience, often without knowing who is watching or how our data will be used. ",{"type":47,"tag":230,"props":416,"children":417},{},[418],{"type":53,"value":9},{"type":53,"value":420}," uses reality as raw material.",{"type":47,"tag":141,"props":422,"children":424},{"id":423},"concept",[425],{"type":53,"value":426},"### Concept",{"type":47,"tag":56,"props":428,"children":429},{},[430,432,446],{"type":53,"value":431},"Working with analog hardware, the piece captures live feeds from publicly accessible surveillance cameras worldwide (watchingtheworld project ",{"type":47,"tag":433,"props":434,"children":435},"sup",{},[436],{"type":47,"tag":437,"props":438,"children":443},"a",{"href":439,"ariaDescribedBy":440,"dataFootnoteRef":8,"id":442},"#user-content-fn-1",[441],"footnote-label","user-content-fnref-1",[444],{"type":53,"value":445},"1",{"type":53,"value":447},"). Each frame is reduced to measurable parameters—color, luminance, edge density—which are transformed into control voltages that trace moving patterns on a laboratory oscilloscope. In parallel, a local machine-learning model generates textual captions of what the algorithm “sees,” projected onto a CRT monitor. Visitors therefore hear the data through sound and read algorithmic interpretations, but never see the original images.",{"type":47,"tag":56,"props":449,"children":450},{},[451],{"type":53,"value":452},"A second module uses an on-site webcam to capture visitor pose. This data modulates the background noise on the monitor and alters the sonic texture, creating granular shifts that make the audience a live participant in the system. 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By remaining offline, the devices in this artwork act as rebellious outcasts—refusing assimilation into the seamless circuits of data capture. The result is an analog loop that resists storage, resists transmission, and returns to technology’s physical origins: electricity, signal, noise.",{"type":47,"tag":141,"props":470,"children":472},{"id":471},"distortion",[473],{"type":53,"value":474},"### Distortion",{"type":47,"tag":56,"props":476,"children":477},{},[478],{"type":53,"value":479},"By translating images into voltages, the work speaks in the language of the machine: electricity. What returns from that current is never the world itself but an algorithmic distortion—a partial, fragmented version of reality. The installation makes this gap tangible: visitors witness how the machine reconstructs only pieces of the world, presenting not truth but data-driven representation. 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